WO2017007707A1

WO2017007707A1 - Methods, apparatus and systems for predicting user traits using non-camera sensors in a mobile device

Info

Publication number: WO2017007707A1
Application number: PCT/US2016/040649
Authority: WO
Inventors: Rahul VANAM; Rajasekhar JETTY; Abhijith JAGANNATH; Khanim ABBO; Yuriy Reznik; Eduardo Asbun
Original assignee: Vid Scale, Inc.
Priority date: 2015-07-03
Filing date: 2016-07-01
Publication date: 2017-01-12

Abstract

Methods, apparatuses, and systems for determining and/or predicting user traits using non-camera sensors of a mobile device are provided. A method implemented by a mobile terminal (MT) using sensor data for determining traits of the MT's user includes receiving sensor data from sensors, determining, in a first stage of an N-stage trait determiner of the MT, an activity being conducted by the user based on a first set of sensor data included in the sensor data, outputting the activity as a first stage output of the N-stage trait determiner, determining, in a second stage of an N-stage trait determiner, physical trait information (PTI) associated with the user based on a second set of sensor data included in the sensor data, the determining of PTI being based on the determined activity being conducted by the user, and outputting the PTI as a second stage output.

Description

METHODS, APPARATUS AND SYSTEMS FOR PREDICTING USER TRAITS USING NON-CAMERA SENSORS IN A MOBILE DEVICE FIELD OF DISCLOSURE

[0001] The present invention relates to the field of wireless communications and mobile devices, and more particularly, to methods, apparatuses and systems for predicting user traits, for example, user characteristics, for example physical characteristics, and/or activities, associated with a user of a mobile device based on data obtained from non-camera sensors of the mobile device. The mobile device may perform wireless communication and/or other smartphone processes and applications.

BACKGROUND

[0002] Conventional solutions exist for predicting user activities with Android and iOS operating systems.

SUMMARY

[0003] Methods, apparatuses, and systems for determining traits of a user of a mobile device using sensor data from a plurality of sensors are provided. A representative method includes receiving a plurality of sensor data from one or more of the plurality of sensors, determining, in a first stage of an N-stage trait determiner of the mobile terminal, an activity being conducted by the user based on a first set of sensor data included in the plurality of sensor data, outputting the activity as a first stage output of the N-stage trait determiner, determining, in a second stage of an N-stage trait determiner of the mobile terminal, physical trait information associated with the user based on a second set of sensor data included in the plurality of sensor data, the determining of physical trait information being based on the determined activity being conducted by the user, and outputting the physical trait information as a second stage output of the N-stage determiner.

[0004] A representative apparatus includes a mobile terminal including an N-stage trait determiner configured to determine traits of a user of the mobile device, and the mobile terminal includes a plurality of sensors including a first set of sensors and a second set of sensors, a processor configured to: receive sensor data from the first set of sensors, determine, in a first stage of the N-stage trait determiner, first trait information of the user using the sensor data from the first set of sensors, output the first trait information as a first stage output of the N-stage trait determiner, receive sensor data from the second set of sensors, determining, in a second stage of the N- stage trait determiner, second trait information of the user using the sensor data from the second set of sensors, the determining of second trait information being based on the first stage output, and output the second trait information as a second stage output of the N-stage determiner.

[0005] A representative method implemented by a mobile terminal using sensor data from a plurality of sensors for determining traits of a user of the mobile device includes determining, in a first stage of an N-stage trait determiner of the mobile terminal, first trait information of the user using sensor data from a first set of the plurality of sensors, outputting the first trait information as a first stage output of the N-stage trait determiner, determining, in a second stage of an N-stage trait determiner of the mobile terminal, second trait information of the user using sensor data from a second set of the plurality of sensors, the determining of second trait information being based on the first stage output, and outputting the second trait information as a second stage output of the N-stage determiner.

[0006] Another representative method for determining a trait of a user of a device having multiple sensors which produce sensor data includes determining, at a first stage of an N-stage trait determiner of the device, a current activity classification for the user based on processed sensor data, and determining, at a second stage of the N-stage trait determiner of the device, a user trait based on the processed sensor data, wherein the processed sensor data used to determine the user trait corresponds to the current activity classification determined at the first stage.

BRIEF DESCRIPTION OF THE DRAWINGS

[0007] A more detailed understanding may be had from the Detailed Description below, given by way of example in conjunction with drawings appended hereto. Figures in such drawings, like the detailed description, are examples. As such, the Figures and the detailed description are not to be considered limiting, and other equally effective examples are possible and likely.

Furthermore, like reference numerals in the Figures indicate like elements, and wherein:

[0008] FIG. 1 is a system diagram illustrating an example communications system in which one or more disclosed embodiments may be implemented; [0009] FIG. 2 is a system diagram illustrating an example wireless transmit/receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1 ;

[0010] FIG. 3 is a system diagram illustrating an example radio access network and another example core network that may be used within the communications system illustrated in FIG. 1 ;

[0011] FIG. 4 is a system diagram illustrating another example radio access network and another example core network that may be used within the communications system illustrated in FIG. 1 ;

[0012] FIG. 5 is a system diagram illustrating a further example radio access network and a further example core network that may be used within the communications system illustrated in FIG. 1 ;

[0013] FIG. 6 is a diagram illustrating a system architecture of a cascaded classifier for prediction of user characteristics;

[0014] FIG. 7 is a graph showing a density plot of predicted heights for a user; and

[0015] FIG. 8 is a flowchart illustrating a method of operating an N-stage trait determiner.

DETAILED DESCRIPTION

[0016] A detailed description of illustrative embodiments may now be described with reference to the figures. However, while the present invention may be described in connection with representative embodiments, it is not limited thereto and it is to be understood that other embodiments may be used or modifications and additions may be made to the described embodiments for performing the same function of the present invention without deviating therefrom.

[0017] Although the representative embodiments are generally shown hereafter using wireless network architectures, any number of different network architectures may be used including networks with wired components and/or wireless components, for example.

[0018] FIG. 1 is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), and the like.

[0019] As shown in FIG. 1 , the communications system 100 may include wireless transmit/receive units (WTRUs) 102a, 102b, 102c, 102d, a radio access network (RAN) 103/104/105, a core network 106/107/109, a public switched telephone network (PSTN) 108, the Internet 1 10, and other networks 1 12, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and/or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and/or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a "station" and/or a "STA", may be configured to transmit and/or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, consumer electronics, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a UE.

[0020] The communications systems 100 may also include a base station 1 14a and/or a base station 1 14b. Each of the base stations 1 14a, 1 14b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the core network 106/107/109, the Internet 1 10, and/or the other networks 1 12. By way of example, the base stations 1 14a, 1 14b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a site controller, an access point (AP), a wireless router, and the like. While the base stations 1 14a, 1 14b are each depicted as a single element, it will be appreciated that the base stations 1 14a, 1 14b may include any number of interconnected base stations and/or network elements.

[0021] The base station 1 14a may be part of the RAN 103/104/105, which may also include other base stations and/or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 1 14a and/or the base station 1 14b may be configured to transmit and/or receive wireless signals within a particular geographic region, which may be referred to as a cell (not shown). The cell may further be divided into cell sectors. For example, the cell associated with the base station 1 14a may be divided into three sectors. Thus, in one embodiment, the base station 1 14a may include three transceivers, i.e., one for each sector of the cell. In another embodiment, the base station 1 14a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell.

[0022] The base stations 1 14a, 1 14b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 1 15/1 16/1 17, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 1 15/1 16/1 17 may be established using any suitable radio access technology (RAT).

[0023] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 1 14a in the RAN 103/104/105 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 1 15/1 16/1 17 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and/or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and/or High-Speed UL Packet Access (HSUPA).

[0024] In another embodiment, the base station 1 14a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 1 15/1 16/1 17 using Long Term Evolution (LTE) and/or LTE-Advanced (LTE-A).

[0025] In other embodiments, the base station 1 14a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.1 1 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0026] The base station 1 14b in FIG. 1 may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, and the like. In one embodiment, the base station 1 14b and the WTRUs 102c, 102d may implement a radio technology such as I EEE 802.1 1 to establish a wireless local area network (WLAN). In another embodiment, the base station 1 14b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 1 14b and the WTRUs 102c, 102d may utilize a cellular- based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, etc.) to establish a picocell or femtocell. As shown in FIG. 1 , the base station 1 14b may have a direct connection to the Internet 1 10. Thus, the base station 1 14b may not be required to access the Internet 1 10 via the core network 106/107/109.

[0027] The RAN 103/104/105 may be in communication with the core network 106/107/109, which may be any type of network configured to provide voice, data, applications, and/or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. For example, the core network 106/107/109 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and/or perform high-level security functions, such as user authentication. Although not shown in FIG. 1 , it will be appreciated that the RAN 103/104/105 and/or the core network 106/107/109 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 103/104/105 or a different RAT. For example, in addition to being connected to the RAN 103/104/105, which may be utilizing an E-UTRA radio technology, the core network 106/107/109 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, or WiFi radio technology.

[0028] The core network 106/107/109 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 1 10, and/or the other networks 1 12. The PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS). The Internet 1 10 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and/or the internet protocol (IP) in the TCP/IP internet protocol suite. The networks 1 12 may include wired and/or wireless communications networks owned and/or operated by other service providers. For example, the networks 1 12 may include another core network connected to one or more RANs, which may employ the same RAT as the RAN 103/104/105 or a different RAT.

[0029] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g. , the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1 may be configured to communicate with the base station 1 14a, which may employ a cellular-based radio technology, and with the base station 1 14b, which may employ an IEEE 802 radio technology.

[0030] FIG. 2 is a system diagram illustrating an example WTRU 102. As shown in FIG. 2, the WTRU 102 may include a processor 1 18, a transceiver 120, a transmit/receive element 122, a speaker/microphone 124, a keypad 126, a display/touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and/or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any subcombination of the foregoing elements while remaining consistent with an embodiment.

[0031] The processor 1 18 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 1 18 may perform signal coding, data processing, power control, input/output processing, and/or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 1 18 may be coupled to the transceiver 120, which may be coupled to the transmit/receive element 122. While FIG. 2 depicts the processor 1 18 and the transceiver 120 as separate components, it will be appreciated that the processor 1 18 and the transceiver 120 may be integrated together in an electronic package or chip.

[0032] The transmit/receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 1 14a) over the air interface 1 15/1 16/1 17. For example, in one embodiment, the transmit/receive element 122 may be an antenna configured to transmit and/or receive RF signals. In another embodiment, the transmit/receive element 122 may be an emitter/detector configured to transmit and/or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit/receive element 122 may be configured to transmit and/or receive both RF and light signals. It will be appreciated that the transmit/receive element 122 may be configured to transmit and/or receive any combination of wireless signals.

[0033] Although the transmit/receive element 122 is depicted in FIG. 2 as a single element, the WTRU 102 may include any number of transmit/receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 1 15/1 16/1 17.

[0034] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit/receive element 122 and to demodulate the signals that are received by the transmit/receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as UTRA and IEEE 802.1 1 , for example.

[0035] The processor 1 18 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light- emitting diode (OLED) display unit). The processor 1 18 may also output user data to the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128. In addition, the processor 1 18 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and/or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 1 18 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0036] The processor 1 18 may receive power from the power source 134, and may be configured to distribute and/or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0037] The processor 1 18 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 1 15/1 16/1 17 from a base station (e.g., base stations 1 14a, 1 14b) and/or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable location-determination method while remaining consistent with an embodiment.

[0038] The processor 1 18 may further be coupled to other peripherals 138, which may include one or more software and/or hardware modules that provide additional features, functionality and/or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and/or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, and the like. In a case where the peripherals 138 includes one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer; an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, and/or a humidity sensor.

[0039]The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g. for reception) may be concurrent and/or simultaneous. The full duplex radio may include an interference management unit 139 to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 1 18).

[0040] FIG. 3 is a system diagram illustrating the RAN 103 and the core network 106 according to another embodiment. As noted above, the RAN 103 may employ a UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 1 15. The RAN 103 may also be in communication with the core network 106. As shown in FIG. 3, the RAN 103 may include Node-Bs 140a, 140b, 140c, which may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 1 15. The Node-Bs 140a, 140b, 140c may each be associated with a particular cell (not shown) within the RAN 103. The RAN 103 may also include RNCs 142a, 142b. It will be appreciated that the RAN 103 may include any number of Node-Bs and RNCs while remaining consistent with an embodiment.

[0041] As shown in FIG. 3, the Node-Bs 140a, 140b may be in communication with the RNC 142a. Additionally, the Node-B 140c may be in communication with the RNC 142b. The Node-Bs 140a, 140b, 140c may communicate with the respective RNCs 142a, 142b via an lub interface. The RNCs 142a, 142b may be in communication with one another via an lur interface. Each of the RNCs 142a, 142b may be configured to control the respective Node-Bs 140a, 140b, 140c to which it is connected. In addition, each of the RNCs 142a, 142b may be configured to carry out or support other functionality, such as outer loop power control, load control, admission control, packet scheduling, handover control, macrodiversity, security functions, data encryption, and the like.

[0042] The core network 106 shown in FIG. 3 may include a media gateway (MGW) 144, a mobile switching center (MSC) 146, a serving GPRS support node (SGSN) 148, and/or a gateway GPRS support node (GGSN) 150. While each of the foregoing elements are depicted as part of the core network 106, it will be appreciated that any one of these elements may be owned and/or operated by an entity other than the core network operator.

[0043] The RNC 142a in the RAN 103 may be connected to the MSC 146 in the core network 106 via an luCS interface. The MSC 146 may be connected to the MGW 144. The MSC 146 and the MGW 144 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices.

[0044] The RNC 142a in the RAN 103 may also be connected to the SGSN 148 in the core network 106 via an luPS interface. The SGSN 148 may be connected to the GGSN 150. The SGSN 148 and the GGSN 150 may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 1 10, to facilitate communications between and the WTRUs 102a, 102b, 102c and I P-enabled devices. [0045] As noted above, the core network 106 may also be connected to the other networks 1 12, which may include other wired and/or wireless networks that are owned and/or operated by other service providers.

[0046] FIG. 4 is a system diagram illustrating the RAN 104 and the core network 107 according to an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 1 16. The RAN 104 may also be in communication with the core network 107.

[0047] The RAN 104 may include eNode-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of eNode-Bs while remaining consistent with an embodiment. The eNode-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 1 16. In one embodiment, the eNode-Bs 160a, 160b, 160c may implement Ml MO technology. Thus, the eNode-B 160a, for example, may use multiple antennas to transmit wireless signals to, and/or receive wireless signals from, the WTRU 102a.

[0048] Each of the eNode-Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in the UL and/or DL, and the like. As shown in FIG. 4, the eNode-Bs 160a, 160b, 160c may communicate with one another over an X2 interface.

[0049] The core network 107 shown in FIG. 4 may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements are depicted as part of the core network 107, it will be appreciated that any of these elements may be owned and/or operated by an entity other than the core network operator.

[0050] The MME 162 may be connected to each of the eNode-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation/deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, and the like. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and/or WCDMA.

[0051] The serving gateway 164 may be connected to each of the eNode Bs 160a, 160b, 160c in the RAN 104 via the S1 interface. The serving gateway 164 may generally route and forward user data packets to/from the WTRUs 102a, 102b, 102c. The serving gateway 164 may perform other functions, such as anchoring user planes during inter-eNode B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing contexts of the WTRUs 102a, 102b, 102c, and the like.

[0052] The serving gateway 164 may be connected to the PDN gateway 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 1 10, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0053] The core network 107 may facilitate communications with other networks. For example, the core network 107 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the core network 107 may include, or may communicate with, an IP gateway (e.g., an I P multimedia subsystem (IMS) server) that serves as an interface between the core network 107 and the PSTN 108. In addition, the core network 107 may provide the WTRUs 102a, 102b, 102c with access to the other networks 1 12, which may include other wired and/or wireless networks that are owned and/or operated by other service providers.

[0054] FIG. 5 is a system diagram illustrating the RAN 105 and the core network 109 according to an embodiment. The RAN 105 may be an access service network (ASN) that employs IEEE 802.16 radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 1 17. As will be further discussed below, the communication links between the different functional entities of the WTRUs 102a, 102b, 102c, the RAN 105, and the core network 109 may be defined as reference points.

[0055] As shown in FIG. 5, the RAN 105 may include base stations 180a, 180b, 180c, and an ASN gateway 182, though it will be appreciated that the RAN 105 may include any number of base stations and ASN gateways while remaining consistent with an embodiment. The base stations 180a, 180b, 180c may each be associated with a particular cell (not shown) in the RAN 105 and may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 1 17. In one embodiment, the base stations 180a, 180b, 180c may implement MIMO technology. The base station 180a, for example, may use multiple antennas to transmit wireless signals to, and/or receive wireless signals from, the WTRU 102a. The base stations 180a, 180b, 180c may also provide mobility management functions, such as handoff triggering, tunnel establishment, radio resource management, traffic classification, quality of service (QoS) policy enforcement, and the like. The ASN gateway 182 may serve as a traffic aggregation point and may be responsible for paging, caching of subscriber profiles, routing to the core network 109, and the like.

[0056] The air interface 1 17 between the WTRUs 102a, 102b, 102c and the RAN 105 may be defined as an R1 reference point that implements the IEEE 802.16 specification. In addition, each of the WTRUs 102a, 102b, 102c may establish a logical interface (not shown) with the core network 109. The logical interface between the WTRUs 102a, 102b, 102c and the core network 109 may be defined as an R2 reference point, which may be used for authentication, authorization, IP host configuration management, and/or mobility management.

[0057] The communication link between each of the base stations 180a, 180b, 180c may be defined as an R8 reference point that includes protocols for facilitating WTRU handovers and the transfer of data between base stations. The communication link between the base stations 180a, 180b, 180c and the ASN gateway 182 may be defined as an R6 reference point. The R6 reference point may include protocols for facilitating mobility management based on mobility events associated with each of the WTRUs 102a, 102b, 100c.

[0058] As shown in FIG. 5, the RAN 105 may be connected to the core network 109. The communication link between the RAN 105 and the core network 109 may be defined as an R3 reference point that includes protocols for facilitating data transfer and mobility management capabilities, for example. The core network 109 may include a mobile IP home agent (MIP-HA) 184, an authentication, authorization, accounting (AAA) server 186, and a gateway 188. While each of the foregoing elements are depicted as part of the core network 109, it will be appreciated that any of these elements may be owned and/or operated by an entity other than the core network operator.

[0059] The MIP-HA 184 may be responsible for IP address management, and may enable the WTRUs 102a, 102b, 102c to roam between different ASNs and/or different core networks. The MIP-HA 184 may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 1 10, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The AAA server 186 may be responsible for user authentication and for supporting user services. The gateway 188 may facilitate interworking with other networks. For example, the gateway 188 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. The gateway 188 may provide the WTRUs 102a, 102b, 102c with access to the other networks 1 12, which may include other wired and/or wireless networks that are owned and/or operated by other service providers.

[0060] Although not shown in FIG. 5, it will be appreciated that the RAN 105 may be connected to other ASNs, other RANS (e.g., RANs 103 and/or 104) and/or the core network 109 may be connected to other core networks (e.g., core network 106 and/or 107. The communication link between the RAN 105 and the other ASNs may be defined as an R4 reference point, which may include protocols for coordinating the mobility of the WTRUs 102a, 102b, 102c between the RAN 105 and the other ASNs. The communication link between the core network 109 and the other core networks may be defined as an R5 reference, which may include protocols for facilitating interworking between home core networks and visited core networks.

[0061] Although the WTRU is described in FIGS. 1 -5 as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0062] In representative embodiments, the other network 1 12 may be a WLAN.

[0063] A WLAN in Infrastructure Basic Service Set (BSS) mode may have an Access Point (AP) for the BSS and one or more stations (STAs) associated with the AP. The AP may have an access or an interface to a Distribution System (DS) or another type of wired/wireless network that carries traffic in to and/or out of the BSS. Traffic to STAs that originates from outside the BSS may arrive through the AP and may be delivered to the STAs. Traffic originating from STAs to destinations outside the BSS may be sent to the AP to be delivered to respective destinations. Traffic between STAs within the BSS may be sent through the AP, for example, where the source STA may send traffic to the AP and the AP may deliver the traffic to the destination STA. The traffic between STAs within a BSS may be considered and/or referred to as peer-to-peer traffic. The peer-to-peer traffic may be sent between (e.g., directly between) the source and destination STAs with a direct link setup (DLS). In certain representative embodiments, the DLS may use an 802.1 1 e DLS or an 802.1 1 z tunneled DLS (TDLS). A WLAN using an Independent BSS (IBSS) mode may not have an AP, and the STAs (e.g., all of the STAs) within or using the IBSS may communicate directly with each other. The I BSS mode of communication may sometimes be referred to herein as an "ad-hoc" mode of communication.

[0064] When using the 802.1 1 ac infrastructure mode of operation or a similar mode of operations, the AP may transmit a beacon on a fixed channel, such as a primary channel. The primary channel may be a fixed width (e.g., 20 MHz wide bandwidth) or a dynamically set width via signaling. The primary channel may be the operating channel of the BSS and may be used by the STAs to establish a connection with the AP. In certain representative embodiments, Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) may be implemented, for example in in 802.1 1 systems. For CSMA/CA, the STAs (e.g., every STA), including the AP, may sense the primary channel. If the primary channel is sensed/detected and/or determined to be busy by a particular STA, the particular STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0065] High Throughput (HT) STAs may use a 40 MHz wide channel for communication, for example, via a combination of the primary 20 MHz channel with an adjacent 20 MHz channel to form a 40 MHz wide contiguous channel.

[0066]Very High Throughput (VHT) STAs may support 20MHz, 40 MHz, 80 MHz, and/or 160 MHz wide channels. The 40 MHz, and/or 80 MHz, channels may be formed by combining contiguous 20 MHz channels. A 160 MHz channel may be formed by combining 8 contiguous 20 MHz channels, or by combining two non-contiguous 80 MHz channels, which may be referred to as an 80+80 configuration. For the 80+80 configuration, the data, after channel encoding, may be passed through a segment parser that may divide the data into two streams. Inverse Fast Fourier Transform (IFFT) processing, and time domain processing, may be done on each stream separately. The streams may be mapped on to the two 80 MHz channels, and the data may be transmitted by a transmitting STA. At the receiver of the receiving STA, the above described operation for the 80+80 configuration may be reversed, and the combined data may be sent to the Medium Access Control (MAC).

[0067] Sub 1 GHz modes of operation are supported by 802.1 1 af and 802.1 1 ah. The channel operating bandwidths, and carriers, are reduced in 802.1 1 af and 802.1 1 ah relative to those used in 802.1 1 η, and 802.1 1 ac. 802.1 1 af supports 5 MHz, 10 MHz and 20 MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.1 1 ah supports 1 MHz, 2 MHz, 4 MHz, 8 MHz, and 16 MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.1 1 ah may support Meter Type Control/Machine-Type Communications, such as MTC devices in a macro coverage area. MTC devices may have certain capabilities, for example, limited capabilities including support for (e.g., only support for) certain and/or limited bandwidths. The MTC devices may include a battery with a battery life above a threshold (e.g., to maintain a very long battery life).

[0068]WLAN systems, which may support multiple channels, and channel bandwidths, such as 802.1 1 η, 802.1 1 ac, 802.1 1 af, and 802.1 1 ah, include a channel which may be designated as the primary channel. The primary channel may have a bandwidth equal to the largest common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and/or limited by a STA, from among all STAs in operating in a BSS, which supports the smallest bandwidth operating mode. In the example of 802.1 1 ah, the primary channel may be 1 MHz wide for STAs (e.g. , MTC type devices) that support (e.g., only support) a 1 MHz mode, even if the AP, and other STAs in the BSS support 2 MHz, 4 MHz, 8 MHz, 16 MHz, and/or other channel bandwidth operating modes. Carrier sensing and/or Network Allocation Vector (NAV) settings may depend on the status of the primary channel. If the primary channel is busy, for example, due to a STA (which supports only a 1 MHz operating mode), transmitting to the AP, the entire available frequency bands may be considered busy even though a majority of the frequency bands remains idle and may be available.

[0069] In the United States, the available frequency bands, which may be used by 802.1 1 ah, are from 902 MHz to 928 MHz. In Korea, the available frequency bands are from 917.5 MHz to 923.5 MHz. In Japan, the available frequency bands are from 916.5 MHz to 927.5 MHz. The total bandwidth available for 802.1 1 ah is 6 MHz to 26 MHz depending on the country code.

[0070]The WTRU described in FIGS. 1 -5, or any other similar and/or suitable wireless terminal and/or wireless systems may include certain and/or representative embodiments of predicting user characteristics.

[0071] In certain embodiments, user characteristics may include one or more physical characteristics of a user, for example, a height, a weight, a body mass index (BMI), etc. However, the embodiments are not limited thereto, and user characteristics may include other user characteristics, such as activity preferences, activity levels, activity metrics, etc. In certain embodiments, a process of predicting user characteristics may be a multistep process. However, embodiments may not be limited thereto, and the process may be a one-step or a multi-step process. For example, the process may involve the following steps: (1 ) convert time-series sensor data into a feature vector using, for example, time domain features; (2) apply an Activity Prediction classifier for classifying user activity; and (3) if the output of the Activity Classifier is a particular activity (e.g., 'Walking'), pass the feature vector to a height, weight and/or body mass index (BMI) regression analysis module and/or gender classifier.

[0072] A mobile device, such as the WTRU 102 (see FIG. 2), may perform data processing (e.g., the multi-step process described above, or any other similar and/or suitable process of predicting user traits), which may include data analysis, using the processor 1 18 or one or more different processors on the mobile device. The processor 1 18 may execute and/or perform one or more algorithms for data analysis according to an embodiment. For example, a mobile device, such as the WTRU 102, may include, perform, and/or execute one or more algorithms for data analysis, for example, including a nearest neighbor algorithm, a random forests algorithm, an AdaBoost algorithm, a C4.5 algorithm which is an extension of the ID3 algorithm, and any other similar and/or suitable algorithm for data analysis for predicting user traits. A variety of commercially available and/or open source packages may be used implement these algorithms. According to an embodiment, for example, a mobile device, such as the WTRU 102, may use one or more of these packages for training algorithms to perform data analysis and, then, may use the results in one or more classifiers, regression modules, and feature generators. A mobile device, such as the WTRU 102, may use the results of the one or more of these packages to generate and/or determine further results, such as a body mass index (BMI) value, or any other similar and/or suitable result that is a user trait corresponding to a user of the mobile device. According to an embodiment, the user trait, as a result, may be provided and/or output to the user via the display/touchpad 128, the speaker/microphone 124, or any other suitable software and/or hardware element that may be used for outputting information.

[0073] As noted above, the WTRU 102 may determine the BMI and/or other physical characteristics of a user possessing the WTRU 102, e.g., a user having the WTRU 102 disposed in the user's pant pocket, in a bag held by the user and/or on the user's person. The BMI is a value derived from the mass (weight) and height of an individual, such as the user possessing the WTRU 102. The BMI may defined as the body mass divided by a square of the body height, and may be expressed in units of kg/m². However, embodiments are not limited thereto, and other definitions of BMI and/or other metrics for determining a user's body composition, with respect to body weight and/or other body composition parameters, may be determined by the WTRU 102. Commonly accepted BMI ranges are shown in Table 1 .

Table 1 : BMI Ranges

[0074] A 'classifier", which may be included and/or stored in the WTRU 102, may refer to one or more of a module, a unit, a processor, a software element, and/or a hardware element that receives an input signal, such as time-series sensor data, and generates, calculates, produces, and/or outputs a result and/or a prediction. A feature generator may be included in the classifier or may be a separate one or more of a module, a unit, a processor, a software element, and/or a hardware element that receives an input signal, such as time-series sensor data, and generates, calculates, produces, and/or outputs one or more of a vector, which may be referred to as a feature vector, based on the received input signal. The feature vector may be generated based on one or more of time domain features and/or frequency domain features discussed below, and/or any other similar and/or suitable features of the received input signal. The feature vector may be provided to and/or used by the classifier to generate, calculate, produce, and/or output a result and/or a prediction based on the feature vector. The term 'classifier¹ may also refer to the result and/or the prediction itself, which may be one of a set of pre-defined classes. For example, an activity classifier may predict user activity to be in one or more of the following classes: Resting, Standing, Walking, Stairs, Jogging, In Motion, and/or Unknown. A gender classifier may predict a gender to be any one or more of the following classes: woman, man, other, unknown, and/or any suitable gender designation. The term 'regression' may refer to a result and/or a prediction that is a real number, or in other words, may refer to a result that is not a class. Furthermore, a regression module, which may also be referred to as a regressor, may generate a result and/or a prediction, wherein the result is a real number. For example, an embodiment including a height, weight and/or BMI regression analysis module may generate one or more real numbers (e.g., one or more values) as predictions for a user's height, weight, and/or BMI. One or more of a classifier module and/or a regression module may be stored on the non-removable memory 130, or any other similar and/or suitable storage device, and may be executed by the processor 1 18 of the WTRU 102.

[0075] According to an embodiment, a mobile device, such as the WTRU 102, may use, determine, and/or calculate one or more of the following metrics in order to generate the above noted results and/or predictions, wherein the results and/or predictions may be numerical values and/or non-numerical outcomes. For example, the processor 1 18 of the WTRU 102 may determine and/or calculate the following metrics. A mean absolute error is a quantity used to measure how close forecasts or predictions are to outcomes, and may be defined as follows, where N is the number of predictions, p is the predicted value and y is the true value.

Mean Absolute Error =

- yi \

[0076]A root-mean square error (RMSE) may be used as a general-purpose error metric for numerical predictions. Compared to the similar Mean Absolute Error, RMSE amplifies and punishes (e.g., severely punishes) large errors, for example, by considering the square of the error terms. The equation for RMSE may be defined as follows:

[0077] FIG. 6 is a diagram illustrating a system architecture of a cascaded classifier for prediction of user characteristics.

[0078] Referring to FIG. 6, an embodiment of the cascaded classifier, which may be included in the WTRU 102, or any other similar and/or suitable wireless device, mobile device, and/or electronic device, may predict and/or generate user characteristics using a multistep process. The embodiment may be implemented using advanced signal processing and data analysis processes and/or techniques. It is contemplated that, certain embodiments may predict and/or generate user characteristics based on the mobile device being disposed at a known and/or predetermined location, for example, a location relative to the user during user activities, such as in the front pocket of the user's pants. In a case where classification of user characteristics is based on gait analysis, the mobile device, such as the WTRU 102, may be (1 ) disposed in a user's pocket (e.g., front pocket) (2) attached to the user; (3) on clothing of the user; (4) in a bag or other carrier on the user's person, among others. However, embodiments are not limited thereto, and the mobile device executing the methods disclosed herein may be disposed in and/or at any suitable location and the classification of user characteristics may be based on an analysis other than gait analysis. A variety of mobile device sensors (e.g., an ambient light sensor, a proximity sensor, an orientation sensor, etc.) may be used to identify when the device is disposed at or in a positions suitable for the analysis (e.g., in the user's pants or any other similar and/or suitable position for the suitable and/or corresponding analysis). Characteristics such as weight, height, gender, and/or BMI may be predicted (e.g., more accurately predicted) when a particular activity is predicted (e.g. , the user activity is determined to be 'walking'). However, embodiments are not limited thereto, and any of the characteristics may be determined for any of the user activities and/or classifications.

[0079]As shown in Figure 6, one or more input sensors may provide sensor data and/or sensor signals, such as Sensor 1 data and Sensor 2 data, to respective first and second low pass filters 601 and 602. For example, Figure 6 illustrates using two input sensors (not shown) respectively providing Sensor 1 data to the first low pass filter 601 and Sensor 2 data to the second low pass filter 602 to reduce noise in the sensor signal and to perform interpolation of time series sensor data, such as Sensor 1 data and Sensor 2 data respectively generated by the two input sensors. The time series sensor data may be converted into one or more feature vector using one or both of time domain and/or frequency domain features. Although a two input prediction process is shown with particular numbers of low pass filters, feature generators and classifiers, embodiments are not limited thereto, and any number of sensor inputs, low pass filters, feature generators may be used. The types of features generated could vary based on the sensors used. Figure 6 illustrates a bank of feature generators 603, including three different feature generators fi , and f3, wherein any one or more of the feature generators fi , and f3 may be selected based on the one or more sensors used. A bank of activity classifiers 604 may include Activity Classifier (AC) ACi , AC2, and AC3. However, embodiments are not limited thereto, and any suitable number of feature generators may be included in a bank of feature generators, any suitable number of activity classifiers may be included in a bank of ACs, and more than one feature generator and/or AC may be selected and/or used based on the one or more sensors used and/or selected.

[0080] Prior to deploying an embodiment, such as a mobile device and/or the WTRU 102, in the field, e.g., prior to an embodiment being used by a user and/or executed on the mobile device, the various algorithms may be trained with a collected database of sensor data along with labels. In other words, a training phase may be executed prior to an embodiment being used by the user and/or executed on the mobile device. During the training phase, the feature generators fi , and f3, and associated activity classifiers ACi , AC2, and AC3 may be configured according to or using a training database 606. The training database 606 may be stored in one or more of the nonremovable memory 130 and the removable memory 132 included in the WTRU 102. Data analysis methods, including tree ensembles models, such as Random Forest and Adaboost models, among others, may be used for user activity classification. As shown in FIG. 6, one or more AC, such as the activity classifiers AC1 , AC2, and AC3 included in the bank of ACs 604, may form a first stage of a cascaded classifier, which may also be referred to as an N-stage trait determiner. However, embodiments are not limited thereto, and a first stage of a cascaded classifier and/or an N-stage trait determiner may include any suitable number of activity classifiers.

[0081] Both the features generated by feature generators and the activities classified by activity classifiers, e.g., the results of the first stage of the N-stage trait determiner, may be used as inputs to a second stage of the N-stage trait determiner. The second stage of the N-stage trait determiner, which may be considered to be a cascaded classifier, may include a bank of regression modules and/or classifiers 605. Any of the classifiers and/or regression modules included in the bank of regression modules and/or classifiers 605 may be selected based on any of a user trait that is to be estimated, the activity classified in the first stage, and features that were previously generated. The user activity, and the user trait to be estimated may be used to select sensors and associated features (e.g., feature vectors) to be considered for the second stage. For example, for a climbing activity, a height of the subject may be estimated using features (e.g., feature vectors) derived from an accelerometer and/or an altimeter. During the user activity of walking, height may be estimated by using features (e.g., feature vectors) derived from an accelerometer and/or a gyroscope. The second stage may include user trait classifiers and regression modules, i.e., regressors, associated with one, some, or all activities.

[0082] For the second stage of the N-stage trait determiner, one or more of the classifiers and/or regression modules may use data analysis methods, such as Weighted Nearest Neighbor algorithms, to execute weight, height and/or body mass index data regression and for a gender classification (e.g., the Weighted Nearest Neighbor algorithms may be used by a BMI regressor and/or a gender classifier). A probabilistic distribution of predicted values may be analyzed over time in order to derive a final prediction value. In certain embodiments, sensor data generated by and/or received from an accelerometer will be discussed. However, embodiments are not limited thereto, and sensor data from other sensors such as a gyroscope, a magnetometer, an altimeter (e.g., a pressure sensor), and any other similar and/or suitable type of sensor that may be included in and/or used by a mobile device, such as the WTRU 102 may be used with the N-stage trait determiner.

Low Pass Filter and Interpolation of Sensor Data

[0083] An embodiment may use a low-pass filter such as the first and second low pass filters 601 and 602, and may send time series sensor data through the low pass filter, prior to performing analysis of the sensor data. A study of time and frequency response of related art consumer grade mobile device sensors under various conditions was conducted by the inventors and the study showed that sensor data is noisy (e.g., very noisy) and that analysis of raw sensor data may not yield accurate results with the noisy sensor data. To eliminate noise from the sensor data, the low-pass filter may be used to pass signals with a frequency lower than a certain cut-off frequency and to attenuate signals with frequencies higher than the cutoff frequency. The low-pass filter may be implemented with Kaiser Bessel window and a cut-off frequency in the range of .1 Hz to 1000 Hz and for example, about 10 Hz. However, embodiments are not limited thereto, and the low-pass filter may be implemented according to any suitable and/or similar characteristics and/or frequency range. [0084]An embodiment may capture sensor data at a very high sampling rate and/or may include sensors using a high sampling rate. For example, the embodiment may leverage and/or use advanced capabilities of mobile device operating systems (OSs) beyond what is typically used in consumer level applications. By using high sampling rates, the amount of time needed for analyzing sensor data may be reduced, and user characteristics may be determined and/or calculated faster. An embodiment, and/or a sensor included in the embodiment, for example, may sample and/or generate the sensor data at a frequency in the range of 1 Hz to 10 KHz and, for example, about 100 Hz. If the embodiment and/or the sensor generates less than a threshold number of samples per second (e.g., 100 samples per second), missing samples may be interpolated, and extra samples, e.g., those exceeding, for example, the 100 samples per second, may be dropped and/or excluded from the sensor data generated by the embodiment. However, embodiments are not limited thereto, and any suitable number of samples generated by the sensor may be used.

Feature Generation from Sensor Data

[0085]According to an embodiment, raw time series sensor data, which may also be referred to as time series sensor data, may be transformed into feature vectors such that each feature vector represents certain time series sensor data, for example, a feature vector in the range of 0.5 to 50 seconds of the time series sensor data and, for example, a feature vector of about 5 seconds of the time series sensor data. The time series sensor data may be split into window segments that contain or include the readings, e.g., the time series sensor data or samples, for a time period of a threshold number of seconds, for example about 5 seconds. The feature generation process and/or techniques may be applied to the sensor data to obtain the features vectors. For example, in one embodiment, as 100 samples may be collected every second, a 5 second time window may include 500 tuples, e.g., 500 samples, of time series sensor data.

[0086] The time window may be a sliding window that slides by a percentage of the time window (e.g., 50% or 2.5 seconds for a 5 second window) after the generation of a feature vector. Because of the slide of the sliding window, there may be a percentage (e.g., 50%) overlap of samples between current and previous windows. However, embodiments are not limited thereto, and a time window of time series sensor data may be any similar and/or suitable length of time and may include any REPLACEMENT SHEET

suitable number of samples. The raw time series sensor data may be used to generate a certain number of features (e.g. , feature vectors), for example, features as disclosed herein, which may be grouped based on time-domain and frequency-domain features according to an embodiment.

Time Domain Features

[0087] According to an embodiment, an accelerometer may generate an n-tuple set of time series sensor data. However, the embodiments are not limited thereto, and an embodiment may generate one or more n-tuple sets of sensor data using any similar and/or suitable sensor included in a mobile device, such as a gyroscope, an orientation sensor, a temperature sensor, etc. For the n-tuple set of sensor data, the following features may be computed. Time domain features are discussed below in a case where Xi, X2, X3, Xn represent input sensor data within a window (e.g., a 5 second window). For an average, X_avg, three values may be computed, which may be, for example, an average acceleration for the three axes (X, Y, Z). The average, X_avg, may be computed according to [Equation 1 ].

Xavg = Mean (Xi, X2, Xn) Equation 1

[0088] Three values may be computed for a standard deviation, X_st_d, which may be, for example, a standard deviation of the acceleration for the three axes (X, Y, Z). The standard deviation, X_st_d, may be computed according to [Equation 2].

Xstd [Equation 2]

[0089] Three values may be computed for an average absolute difference, XABSDIFF, which may be, for example, an average absolute difference between the accelerometer value and the mean accelerometer value within the sliding window of n-tuples for the three axes (X, Y, Z). The average absolute difference, XABSDIFF, may be computed according to [Equation 3].

XABSDIFF = ^{~ X}avg \ [Equation 3]

[0090] One value may be computed for a resultant, which may be, for example, an average of the square roots of the sum of the values of each axis squared. The resultant may be computed according to [Equation 4]. RESULTANT =

...[Equation 4]

Frequency Domain Features

[0091] Frequency domain features will be discussed below in a case where Xi, X2, X3, X_n represent input sensor data within a window (e.g., a 5 second window). For spectral entropy, three values may be computed, for example, a spectral entropy along the X, Y, and Z axes may be computed. In the case of a time series, e.g. a signal, of finite length, entropy may measure unpredictability of information content of the signal's spectrum, and such entropy may be referred to as spectral entropy.

[0092]According to an embodiment, a Discrete Fourier Transform (DFT), which may be expressed as X(f), of the sensor signal may be computed. Using the DFT, X(f), a Power Spectral Density (PSD) may be computed as |X(f)|². The PSD may be normalized such that it may be viewed as a Probability Density Function (PDF). A normalized PSD, which may be referred to as PSD_n may be computed according to [Equation 5].

....[Equation 5]

[0093] The spectral entro of a time series ma be com uted according to [Equation 6].

[Equation 6]

[0094] The spectral entropy may increase with the amount of the random noise. According to an embodiment, the spectral entropy defined according to [Equation 6] may provide a measure of the complexity and/or unpredictability of the signal. According to an embodiment, a mobile device implementing a Fast DFT may use a sliding window in order to optimize a speed of computing the DFT, wherein the sliding window exploits a 50% overlap between successive input window samples. For frequency peaks, values (e.g., values) may be computed, for example, atop number of frequency peaks, such as the top two frequency peaks along each of the X, Y, and Z axes, may be computed. After calculating the DFT, the top peak frequencies (e.g., 1 , 2, N peak frequencies) along each of the three axes (Χ,Υ,Ζ) may be calculated and may contribute 2N features (e.g., 6 features corresponding to two peak frequencies along each of the three axes) to the feature vector. A moving average approach for finding two non-adjacent peaks may be used according to an embodiment.

Training Algorithms using Training Dataset

[0095]According to an embodiment, a sensor database may be included in, provided to, and/or stored on the mobile terminal. For example, the sensor database may be stored in the non-removable memory 130 included in the WTRU 102. The sensor database may consist of, or include, sensor data labelled with, and/or organized so as to correspond to, associated activities. A training dataset may be included in a sensor database. Data included in the training dataset may be acquired from a large number of subjects, e.g., exceeding a threshold number of users having mobile devices including sensors. For the training dataset, according to an embodiment, data may be collected for various activities (e.g., different activities such as walking, jogging, sitting, standing, and stairs, etc.). The stairs, as an activity, may include both climbing up and climbing down the stairs.

[0096] The training data, which may be included in a training database 606, may be data that is collected using mobile device applications custom built according to an embodiment. The data collected from subjects, e.g., users, may be verified and/or consolidated into a training dataset. The training dataset, according to an embodiment, may include data generated according to a high sampling rate and which may have been examined for accuracy. The training dataset, which may include the labelled data, may be provided as input for training the algorithms such that the algorithms learn and/or determine parameters from the data. The parameters learned from the labelled data may be used by classifier algorithms to classify the unlabeled data obtained from sensors during operation of the system.

[0097]According to an embodiment, a decision tree with post-pruning, such as C4.5, may be used for activity classification because the decision tree may provide high accuracy and low complexity as compared to other similar and/or suitable algorithms. However, embodiments are not limited thereto, and other similar and/or suitable algorithms, such as Random Forests algorithms and Boosting algorithms, may be used. The C4.5 algorithm may generate a decision tree and may have several advantages, including, for example, handling continuous and/or discrete attributes, handling training data with missing attribute values, handling attributes with differing costs, and/or providing post-pruning. The C4.5 algorithm may prune trees after creation by going back through the tree once the tree has been created and/or by attempting to remove branches that do not help by replacing the branches with leaf nodes.

Activity Classification

[0098] After training the algorithm for classification, a decision tree may be generated as a result of the training. According to an embodiment, this decision tree may be used during system operation to predict the user activity by collecting sensor data and, after filtering out noise, generate the features described herein with respect to time- domain and frequency domain features. The embodiment may use the decision tree for classification according to the features described herein.

Algorithm for Height, Weight and Body Mass Index Regression Analysis and Gender Classifier

[0099]The nearest neighbor method represents one of the simplest and most intuitive techniques in the field of statistical discrimination. It is a non-parametric method, where a new observation is placed into the class of the observation from the learning set that is closest to the new observation, with respect to the covariates used. The determination of this similarity may be based on distance measures. A Euclidean distance, as a metric, may be used as a similarity measure. A first extension of the nearest neighbor method may include the k-nearest neighbor method. In the k-nearest neighbor method, both the closest observation within the learning set and the k most similar cases may be considered when performing a classification. The parameter k may be selected (e.g., by a system designer), and may be any suitable number and/or value.

[0100] A second extension of the nearest neighbor method may be implemented for which observations within the learning set that are particularly close to the new observation may get a higher weight in the decision tree than neighbors that are far away. According to an embodiment, the distances, on which the search for the nearest neighbors is based in the first step, may be transformed into similarity measures, which may be used as weights. The mapping of distances to weights may follow according to any arbitrary kernel functions, for example, distances could be mapped to weights using a Gaussian kernel function that generates lower weights for longer distances. After a determination of the similarity measures for the observations in the learning set, new cases (e.g. , each new case) may be classified into the class with the largest weight. In the case of performing a regression, the weighted average of the target values of the k nearest neighbors may be provided (e.g., given out) as the predicted result.

[0101] Even though BMI may be computed from height and weight values estimated by their respective regressors, BMI accuracy is higher when using a regression that has been trained specifically using BMI data.

User Profiling to Aggregate Predictions

[0102] In an embodiment, the mobile device may collect a set of feature vectors during a window, which may be a time window and/or a frequency window, and may apply the decision tree to the feature vectors to obtain a set of predictions of weight, height, gender and/or body mass index. A probabilistic distribution is constructed over this set of predictions. The mean of this set of predictions may be taken as the final prediction.

[0103] FIG. 7 is a graph showing a density plot of predicted heights for a user.

[0104] Referring to FIG. 7, the vertical line represents the mean of the predictions, while the curve represents the probability density function that is the probabilistic density of the predicted heights. The same process may be carried out for determining other characteristics of interest, such as weight, and/or gender, among others.

[0105] FIG. 8 is a flowchart illustrating a method of operating an N-stage trait determiner.

[0106] Referring to FIGS. 2 and 8, according to an embodiment of the present disclosure, the N-stage trait determiner may be operated and/or executed by the processor 1 18 included in the WTRU 102. For example, the N-stage trait determiner may be executed and/or activated upon the WTRU 102 detecting and/or determining that the WTRU 102 is disposed in a user's pocket or upon another similar and/or suitable condition for the WTRU 102 being satisfied. However, embodiments are not limited thereto, and the N-stage trait determiner may be executed and/or operated at any suitable time and/or upon any suitable event. The following operations may be executed while the N-stage trait determiner is active and/or being executed. At operation 801 , time series sensor data is generated. For example, the WTRU 102 may use one or more sensors included in the peripherals 138 to generate the time series sensor data. The WTRU 102 may receive and/or use sensor data from sensors that may be remote to the WTRU 102, such as sensors connected to the WTRU 102 via a wireless connection, and may receive the time series sensor data via a wireless network and/or wireless connection. The time series sensor data may include one or more sets of data, each of which may be respectively generated by a respective sensor from among the one or more sensors. At operation 802 the time series sensor data may be stored as at least one of a first set of data and a second set of data.

[0107] At operation 803, first trait information of the user may be determined using sensor data of the first set of data. The first trait information may be output as a first stage output at operation 804. At operation 805, second trait information of the user may be determined based on sensor data of the second set of data. However, embodiments are not limited thereto, and, the second trait information may be based on any suitable sensor data and/or any other suitable information. For example, the second trait information may be determined based on all or part of the first trait information and/or all or part of the first set of data. According to another example, the second trait information may be determined based on all or part of the second set of data and all or part of the first trait information and/or all or part of the first set of data. At operation 806, the second trait information may be output as a second stage output. At operation 807, the first trait information and the second trait information may be stored. For example, the WTRU 102 may store the first trait information and the second trait information on the non-removable memory 130. The operations described with respect to FIG. 8 may be referred to as a non-cascaded embodiment and/or a parallel embodiment.

[0108] Furthermore, the first trait information and the second trait information may be transmitted to another electronic device. For example, after storing the first trait information and the second trait information, a WTRU 102a may transmit such information via one or more of the wireless networks illustrated in FIGS. 1 and 3-5. Referring to FIGS. 1 and 8, for example, the WTRU 102a may transmit the first trait information and the second trait information, over the air interface 1 15/1 16/1 17, to the base station 1 14a. The base station 1 14a may further analyze and/or store the first trait information and the second trait information, and may further transmit such information to another service provider, application, electronic device, and/or network location via the core network 106/017/109, the internet 1 10, the PSTN 108, and/or other networks 1 12.

[0109] In such a case, any of the first trait information and the second trait information may be used, analyzed, and/or stored by a service provider, such as a health service provider, an advertising service provider, a network service provider, a commercial service provider, or any other similar and/or suitable service provider that receives any of the first trait information and the second trait information transmitted by the WTRU 102a. Furthermore, any of the first trait information and the second trait information may be transmitted to and/or received by an application, an electronic device, and/or a network location of a service provider to be further analyzed, stored, and/or transmitted. The first trait information and/or the second trait information may be used by the service provider to determine further information corresponding to the user of a mobile device, such as the WTRU 102a. For example, advertisements corresponding to the user, health information corresponding to the user, and/or any other similar and/or suitable information about the user of the mobile terminal that may be based on any of the first trait information and the second trait information, may be determined by a service provider and may be further transmitted to the mobile device, e.g., the WTRU 102a and/or provided to the user of mobile device, e.g., the WTRU 102a.

Experimental Results

[0110] Table 2 is the confusion matrix for activity classification using a predetermined database, obtained using a set of mobile devices executing the Android OS. In order to obtain the results shown in Table 2, cross validation with a Leave One Subject Out (LOSO) strategy was used. In implementing the LOSO strategy, if there are N subjects in the database, data corresponding to (N-1 ) subjects are used to train a classifier/regressor, which is then tested on the one subject data that was left out during training. This process is repeated N times for all possible training-test pairs, and their associated classification/regression results are aggregated.

[0111] Results from classifiers are typically summarized as a confusion matrix, which may also be referred to as an error matrix that uses a specific table layout that provides visualization of the performance of an algorithm. The horizontal axis of Table 2 represents the labels in the training database, while the vertical axis represents the output from the classifier. Thus, according to an embodiment, numbers along the main diagonal show the number of correct classifications. The overall prediction, 92.13%, indicates the accuracy of the activity classifier.

Table 1 . Confusion matrix for activity classifier using C4.5 decision tree

[0112] After carrying out the 'Leave One Subject Out' cross validation on all the subjects, the following sample results for height, weight and BMI may be achieved.

[0113] Height Regression:

[0114] Weight Regression:

[0115] BMI Regression:

[0116] Although features and elements are described above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element can be used alone or in any combination with the otherfeatures and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer readable medium for execution by a computer or processor. Examples of non-transitory computer-readable storage media include, but are not limited to, a read only memory (ROM), random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU 102, UE, terminal, base station, RNC, or any host computer.

[0117] Moreover, in the embodiments described above, processing platforms, computing systems, controllers, and other devices containing processors are noted. These devices may contain at least one Central Processing Unit ("CPU") and memory. In accordance with the practices of persons skilled in the art of computer programming, reference to acts and symbolic representations of operations or instructions may be performed by the various CPUs and memories. Such acts and operations or instructions may be referred to as being "executed," "computer executed" or "CPU executed."

[0118] One of ordinary skill in the art will appreciate that the acts and symbolically represented operations or instructions include the manipulation of electrical signals by the CPU. An electrical system represents data bits that can cause a resulting transformation or reduction of the electrical signals and the maintenance of data bits at memory locations in a memory system to thereby reconfigure or otherwise alter the CPU's operation, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to or representative of the data bits. It should be understood that the exemplary embodiments are not limited to the above-mentioned platforms or CPUs and that other platforms and CPUs may support the provided methods.

[0119]The data bits may also be maintained on a computer readable medium including magnetic disks, optical disks, and any other volatile (e.g., Random Access Memory ("RAM")) or non-volatile (e.g., Read-Only Memory ("ROM")) mass storage system readable by the CPU. The computer readable medium may include cooperating or interconnected computer readable medium, which exist exclusively on the processing system or are distributed among multiple interconnected processing systems that may be local or remote to the processing system. It is understood that the representative embodiments are not limited to the above-mentioned memories and that other platforms and memories may support the described methods. [0120] In an illustrative embodiment, any of the operations, processes, etc. described herein may be implemented as computer-readable instructions stored on a computer- readable medium. The computer-readable instructions may be executed by a processor of a mobile unit, a network element, and/or any other computing device.

[0121] There is little distinction left between hardware and software implementations of aspects of systems. The use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software may become significant) a design choice representing cost vs. efficiency tradeoffs. There may be various vehicles by which processes and/or systems and/or other technologies described herein may be effected (e.g., hardware, software, and/or firmware), and the preferred vehicle may vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle. If flexibility is paramount, the implementer may opt for a mainly software implementation. Alternatively, the implementer may opt for some combination of hardware, software, and/or firmware.

[0122] The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples may be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Suitable processors include, by way of example, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs); Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), and/or a state machine.

[0123] Although features and elements are provided above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element can be used alone or in any combination with the other features and elements. The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations may be made without departing from its spirit and scope, as will be apparent to those skilled in the art. No element, act, or instruction used in the description of the present application should be construed as critical or essential to the invention unless explicitly provided as such. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that this disclosure is not limited to particular methods or systems.

[0124] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. As used herein, when referred to herein, the terms "station" and its abbreviation "STA", "user equipment" and its abbreviation "UE" may mean (i) a wireless transmit and/or receive unit (WTRU), such as described infra; (ii) any of a number of embodiments of a WTRU, such as described infra; (iii) a wireless-capable and/or wired-capable (e.g., tetherable) device configured with, inter alia, some or all structures and functionality of a WTRU, such as described infra; (iii) a wireless-capable and/or wired-capable device configured with less than all structures and functionality of a WTRU, such as described infra; or (iv) the like. Details of an example WTRU, which may be representative of any UE recited herein, are provided below with respect to FIGS. 1 -5.

[0125] In certain representative embodiments, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), and/or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, may be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein may be distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a CD, a DVD, a digital tape, a computer memory, etc., and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).

[0126] The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely examples, and that in fact many other architectures may be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality may be achieved. Hence, any two components herein combined to achieve a particular functionality may be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermediate components. Likewise, any two components so associated may also be viewed as being "operably connected", or "operably coupled", to each other to achieve the desired functionality, and any two components capable of being so associated may also be viewed as being "operably couplable" to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

[0127] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

[0128] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, where only one item is intended, the term "single" or similar language may be used. As an aid to understanding, the following appended claims and/or the descriptions herein may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should be interpreted to mean "at least one" or "one or more"). The same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g. , "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B." Further, the terms "any of" followed by a listing of a plurality of items and/or a plurality of categories of items, as used herein, are intended to include "any of," "any combination of," "any multiple of," and/or "any combination of multiples of" the items and/or the categories of items, individually or in conjunction with other items and/or other categories of items. Moreover, as used herein, the term "set" or "group" is intended to include any number of items, including zero. Additionally, as used herein, the term "number" is intended to include any number, including zero.

[0129] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

[0130] As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein may be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as "up to," "at least," "greater than," "less than," and the like includes the number recited and refers to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1 -3 cells refers to groups having 1 , 2, or 3 cells. Similarly, a group having 1 -5 cells refers to groups having 1 , 2, 3, 4, or 5 cells, and so forth.

[0131] Moreover, the claims should not be read as limited to the provided order or elements unless stated to that effect. In addition, use of the terms "means for" in any claim is intended to invoke 35 U.S.C. §1 12, H 6 or means-plus-function claim format, and any claim without the terms "means for" is not so intended.

[0132] A processor in association with software may be used to implement a radio frequency transceiver for use in a wireless transmit receive unit (WTRU), user equipment (UE), terminal, base station, Mobility Management Entity (MME) or Evolved Packet Core (EPC), or any host computer. The WTRU may be used m conjunction with modules, implemented in hardware and/or software including a Software Defined Radio (SDR), and other components such as a camera, a video camera module, a videophone, a speakerphone, a vibration device, a speaker, a microphone, a television transceiver, a hands free headset, a keyboard, a Bluetooth® module, a frequency modulated (FM) radio unit, a Near Field Communication (NFC) Module, a liquid crystal display (LCD) display unit, an organic light-emitting diode (OLED) display unit, a digital music player, a media player, a video game player module, an Internet browser, and/or any Wireless Local Area Network (WLAN) or Ultra Wide Band (UWB) module.

[0133] Although the invention has been described in terms of communication systems, it is contemplated that the systems may be implemented in software on microprocessors/general purpose computers (not shown). In certain embodiments, one or more of the functions of the various components may be implemented in software that controls a general-purpose computer.

[0134] In addition, although the invention is illustrated and described herein with reference to specific embodiments, the invention is not intended to be limited to the details shown. Rather, various modifications may be made in the details within the scope and range of equivalents of the claims and without departing from the invention.

REPRESENTATIVE EMBODIMENT

[0135] In a first representative embodiment, a representative method includes receiving a plurality of sensor data from one or more of the plurality of sensors, determining, in a first stage of an N-stage trait determiner of the mobile terminal, an activity being conducted by the user based on a first set of sensor data included in the plurality of sensor data, outputting the activity as a first stage output of the N-stage trait determiner, determining, in a second stage of an N-stage trait determiner of the mobile terminal, physical trait information associated with the user based on a second set of sensor data included in the plurality of sensor data, the determining of physical trait information being based on the determined activity being conducted by the user, and outputting the physical trait information as a second stage output of the N-stage determiner.

[0136] In a second representative embodiment, a representative apparatus includes a mobile terminal including an N-stage trait determiner configured to determine traits of a user of the mobile device, and the mobile terminal includes a plurality of sensors including a first set of sensors and a second set of sensors, a processor configured to: receive sensor data from the first set of sensors, determine, in a first stage of the N- stage trait determiner, first trait information of the user using the sensor data from the first set of sensors, output the first trait information as a first stage output of the N- stage trait determiner, receive sensor data from the second set of sensors, determining, in a second stage of the N-stage trait determiner, second trait information of the user using the sensor data from the second set of sensors, the determining of second trait information being based on the first stage output, and output the second trait information as a second stage output of the N-stage determiner.

[0137] In a third representative embodiment, a representative method implemented by a mobile terminal using sensor data from a plurality of sensors for determining traits of a user of the mobile device includes determining, in a first stage of an N-stage trait determiner of the mobile terminal, first trait information of the user using sensor data from a first set of the plurality of sensors, outputting the first trait information as a first stage output of the N-stage trait determiner, determining, in a second stage of an N- stage trait determiner of the mobile terminal, second trait information of the user using sensor data from a second set of the plurality of sensors, the determining of second trait information being based on the first stage output, and outputting the second trait information as a second stage output of the N-stage determiner.

[0138] In a fourth representative embodiment, another representative method for determining a trait of a user of a device having multiple sensors which produce sensor data includes determining, at a first stage of an N-stage trait determiner of the device, a current activity classification for the user based on processed sensor data, and determining, at a second stage of the N-stage trait determiner of the device, a user trait based on the processed sensor data, wherein the processed sensor data used to determine the user trait corresponds to the current activity classification determined at the first stage.

[0139] In the first representative embodiment, the determining of the activity being conducted by the user includes executing a classification operation based on a first group of feature vectors which are determined based on the first set of sensor data.

[0140] In the first representative embodiment, the determining of the physical trait information of the user includes executing one or more of a classification operation and a regression operation based on a second group of feature vectors which are determined based on the second set of sensor data.

[0141] In the first representative embodiment, the activity being conducted by the user is one of: (1 ) resting (2) standing (3) walking; (4) traversing a stairs; (5) jogging; (6) in motion; or (7) doing an unknown activity. [0142] In the first representative embodiment, the physical trait information associated with the user indicates any of: (1 ) a gender of the user; (2) a height of the user (3) a weight of the user and/or (4) a body mass index (BMI) of the user.

[0143] In the first representative embodiment, the second set of sensor data includes at least part of the first set of sensor data.

[0144] In the second representative embodiment, a transceiver is configured to transmit one or more of the first trait information, the second trait information, and/or Nth trait information determined by an Nth-stage of the N-stage trait determiner.

[0145] In the second representative embodiment, a memory is configured to store one or more of the first trait information and/or the second trait information.

[0146] In the second representative embodiment, a display/touchpad is configured to display one or more of the first trait information and the second trait information.

[0147] In the second representative embodiment, the plurality of sensors includes any of: a gyroscope, an accelerometer; an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a microphone a magnetometer, a barometer, a gesture sensor, and/or a humidity sensor.

[0148] In the third representative embodiment, the determining of first trait information of the user includes executing any of: a classification operation and/or a regression operation using a first group of feature vectors generated from the sensor data from the first set of the plurality of sensors.

[0149] In the third representative embodiment, the determining of second trait information of the user includes executing any of: a classification operation and/or a regression operation on a second group of feature vectors generated from the sensor data from the second set of the plurality of sensors.

[0150] In the third representative embodiment, the method further comprises sampling data from the first set of the plurality of sensors, converting the sampled data into one or more feature vectors associated with the first trait information, and classifying or regressing the feature vectors into the first trait information.

[0151] In the third representative embodiment, the sampling of data from the first set of the plurality of sensors includes low pass filtering of the data from at least one of the first set of the plurality of sensors. [0152] In the third representative embodiment, the method further comprises choosing the sensors to be used for determining the second trait information based on the outputted first trait information.

[0153] In the third representative embodiment, the method further comprises sampling data from the second set of the plurality of sensors, converting the sampled data into one or more feature vectors associated with the second trait information, and classifying or regressing the feature vectors into the second trait information.

[0154] In the third representative embodiment, the sampling of data from the second set of the plurality of sensors includes low pass filtering of the data from at least one of the second set of the plurality of sensors.

[0155] In the third representative embodiment, the classifying or regressing of the feature vectors into the second trait information includes determining one or more of a classifier module and/or a regression module for execution in accordance with the first trait information.

[0156] In the third representative embodiment, the first and second trait information includes any of: (1 ) activity information associated with the user; and/or (2) physical trait information associated with the user.

[0157] In the third representative embodiment, the activity information indicates whether the user is one of: (1 ) resting (2) standing (3) walking; (4) traversing a stairs; (5) jogging; (6) in motion; or (7) doing an unknown activity.

[0158] In the third representative embodiment, the physical trait information associated with the user indicates any of: (1 ) a gender of the user; (2) a height of the user (3) a weight of the user and/or (4) a body mass index (BMI) of the user.

[0159] In the third representative embodiment, the method further comprises holding the mobile terminal directly or indirectly on or with the user of the mobile terminal.

[0160] In the third representative embodiment, the method further comprises determining, in a Nth stage of the N-stage trait determiner of the mobile terminal, Nth trait information of the user using sensor data from a Nth set of the plurality of sensors, the determining of Nth trait information being based on one or more of the first to N-1 stage outputs, and outputting the Nth trait information, as the Nth stage output of the N-stage determiner.

[0161] In the third representative embodiment, the choosing of the sensors used for the second trait information includes selecting the sensors from among non-camera sensors in the mobile terminal. [0162] In the third representative embodiment, the choosing of the sensors used for the second trait information includes selecting the sensors to provide: (1 ) sensor data from non-camera sensors in the mobile terminal or (2) sensor data remotely available to the mobile terminal.

[0163] In the third representative embodiment, the sensors in the mobile terminal includes any of: a gyroscope, an accelerometer; an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a microphone, a magnetometer, a barometer, a gesture sensor, and/or a humidity sensor.

[0164] In the third representative embodiment, the first trait information relates to an activity of the user and the second trait information relates to one or more of a gender and/or at least one physical characteristic of the user.

[0165] In the third representative embodiment, the first group of feature vectors are compared with a first set of stored feature vectors relating to particular user activities to determine the user activity of the user of the mobile terminal, the second group of feature vectors are compared with a second set of stored feature vectors relating to particular gender of a subject to determine the gender of the user of the mobile terminal, and the second group of feature vectors are compared with a third set of stored feature vectors relating to particular physical traits of a subject to determine the physical traits of the user of the mobile terminal.

[0166] In the third representative embodiment, the comparison for the first, second and third groups of feature vectors each includes a nearest neighbor determination with the corresponding set of stored feature vectors.

[0167] In the fourth representative embodiment, the current activity classification is one of: (1 ) resting (2) standing (3) walking; (4) traversing a stairs; (5) jogging; (6) in motion; or (7) doing an unknown activity.

[0168] In the fourth representative embodiment, the user trait is any of: (1 ) a gender of the user; (2) a height of the user (3) a weight of the user and/or (4) a body mass index (BMI) of the user.

Claims

What is Claimed is:

1 . A method implemented by a mobile terminal using sensor data from a plurality of sensors for determining traits of a user of the mobile terminal, the method comprising:

receiving a plurality of sensor data from one or more of the plurality of sensors; determining, in a first stage of an N-stage trait determiner of the mobile terminal, an activity being conducted by the user based on a first set of sensor data included in the plurality of sensor data;

outputting the activity as a first stage output of the N-stage trait determiner; determining, in a second stage of an N-stage trait determiner of the mobile terminal, physical trait information associated with the user based on a second set of sensor data included in the plurality of sensor data, the determining of physical trait information being based on the determined activity being conducted by the user; and outputting the physical trait information as a second stage output of the N- stage determiner.

2. The method of claim 1 , wherein the determining of the activity being conducted by the user includes executing a classification operation based on a first group of feature vectors which are determined based on the first set of sensor data.

3. The method of claim 1 , wherein the determining of the physical trait information of the user includes executing one or more of a classification operation and a regression operation based on a second group of feature vectors which are determined based on the second set of sensor data.

4. The method of claim 1 , wherein the activity being conducted by the user is one of: (1 ) resting (2) standing (3) walking; (4) traversing a stairs; (5) jogging; (6) in motion; or (7) doing an unknown activity.

5. The method of claim 1 , wherein the physical trait information associated with the user indicates any of: (1 ) a gender of the user; (2) a height of the user (3) a weight of the user and/or (4) a body mass index (BMI) of the user.

6. The method of claim 1 , wherein the second set of sensor data includes at least part of the first set of sensor data.

7. A mobile terminal including an N-stage trait determiner configured to determine traits of a user of the mobile device, the mobile terminal comprising:

a plurality of sensors including a first set of sensors and a second set of sensors;

a processor configured to:

receive sensor data from the first set of sensors;

determine, in a first stage of the N-stage trait determiner, first trait information of the user using the sensor data from the first set of sensors; output the first trait information as a first stage output of the N-stage trait determiner;

receive sensor data from the second set of sensors;

determining, in a second stage of the N-stage trait determiner, second trait information of the user using the sensor data from the second set of sensors, the determining of second trait information being based on the first stage output; and

outputting the second trait information as a second stage output of the N-stage determiner.

8. The mobile terminal of claim 7, further comprising a transceiver configured to transmit one or more of the first trait information, the second trait information, and/or N^th trait information determined by an N^th-stage of the N-stage trait determiner.

9. The mobile terminal of claim 7, further comprising a memory configured to store one or more of the first trait information and/or the second trait information.

10. The mobile terminal of claim 7, further comprising a display/touchpad configured to display one or more of the first trait information and the second trait information.

1 1 . The mobile terminal of claim 7, wherein the plurality of sensors includes any of: a gyroscope, an accelerometer; an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a microphone a magnetometer, a barometer, a gesture sensor, and/or a humidity sensor.

12. A method implemented by a mobile terminal using sensor data from a plurality of sensors for determining traits of a user of the mobile device, the method comprising:

determining, in a first stage of an N-stage trait determiner of the mobile terminal, first trait information of the user using sensor data from a first set of the plurality of sensors;

outputting the first trait information as a first stage output of the N-stage trait determiner;

determining, in a second stage of an N-stage trait determiner of the mobile terminal, second trait information of the user using sensor data from a second set of the plurality of sensors, the determining of second trait information being based on the first stage output; and

13. The method of claim 12, wherein the determining of first trait information of the user includes executing any of: a classification operation and/or a regression operation using a first group of feature vectors generated from the sensor data from the first set of the plurality of sensors.

14. The method of claim 12, wherein the determining of second trait information of the user includes executing any of: a classification operation and/or a regression operation on a second group of feature vectors generated from the sensor data from the second set of the plurality of sensors.

15. The method of claim 12, further comprising:

sampling data from the first set of the plurality of sensors; converting the sampled data into one or more feature vectors associated with the first trait information; and

classifying or regressing the feature vectors into the first trait information.

16. The method of claim 15, wherein the sampling of data from the first set of the plurality of sensors includes low pass filtering of the data from at least one of the first set of the plurality of sensors.

17. The method of claim 12, further comprising:

choosing the sensors to be used for determining the second trait information based on the outputted first trait information.

18. The method of claim 17, further comprising:

sampling data from the second set of the plurality of sensors;

converting the sampled data into one or more feature vectors associated with the second trait information; and

classifying or regressing the feature vectors into the second trait information.

19. The method of claim 18, wherein the sampling of data from the second set of the plurality of sensors includes low pass filtering of the data from at least one of the second set of the plurality of sensors.

20. The method of claim 18, wherein the classifying or regressing of the feature vectors into the second trait information includes determining one or more of a classifier module and/or a regression module for execution in accordance with the first trait information.

21 . The method of claim 1 , wherein the first and second trait information includes any of: (1 ) activity information associated with the user; and/or (2) physical trait information associated with the user.

22. The method of claim 22, wherein the activity information indicates whether the user is one of: (1 ) resting (2) standing (3) walking; (4) traversing a stairs; (5) jogging; (6) in motion; or (7) doing an unknown activity.

23. The method of claim 22, wherein the physical trait information associated with the user indicates any of: (1 ) a gender of the user; (2) a height of the user (3) a weight of the user and/or (4) a body mass index (BMI) of the user.

24. The method of claim 12, further comprising holding the mobile terminal directly or indirectly on or with the user of the mobile terminal.

25. The method of claim 12, further comprising:

determining, in a Nth stage of the N-stage trait determiner of the mobile terminal, Nth trait information of the user using sensor data from a Nth set of the plurality of sensors, the determining of Nth trait information being based on one or more of the first to N-1 stage outputs; and

outputting the Nth trait information, as the Nth stage output of the N-stage determiner.

26. The method of claim 17, wherein the choosing of the sensors used for the second trait information includes selecting the sensors from among non-camera sensors in the mobile terminal.

27. The method of claim 17, wherein the choosing of the sensors used for the second trait information includes selecting the sensors to provide: (1 ) sensor data from non-camera sensors in the mobile terminal or (2) sensor data remotely available to the mobile terminal.

28. The method of claim 15, wherein the sensors in the mobile terminal includes any of: a gyroscope, an accelerometer; an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a microphone, a magnetometer, a barometer, a gesture sensor, and/or a humidity sensor.

29. The method of claim 12, wherein the first trait information relates to an activity of the user and the second trait information relates to one or more of a gender and/or at least one physical characteristic of the user.

30. The method of claim 13, wherein:

the first group of feature vectors are compared with a first set of stored feature vectors relating to particular user activities to determine the user activity of the user of the mobile terminal;

the second group of feature vectors are compared with a second set of stored feature vectors relating to particular gender of a subject to determine the gender of the user of the mobile terminal; and

the second group of feature vectors are compared with a third set of stored feature vectors relating to particular physical traits of a subject to determine the physical traits of the user of the mobile terminal.

31 . The method of claim 30, wherein the comparison for the first, second and third groups of feature vectors each includes a nearest neighbor determination with the corresponding set of stored feature vectors.

32. A method for determining a trait of a user of a device, the device having multiple sensors which produce sensor data, the method comprising:

determining, at a first stage of an N-stage trait determiner of the device, a current activity classification for the user based on processed sensor data; and

determining, at a second stage of the N-stage trait determiner of the device, a user trait based on the processed sensor data,

wherein the processed sensor data used to determine the user trait corresponds to the current activity classification determined at the first stage.

33. The method of claim 31 , wherein the current activity classification is one of: (1 ) resting (2) standing (3) walking; (4) traversing a stairs; (5) jogging; (6) in motion; or (7) doing an unknown activity.

34. The method of claim 31 , wherein the user trait is any of: (1 ) a gender of the user; (2) a height of the user (3) a weight of the user and/or (4) a body mass index (BMI) of the user.