KR20220127598A - An electronic device for providing work related information based on work intensity and a method of the same - Google Patents

Abstract

An electronic device according to various embodiments disclosed in this document includes a memory, a motion sensor, an optical sensor, and a processor operatively connected to the memory, the motion sensor, and the optical sensor. The memory, when executed, may be set to allow the processor to: determine whether a user is in a physical work state or a mental work state based on a sensor signal of at least one of the motion sensor and the optical sensor for the user; calculate a physical work intensity or mental work intensity according to the determined physical work state or mental work state by using at least one of the optical sensor and the biosensor; and based on at least one of the calculated physical work intensity and the calculated mental work intensity, provide work state-related information for the user. It is possible to monitor a user's work intensity.

Description

Electronic device and method for providing work-related information based on work intensity

Various embodiments disclosed in this document relate to an electronic device that provides work-related information based on work intensity, for example, to an electronic device that calculates a user's physical and mental work intensity and provides work-related information based thereon. it's about

With the development of technology, electronic devices are becoming smaller so that they can be easily carried, and they are evolving so that they can perform various functions in various usage forms according to user needs. Various types of wearable devices that can be directly attached to a user's body part may also be one of them.

Recently, attempts have been made to obtain various information from a user's body by utilizing the characteristics of a wearable device that is directly attached to a part of the user's body and to provide various services based on the obtained information.

Various embodiments disclosed in this document are provided to provide a method for monitoring a user's work status and an electronic device thereof.

Various embodiments disclosed in this document provide a method for monitoring a user's physical work intensity and mental work intensity, and calculating a stress resistance, and an electronic device thereof.

Various embodiments disclosed in this document provide a method and an electronic device for providing work-related information according to a user's work status.

The technical problems to be achieved in the present disclosure are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. There will be.

An electronic device according to various embodiments disclosed herein includes a memory; motion sensor; optical sensor; and a processor operatively coupled with the memory, the motion sensor, and the optical sensor, wherein the memory, when executed, causes the processor to: based on a sensor signal of at least one of the motion sensor and the optical sensor to a user. to determine whether the user is in a physical work state or a mental work state, and use at least one of the optical sensor and the biometric sensor to determine the physical work intensity or mental work intensity according to the determined physical work state or mental work state and, based on at least one of the calculated physical work intensity and the calculated mental work intensity, may be set to provide work status related information to the user.

The method of the electronic device according to various embodiments of the present disclosure includes an operation of determining whether the user is in a physical work state or a mental work state based on a signal from at least one of the motion sensor and the optical sensor for the user , using at least one of the optical sensor and the biometric sensor to calculate the physical work intensity or mental work intensity according to the determined physical work state or mental work state, and the calculated physical work intensity or the calculated mental work intensity Based on at least one of the intensity, the operation may include providing work status related information to the user.

The electronic device according to various embodiments disclosed in this document may obtain a sensor value from a user, and monitor the user's work intensity by distinguishing the user's work state based on the obtained sensor value.

The electronic device according to various embodiments disclosed herein may distinguish the user's work state based on a sensor value obtained from the user, and calculate the work intensity and stress resistance of the differentiated task.

The electronic device according to various embodiments disclosed in this document calculates the work intensity and stress resistance according to the user's work state determined based on the sensor value obtained from the user's body, and based on this, calculates the work intensity and the stress resistance for the user's work adjustment You can provide work-related information, including information.

In addition, various effects directly or indirectly identified through this document may be provided.

In connection with the description of the drawings, the same or similar reference numerals may be used for the same or similar components .
1 is a block diagram of an electronic device in a network environment, according to various embodiments of the present disclosure;
2A and 2B are diagrams illustrating an external appearance of a wearable electronic device according to various embodiments of the present disclosure;
3 is a block diagram of a wearable electronic device according to various embodiments;
4 is a diagram for describing an interworking operation between a wearable electronic device and an external electronic device according to various embodiments of the present disclosure;
5 is a flowchart illustrating an operation of an electronic device according to various embodiments of the present disclosure;
6A to 6C are diagrams for explaining an operation of distinguishing between physical work and mental work according to various embodiments of the present disclosure;
7A and 7B are diagrams for explaining an operation of calculating a stress resistance degree of physical work according to various embodiments of the present disclosure;
8A and 8B are diagrams for explaining an operation of calculating a stress resistance of a mental task according to various embodiments of the present disclosure;
9 is a diagram for explaining an example of an operation of providing information related to a work state according to various embodiments of the present disclosure;
10 is a diagram for explaining another example of an operation of providing information related to a work state according to various embodiments of the present disclosure;
11 is a diagram for explaining another example of an operation of providing information related to a work state according to various embodiments of the present disclosure;
12 is a diagram for explaining another example of an operation of providing information related to a work state according to various embodiments of the present disclosure;

1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100 , the electronic device 101 communicates with the electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or a second network 199 . It may communicate with at least one of the electronic device 104 and the server 108 through (eg, a long-distance wireless communication network). According to an embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120 , a memory 130 , an input module 150 , a sound output module 155 , a display module 160 , an audio module 170 , and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or an antenna module 197 . In some embodiments, at least one of these components (eg, the connection terminal 178 ) may be omitted or one or more other components may be added to the electronic device 101 . In some embodiments, some of these components (eg, sensor module 176 , camera module 180 , or antenna module 197 ) are integrated into one component (eg, display module 160 ). can be

The processor 120, for example, executes software (eg, a program 140) to execute at least one other component (eg, a hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or operations. According to one embodiment, as at least part of data processing or operation, the processor 120 converts commands or data received from other components (eg, the sensor module 176 or the communication module 190 ) to the volatile memory 132 . may be stored in , process commands or data stored in the volatile memory 132 , and store the result data in the non-volatile memory 134 . According to an embodiment, the processor 120 is the main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit (eg, a graphic processing unit, a neural network processing unit) a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor). For example, when the electronic device 101 includes the main processor 121 and the sub-processor 123 , the sub-processor 123 uses less power than the main processor 121 or is set to be specialized for a specified function. can The auxiliary processor 123 may be implemented separately from or as a part of the main processor 121 .

The secondary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or when the main processor 121 is active (eg, executing an application). ), together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to an embodiment, the coprocessor 123 (eg, an image signal processor or a communication processor) may be implemented as part of another functionally related component (eg, the camera module 180 or the communication module 190 ). have. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. Artificial intelligence models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example not limited The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the above example. The artificial intelligence model may include, in addition to, or alternatively, a software structure in addition to the hardware structure.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176 ) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, the program 140 ) and instructions related thereto. The memory 130 may include a volatile memory 132 or a non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 , and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120 ) of the electronic device 101 from the outside (eg, a user) of the electronic device 101 . The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output a sound signal to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from or as part of the speaker.

The display module 160 may visually provide information to the outside (eg, a user) of the electronic device 101 . The display module 160 may include, for example, a control circuit for controlling a display, a hologram device, or a projector and a corresponding device. According to an embodiment, the display module 160 may include a touch sensor configured to sense a touch or a pressure sensor configured to measure the intensity of a force generated by the touch.

The audio module 170 may convert a sound into an electric signal or, conversely, convert an electric signal into a sound. According to an embodiment, the audio module 170 acquires a sound through the input module 150 , or an external electronic device (eg, a sound output module 155 ) connected directly or wirelessly with the electronic device 101 . The electronic device 102) (eg, a speaker or headphones) may output a sound.

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the sensed state. can do. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, an optical sensor, It may include a temperature sensor, a humidity sensor, or an illuminance sensor.

The interface 177 may support one or more specified protocols that may be used by the electronic device 101 to directly or wirelessly connect with an external electronic device (eg, the electronic device 102 ). According to an embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102 ). According to an embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert an electrical signal into a mechanical stimulus (eg, vibration or movement) or an electrical stimulus that the user can perceive through tactile or kinesthetic sense. According to an embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to an embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to an embodiment, the power management module 188 may be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). It can support establishment and communication performance through the established communication channel. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : It may include a local area network (LAN) communication module, or a power line communication module). A corresponding communication module among these communication modules is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (eg, a telecommunication network such as a LAN or a WAN). These various types of communication modules may be integrated into one component (eg, a single chip) or may be implemented as a plurality of components (eg, multiple chips) separate from each other. The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199 . The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, a new radio access technology (NR). NR access technology includes high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency) -latency communications)). The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various techniques for securing performance in a high-frequency band, for example, beamforming, massive multiple-input and multiple-output (MIMO), all-dimensional multiplexing. It may support technologies such as full dimensional MIMO (FD-MIMO), an array antenna, analog beam-forming, or a large scale antenna. The wireless communication module 192 may support various requirements defined in the electronic device 101 , an external electronic device (eg, the electronic device 104 ), or a network system (eg, the second network 199 ). According to an embodiment, the wireless communication module 192 may include a peak data rate (eg, 20 Gbps or more) for realizing eMBB, loss coverage (eg, 164 dB or less) for realizing mMTC, or U-plane latency for realizing URLLC ( Example: Downlink (DL) and uplink (UL) each 0.5 ms or less, or round trip 1 ms or less) can be supported.

The antenna module 197 may transmit or receive a signal or power to the outside (eg, an external electronic device). According to an embodiment, the antenna module 197 may include an antenna including a conductor formed on a substrate (eg, a PCB) or a radiator formed of a conductive pattern. According to an embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is connected from the plurality of antennas by, for example, the communication module 190 . can be selected. A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) other than the radiator may be additionally formed as a part of the antenna module 197 .

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module comprises a printed circuit board, an RFIC disposed on or adjacent to a first side (eg, bottom side) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, an array antenna) disposed on or adjacent to a second side (eg, top or side) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and a signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, the command or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or a part of operations executed in the electronic device 101 may be executed in one or more external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a function or service automatically or in response to a request from a user or other device, the electronic device 101 may perform the function or service itself instead of executing the function or service itself. Alternatively or additionally, one or more external electronic devices may be requested to perform at least a part of the function or the service. One or more external electronic devices that have received the request may execute at least a part of the requested function or service, or an additional function or service related to the request, and transmit a result of the execution to the electronic device 101 . The electronic device 101 may process the result as it is or additionally and provide it as at least a part of a response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an Internet of things (IoT) device. The server 108 may be an intelligent server using machine learning and/or neural networks. According to an embodiment, the external electronic device 104 or the server 108 may be included in the second network 199 . The electronic device 101 may be applied to an intelligent service (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology. .

The electronic device according to various embodiments disclosed in this document may have various types of devices. The electronic device may include, for example, a portable communication device (eg, a smart phone), a computer device, a portable multimedia device, a portable medical device, a camera, a wearable device, or a home appliance device. The electronic device according to the embodiment of the present document is not limited to the above-described devices.

The various embodiments of this document and terms used therein are not intended to limit the technical features described in this document to specific embodiments, but it should be understood to include various modifications, equivalents, or substitutions of the embodiments. In connection with the description of the drawings, like reference numerals may be used for similar or related components. The singular form of the noun corresponding to the item may include one or more of the item, unless the relevant context clearly dictates otherwise. As used herein, "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "A , B, or C," each of which may include any one of the items listed together in the corresponding one of the phrases, or all possible combinations thereof. Terms such as “first”, “second”, or “first” or “second” may simply be used to distinguish the component from other such components, and refer to those components in other aspects (e.g., importance or order) is not limited. It is said that one (eg, first) component is “coupled” or “connected” to another (eg, second) component, with or without the terms “functionally” or “communicatively”. When referenced, it means that one component can be connected to the other component directly (eg by wire), wirelessly, or through a third component.

The term “module” used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as, for example, logic, logic block, component, or circuit. can be used as A module may be an integrally formed part or a minimum unit or a part of the part that performs one or more functions. For example, according to an embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).

Various embodiments of the present document include one or more instructions stored in a storage medium (eg, internal memory 136 or external memory 138) readable by a machine (eg, electronic device 101). may be implemented as software (eg, the program 140) including For example, the processor (eg, the processor 120 ) of the device (eg, the electronic device 101 ) may call at least one of the one or more instructions stored from the storage medium and execute it. This makes it possible for the device to be operated to perform at least one function according to the called at least one command. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' only means that the storage medium is a tangible device and does not contain a signal (eg, electromagnetic wave), and this term is used in cases where data is semi-permanently stored in the storage medium and It does not distinguish between temporary storage cases.

According to one embodiment, the method according to various embodiments disclosed in this document may be provided in a computer program product (computer program product). Computer program products may be traded between sellers and buyers as commodities. The computer program product is distributed in the form of a machine-readable storage medium (eg compact disc read only memory (CD-ROM)), or through an application store (eg Play Store™) or on two user devices ( It can be distributed (eg downloaded or uploaded) directly, online between smartphones (eg: smartphones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created in a machine-readable storage medium such as a memory of a server of a manufacturer, a server of an application store, or a relay server.

According to various embodiments, each component (eg, a module or a program) of the above-described components may include a singular or a plurality of entities, and some of the plurality of entities may be separately disposed in other components. have. According to various embodiments, one or more components or operations among the above-described corresponding components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (eg, a module or a program) may be integrated into one component. In this case, the integrated component may perform one or more functions of each component of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. . According to various embodiments, operations performed by a module, program, or other component are executed sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations are executed in a different order, or omitted. , or one or more other operations may be added.

2A and 2B are diagrams illustrating an external appearance of an electronic device (eg, the electronic device 101 of FIG. 1 ) according to various embodiments of the present disclosure. Fig. 2A is a perspective view and Fig. 2B is a rear view.

2A and 2B , in various embodiments, an electronic device 101 (eg, a wearable device) (hereinafter, referred to as a wearable device) may include an electronic device worn on a user's wrist. However, the present invention is not limited thereto, and the wearable device 101 includes various types of portable electronic devices equipped with an optical sensor (eg, a PPG sensor) and capable of acquiring the user's biometric information while at least partially in contact with the human body. can do. For example, the wearable device 101 is a body-attached device (eg, a body-attached device capable of extracting biometric information of a user through a skin region in which blood vessels are located while maintaining at least partially contact with the human body when worn). It may be implemented as various wearable devices such as health patches, digital tattoos), clothing-type devices (eg, smart clothing, gloves), or band-type devices (eg, wrist/arm/finger bands, smart rings).

According to various embodiments of the present disclosure, in the wearable device 101, an optical sensor 210 (eg, a photoplethysmography (PPG) sensor) for extracting user's biometric data may be mounted therein. For example, the region in which the optical sensor 210 is disposed may maintain a state in which it is at least partially in close contact with the human body, and the sensing region of the optical sensor 210 may be formed to correspond to the skin where the user's blood vessels are located.

2A and 2B , the wearable device 101 includes a first surface (or front surface) 211a, a second surface (or rear surface) 211b, and a first surface 211a and a second surface ( 211b) and a housing 211 including a side surface 211c surrounding the space between them, and connected to at least a portion of the housing 211, to be detachably attached to a part of the user's body (eg, wrist or ankle). A configured binding member 212 (eg, a strap) may be included.

According to various embodiments, at least a portion of the first surface 211a may be formed by a substantially transparent front plate (eg, a glass plate including various coating layers, or a polymer plate).

According to various embodiments, the second surface 211b may be formed by a substantially opaque back plate. The back plate may be formed, for example, by coated or tinted glass, ceramic, polymer, metal (eg, aluminum, stainless steel (STS), or magnesium), or a combination of at least two of the above materials. . The second surface (eg, the rear surface) 211b of the wearable device 101 may be a surface in direct contact with the human body.

According to various embodiments, the side surface 211c is partially coupled to a front plate (eg, the first side 211a) and a back plate (eg, the second side 211b), and includes a metal and/or a polymer. It may be formed by a side bezel structure (or “side member”).

According to various embodiments, the back plate and the side bezel structure may be integrally formed and may include the same material (eg, a metal material such as aluminum). The binding member 212 may be formed of various materials and shapes. For example, a woven fabric, leather, rubber, urethane, metal, ceramic, or a combination of at least two of the above materials may be used to form an integral and a plurality of unit links to be able to flow with each other.

According to various embodiments, the wearable device 101 may include an optical sensor 210 for acquiring biometric information from the user's body. For example, the optical sensor 210 may be at least partially disposed on the second surface 211b of the wearable device 101 to at least partially contact the human body. For example, the wearable device 101 may utilize a partial area corresponding to the position where the optical sensor 210 is disposed as the sensing area. For example, the sensing region of the optical sensor 210 may include a region in direct contact with the human body when the wearable device 101 is worn, and may be formed to correspond to the skin where the user's blood vessels are located. According to various embodiments, the optical sensor 210 may be partially disposed on the binding member 212 instead of the housing 211 of the wearable device 101 . For example, the arrangement position of the optical sensor 210 may not be limited to the housing 211 .

According to various embodiments, the optical sensor 210 may include a PPG sensor (eg, a pulse wave sensor). For example, the PPG sensor may obtain a sensor value (eg, a raw sensor signal) by emitting light to a skin surface on which blood vessels are located, and receiving reflected light of the emitted light. For example, the wearable device 101 may include a user's heart rate information, heart rate variation information, stress information, and/or sleep information based on the sensor value. Various biometric information can be checked.

According to various embodiments, the wearable device 101 may measure a user's heartbeat and/or heartbeat variability based on the sensor value. For example, the optical sensor 210 may extract a feature value corresponding to the user's biometric data based on a pulse collected from the sensor value. For example, the wearable device 101 may compare the extracted feature value with the preset reference biometric data using the optical sensor 210 , and based on the difference value according to the comparison result, the user's heart rate and/or heart rate variability can be obtained.

3 is a block diagram of an electronic device (eg, the electronic device 101 of FIG. 1 ) according to various embodiments of the present disclosure.

Referring to FIG. 3 , an electronic device 101 (eg, a wearable device) (hereinafter, referred to as a wearable device) includes a processor (eg, the processor 120 of FIG. 1 ) and a memory (eg, the memory 130 of FIG. 1 ). ), a display module (eg, the display module 160 of FIG. 1 ), a wireless communication circuit (eg, the communication module 190 of FIG. 1 ), an optical sensor 310 (eg, the sensor module 176 of FIG. 1 ) or optical sensor 210 of FIG. 2B ) and/or motion sensor 320 (eg, sensor module 176 of FIG. 1 ). The components shown in FIG. 3 are merely examples, and some of them may be omitted or substituted or may be integrated as one module according to various embodiments. Among the components shown in FIG. 3 , a detailed description thereof may be omitted here for a description overlapping with the description of the components described with reference to FIG. 1 .

According to various embodiments, the wearable device 101 may measure the heartbeat of the user using the optical sensor 310 . For example, the optical sensor 310 may include a photoplethysmography (PPG) sensor for acquiring the user's biometric data.

According to various embodiments, the optical sensor 310 includes a light source 311 emitting light and a light detector (photo detector, PD) 312 receiving the reflected light of the emitted light. can do.

According to various embodiments, the light emitting unit 311 and the light receiving unit 312 of the optical sensor 310 may be disposed to correspond to a sensing region for measuring blood pressure, and may be at least partially in contact with the skin where the user's blood vessels are located. state can be driven. For example, the light emitting unit 311 may emit light toward the skin where the user's blood vessels are located, and the emitted light is partially absorbed by the user's skin or blood vessels and the remaining part is reflected or scattered through the user's body. reflected light may be generated.

In general, the amount of blood flow flowing through blood vessels may change with time. For example, light emitted toward the skin where the user's blood vessels are located may be partially absorbed by the user's skin or blood vessels. For example, the amount of light absorbed by blood vessels may fluctuate according to fluctuations in blood flow. For example, blood vessels can absorb a lot of light when the blood flow is high, and can absorb less light when the blood flow is low.

According to various embodiments, the processor 120 may receive the reflected light through the light receiving unit 312 of the optical sensor 310 and extract sensor data including a change in blood flow based on a sensor signal output. For example, the optical sensor 310 may receive reflected light through the light receiving unit 312 , and receive a sensor value corresponding to the received reflected light (eg, light scattered or reflected from a region of the skin where blood vessels are located). can be printed out. For example, the processor 120 may extract the user's biometric data (eg, cardiac output (CO), total peripheral resistance (TPR), and biometric parameters) based on the sensor value. For example, the processor 120 may analyze the sensor value to estimate a change in intensity of the reflected light over time.

According to various embodiments, the processor 120 analyzes the sensor value to estimate the fluctuation of reflected light corresponding to the volume change of the user's blood vessel (eg, a blood vessel located on a finger or wrist, a radial artery below the wrist). , the user's biometric data can be extracted. For example, the processor 120 may extract the user's biometric data based on the correlation between the change amount of the reflected light reflected in the sensor value and the volume change.

According to various embodiments, the light emitting unit 311 and/or the light receiving unit 312 may be configured in plurality. For example, the light emitting unit 311 may include a plurality of light emitters capable of emitting light (eg, red, green, blue, and/or IR) of the same or different wavelengths, respectively. For example, the light emitting unit 311 may include a light emitting diode (LED), an organic light emitting diode (OLED), a semiconductor laser (LD, laser diode), a solid laser, or an IR. It may include at least one of (infrared) diodes.

For example, the light emitting unit 311 may include blue light having a wavelength of about 400 nm to about 550 nm, green light having a wavelength of about 450 nm to about 650 nm, red light having a wavelength of about 550 nm to about 700 nm, and/or Alternatively, infrared (IR) light having a wavelength of about 880 nm to about 940 nm may be output, respectively. For example, the light receiving unit 312 may include a plurality of light receiving elements (eg, photo diodes (PD)). For example, the light receiving unit 312 may include at least one of an avalanche photo diode (APD), a photo transistor, and an image sensor in addition to a photo diode (PD).

According to various embodiments, the motion sensor 320 may include various types of sensors capable of detecting the motion of the wearable device 101 , such as a gyro sensor, an acceleration sensor, and/or a GPS. For example, the motion sensor 320 may be electrically connected to the processor 120 to provide a sensor value (eg, a motion signal) generated according to motion detection of the wearable device 101 to the processor 120 .

According to an embodiment, the processor 120 may calculate a movement direction, a moved distance, the number of steps, and/or a speed of the user wearing the wearable device 101 based on a sensor signal of the motion sensor 320 . .

According to various embodiments, the processor 120 may check the user's work status based on a sensor signal of the optical sensor 310 and/or the motion sensor 320 . For example, the user's work state may include information on whether the user is in a physical work state or a mental work state. For example, the physical work state may include a state in which the user is performing a physical work or is resting after the physical work. For example, the mental task state may include a state in which the user is performing a mental task or is resting after a mental task.

According to an embodiment, the processor 120 may check the user's work status based on motion information derived from sensor signals of the optical sensor 310 and the motion sensor 320 and heart rate related information. According to an embodiment, the processor 120 may be configured to perform a user's work status based on a change pattern of motion information derived from sensor signals of the optical sensor 310 and the motion sensor 320 and heart rate-related information and/or a correlation thereof. can be checked. For example, when motion information and heart rate-related information are less than a threshold value without significant change for a specified period, it may be determined as a resting period or a stable period. For example, if the motion information is below the threshold value without significant change for a specified period and the heart rate-related information rises above the threshold value, it can be identified as a mental task state. For example, if motion information and heart rate-related information rise or fall above a threshold value during a specified period, respectively, and the pattern of change is similar, it can be confirmed as a physical work state.

According to an embodiment, the processor 120 determines the user's information based on the change pattern and/or correlation between the motion information derived from the sensor values of the optical sensor 310 and the motion sensor 320 and the heartbeat-related information, and the user information. You can check the work status. For example, a threshold for determining a change pattern of each of the motion information and the heartbeat-related information may be set and/or adjusted based on user information. For example, the user information may include body information such as age, gender, height, and weight of the user.

According to an embodiment, the processor 120 is configured to change a pattern of motion information derived from sensor values of the optical sensor 310 and the motion sensor 320 and heart rate-related information and/or their correlation, and the confirmed before and after work status. based on the user's work status can be checked. For example, according to the motion information and heart rate related information, even if the motion information is less than the threshold and the heart rate related information is above the threshold, the point at which the current motion information and heart rate related information is measured follows the previous physical task state. In the case of the period, it may be determined as a recovery period after physical work rather than a mental work state, and it may be determined as a physical work state until the heart rate-related information decreases below a threshold value.

According to various embodiments, the processor 120 measures a signal corresponding to the movement of the wearable device 101 for a set time using an acceleration sensor and/or a gyro sensor included in the motion sensor 320, and Based on the corresponding signal, the distance traveled, the number of steps, the speed and/or the average speed of the user during the specified time may be calculated. For example, when the magnitude of the average speed calculated based on the signal output from the motion sensor 320 is less than a set threshold, the processor 120 may determine the state of rest or mental work.

According to various embodiments, the processor 120 may obtain a user's heart rate (HR) and/or heart rate variation (HRV) based on a signal measured through the optical sensor 310 . . For example, the processor 120 may compare the heart rate calculated based on the signal output from the optical sensor 310 with the user's resting heart rate to determine whether the current user is in a resting period or in a work state. For example, the processor 120 may control the heart rate obtained based on the sensor value of the optical sensor 310 during the user's resting period or rest period determined based on the sensor value of the motion sensor 320 and/or the optical sensor 310 . The average value can be calculated and set as the user's resting heart rate.

According to various embodiments, the processor 120 may be configured to perform a step frequency, a moving speed, and/or a user's step frequency for a specified time calculated based on a sensor signal of the motion sensor 320 in a physical work state. The intensity of physical work may be calculated based on at least one of an average speed and/or a heart rate calculated based on a sensor signal of the optical sensor 310 , an average heart rate, and/or a heart rate variability.

According to an embodiment, the processor 120 may include, in a physical work state, the number of steps (step frequency), movement speed (speed), and optical sensor for a user's specified time calculated based on a sensor signal of the motion sensor 320 . Physical work intensity may be calculated based on the heart rate calculated based on the sensor signal of 310 and/or user information. For example, in the physical work state, the processor 120 calculates the number of steps for 30 seconds of the user calculated based on the sensor signal of the motion sensor 320, the speed of movement for 30 seconds, the optical The intensity of physical work may be calculated based on a heart rate calculated based on a sensor signal of the sensor 310 and/or user information. For example, physical activity intensity can be indexed based on oxygen consumption (eg VO2 value) or maximum oxygen consumption (eg % of VO2 max value). For example, oxygen consumption or maximum oxygen consumption may be generally measured through a gas analyzer, and the wearable device 101 calculates the number of steps for a specified time of the user based on a sensor signal of the motion sensor 320 (step frequency). ), moving speed, a heart rate calculated based on a sensor signal of the optical sensor 310 and/or a value close to the maximum oxygen consumption measured through the above-described gas analyzer based on user information Oxygen consumption or maximum oxygen consumption can be calculated using an algorithm or machine learning model designed to calculate physical work intensity.

According to an embodiment, the processor 120 is configured to perform a step frequency, a moving speed, and/or a user's step frequency for a specified time calculated based on a sensor signal of the motion sensor 320 in a physical work state. Physical task intensity, for example, 0 to It can be calculated as an index value between 100.

According to various embodiments, the processor 120 may calculate the physical stress resistance based on a change in the intensity of physical work over time. For example, if the intensity of physical work decreases and the recovery time is short after performing physical work, the resistance to physical stress may be high. For example, if the intensity of physical work decreases after performing physical work and it takes a long time to recover, the resistance to physical stress may be low.

According to various embodiments, in the mental task state, the processor 120 may calculate the mental task intensity based on at least one of a heart rate calculated based on a sensor signal of the optical sensor 310, a resting heart rate, and/or a heart rate variability. can For example, mental work intensity can be indexed based on a stress index. For example, the stress index may be proportional to the heart rate, may be inversely proportional to the resting heart rate, and may be calculated as a value proportional to the heart rate variability.

According to an embodiment, in the mental task state, the processor 120 uses at least one of a heart rate calculated based on a sensor signal of the optical sensor 310, a resting heart rate, and/or a heart rate variability to generate a specified machine learning model. Through this, for example, the mental work intensity can be calculated as an index value between 0 and 100.

According to an embodiment, the mental task intensity may be calculated based on at least one of a heart rate calculated based on a sensor signal of the optical sensor 310 , a resting heart rate, and/or a heart rate variability. For example, mental work intensity can be indexed based on a stress index. For example, the stress index may be proportional to the heart rate, may be inversely proportional to the resting heart rate, and may be calculated as a value proportional to the heart rate variability.

According to various embodiments, the processor 120 may calculate the mental stress resistance based on a change in mental task intensity with time. For example, if the recovery time for mental task intensity after mental task performance is short, mental stress resistance may be high. For example, a short period of time during which mental task intensity increases at the onset of mental task may result in higher mental stress resistance. For example, if mental task intensity takes longer to recover after mental task performance, mental stress resistance may be low. For example, if mental task intensity takes a long time to increase at the onset of mental task, mental stress resistance may be low.

According to various embodiments, the processor 120 executes a program (eg, the program 140 of FIG. 1 ) stored in the memory 130 to control at least one other component (eg, a hardware or software component). and can perform various data processing or operations. According to various embodiments, the processor 120 may extract biometric data related to the user by driving the optical sensor 310, and using a heart rate and/or heart rate variability calculation algorithm stored in the memory 130, Heart rate and/or heart rate variability information may be obtained from the extracted biometric data.

According to various embodiments, the processor 120 may apply a heart rate and/or heart rate variability learning model previously stored in the memory 130 to the extracted biometric data to calculate more accurate heart rate and/or heart rate variability information. . For example, the memory 130 may store a learning model built through accumulated learning of the user's heart rate and/or heart rate variability obtained by using the optical sensor 310 . For example, a learning model for heart rate and/or heart rate variability may be based on a user's heart rate and/or heart rate for a learning model built using user information, for example, body information such as age, gender, height and/or weight. It can be updated through cumulative learning using variability.

According to various embodiments, the wearable device 101 drives the optical sensor 310 and/or the motion sensor 320 to check the user's work status, and based on this, the optical sensor 310 and/or the motion sensor ( 320) to obtain a sensor signal, and may acquire physical work intensity or mental work intensity based on the acquired sensor signal.

According to various embodiments, the memory 130 may include a step frequency, a moving speed and/or an average speed, and/or a user's step frequency for a specified time calculated based on a sensor signal of the motion sensor 320 , and/or Alternatively, a machine learning model for calculating the physical work intensity based on at least one of a heart rate calculated based on a sensor signal of the optical sensor 310 , an average heart rate, and/or a heart rate variability may be stored.

According to various embodiments, the memory 130 uses at least one of a heart rate calculated based on a sensor signal of the optical sensor 310 , a resting heart rate, and/or a heart rate variability, for machine learning for calculating the mental task intensity. You can save the model.

According to various embodiments, the processor 120 determines the user's work status by using the optical sensor 310 and/or the motion sensor 320 , and based on this, the optical sensor 310 and/or the motion sensor 320 . ) at a specified period (eg, every 5 minutes) for a specified time (eg, 30 seconds) to output a sensor signal.

According to various embodiments, the display module 160 may provide various visual information related to the physical work intensity and/or the mental work intensity according to the work state acquired by the processor 120 . For example, the display module 160 may display various visual information generated based on physical work intensity and/or mental work intensity, or a visual notification necessary in relation to guide information for work intensity adjustment.

According to various embodiments, the wearable device 101 wirelessly communicates with an external electronic device (eg, the electronic devices 102 and 104 of FIG. 1 ) through a wireless communication circuit (eg, the communication module 190 of FIG. 1 ). can be performed. For example, the wearable device 101 may perform wireless communication with a portable electronic device (eg, a smartphone), and may provide commands and/or data (eg, the user's work status, mental work intensity or physical work intensity; mental stress resistance, physical stress resistance, user information, and/or guide information) may be exchanged. According to various embodiments, the wearable device 101 may be at least partially controlled by an external electronic device. For example, the wearable device 101 may perform at least one function under the control of an external electronic device. For example, the wearable device 101 may jointly perform at least one function by interworking with an external electronic device.

4 illustrates an interworking operation between the electronic device 401 (eg, the electronic device 101 of FIG. 1 ) and the external electronic device 402 (eg, the electronic device 102 or 104 of FIG. 1 ) according to various embodiments of the present disclosure; It is a drawing for explanation.

Referring to FIG. 4 , a first electronic device 401 (eg, the electronic device 101 of FIG. 1 , or the wearable device 101 of FIGS. 2A, 2B, or 3 ) according to various embodiments of the present disclosure is an external electronic device. may perform wireless communication with the second electronic device 402 (eg, a smartphone). For example, the first electronic device 401 and the second electronic device 402 communicate with each other in a wireless communication method to communicate commands and/or data (eg, sensor values, biometric data, or user information). work status, mental work intensity or physical work intensity, mental stress resistance, physical stress resistance, user information, and/or guide information) may be shared. For example, the first electronic device 401 may include at least one of a sensor value (raw data), biometric data, a user's work status, mental work intensity or physical work intensity, mental stress resistance, physical stress resistance, and/or user information. A portion is transmitted to the second electronic device 402 through a wireless communication circuit (eg, the communication module 190 of FIG. 1 or the wireless communication circuit 190 of FIG. 3 ), and a guide from the second electronic device 402 information can be received. According to various embodiments, the second electronic device 402 may include at least some of the components of the electronic device 101 illustrated in FIG. 1 .

According to various embodiments, the first electronic device 401 may be at least partially controlled by the second electronic device 402 . For example, the second electronic device 402 may at least partially control a function performed by the first electronic device 401 . For example, the first electronic device 401 may receive a control command from the second electronic device 402 in a wireless communication method.

According to various embodiments, the first electronic device 401 and the second electronic device 402 may interwork with each other to execute at least one program or function at substantially the same time. For example, the second electronic device 402 may include an optical sensor (eg, the optical sensor 310 of FIG. 3 ) and/or a motion sensor (eg, the motion sensor 320 of FIG. 3 ) in the first electronic device 401 . ) to obtain a sensor signal using at least one of and determine the user's work status, and drive the optical sensor 310 and/or the motion sensor 320 according to the determined work status to drive the user's biometric data and/or motion The first electronic device 401 may be controlled to extract data. For example, the first electronic device 401 may receive motion data acquired through the optical sensor 310 and/or the motion sensor 320 and/or sensor signals and/or biometric data acquired through the optical sensor 310 . It may be controlled to transmit to the second electronic device 402 . For example, the second electronic device 402 determines the user's work status based on the received motion data, sensor signals, and/or biometric data, calculates the work intensity according to the determined work status, and among the guide information accordingly At least a portion may be transmitted to the first electronic device 401 to provide guide information through at least one of the first electronic device 401 and the second electronic device 402 .

According to various embodiments, the electronic device (eg, the electronic device 101 of FIGS. 1 to 3 , or the electronic device 401 of FIG. 4 ) includes a memory (eg, the memory 130 of FIGS. 1 or 3 ), motion A sensor (eg, motion sensor 320 of FIG. 3 ), an optical sensor (eg, optical sensor 310 of FIG. 3 ), and a processor operatively coupled with the memory, the motion sensor and the optical sensor (eg, FIG. 1 ) or processor 120 of FIG. 3 ), wherein the memory, when executed, causes the processor to determine whether the user is physically working state or determine whether it is a mental work state, use at least one of the optical sensor and the biometric sensor to calculate a physical work intensity or mental work intensity according to the determined physical work state or mental work state, and the calculated physical work intensity Alternatively, based on at least one of the calculated mental work intensity, it may be set to provide work status related information to the user.

According to various embodiments, the work state related information may include at least one of a maximum value or an average value of the physical work intensity for a specified time, or a maximum value or an average value of the physical work intensity.

According to various embodiments of the present disclosure, the processor may be further configured to: Based on the average value of the intensity of the physical work or the average value of the intensity of the mental work during the specified time, the average value of the physical work intensity increases by more than a first threshold value or the mental work intensity increases by more than a first threshold value When the average value of the task intensity increases by more than a second threshold value, it may be set to provide an alarm for task adjustment to the user.

According to various embodiments, the processor may determine the intensity of the physical work based on at least one of the user's heart rate, speed, steps, or body information of the user derived based on the measurement signals of the motion sensor and the bio-sensor. can be calculated by

According to various embodiments of the present disclosure, the processor may determine the mental task intensity from at least one of a heart rate (HR), a resting heart rate, or a heart rate variability (HRV) of the user derived based on the measurement signals of the motion sensor and the bio-sensor. It can be calculated based on one.

According to various embodiments, the processor may distinguish the physical work state or the mental work state based on the motion information and heartbeat-related information derived from the measurement signal of the motion sensor and the measurement signal of the bio-sensor.

According to various embodiments, the processor may distinguish the physical work state or the mental work state based on a change pattern of motion information and heartbeat-related information derived from the measurement signal of the motion sensor and the measurement signal of the bio-sensor. have.

According to various embodiments, the physical stress resistance may be calculated based on a change in the intensity of the physical work over time, and the mental stress resistance may be calculated based on the change in the intensity of the mental work over time.

According to various embodiments, the processor monitors changes in the intensity of the physical work and the intensity of the mental work of the user based on the physical work intensity, the physical stress resistance, the mental work intensity, and the mental stress resistance. And, it is possible to determine the adaptability to the user's work and the need for management.

According to various embodiments, the processor monitors changes in the intensity of the physical work and the intensity of the mental work of the user based on the physical work intensity, the physical stress resistance, the mental work intensity, and the mental stress resistance. and may provide a replacement, relocation or adjustment guide for the user's work.

5 is a diagram for explaining an operation of an electronic device (eg, the electronic device 101 of FIGS. 1, 2A, 2B and/or 3 or the first electronic device 401 of FIG. 4 ) according to various embodiments; It is a flow chart. Hereinafter, a detailed description of the overlapping description during the operation of the electronic device 101 described with reference to FIGS. 1, 2A, 2B, 3 and/or 4 may be omitted.

According to various embodiments, the processor (eg, processor 120 of FIG. 1 or FIG. 3 ) may in operation 501 an optical sensor (eg, optical sensor 310 of FIG. 3 ) and/or a motion sensor (eg, of FIG. 3 ). The user's work state may be determined based on a signal from at least one of the motion sensors 320).

6A to 6C are diagrams for explaining an operation of distinguishing between physical work and mental work according to various embodiments of the present disclosure;

Referring to FIG. 6A , the horizontal axis of the graph represents time, and the vertical axis represents a value calculated based on the sensor signal of the motion sensor 320 (eg, motion information such as average acceleration) and/or the sensor signal of the optical sensor 310 . It may represent a value calculated based on . According to various embodiments, the first curve 601 represents a change over time of a value (eg, average acceleration) calculated based on a sensor signal of the motion sensor 320 , and the second curve 603 is an optical sensor A change over time of a value (eg, heart rate/stabilizing heart rate) calculated based on the sensor signal of 310 may be represented.

Referring to the first curve 601 in the drawing, it can be seen that the motion information is less than the first threshold (eg, 0.1 (602)) for a specified time period, for example, several seconds to several minutes or more (eg, 30 seconds), and accordingly As a state in which the user does not move, it may be confirmed that the user is in a resting state or mental work state. Also, referring to the second curve 603, it can be seen that the second threshold value (eg, 1.2 (604)) is less than the second threshold (eg, 1.2 (604)) for a specified time period, for example, several seconds to several minutes or more (eg, 30 seconds), and the first curve (601) ) together with the analysis of the change, it can be confirmed that the user is in a resting state without mental stress in a state where there is no movement.

Referring to FIG. 6B , the first curve 611 that is motion information calculated based on the sensor signal of the motion sensor 320 indicates that the motion information is first It can be seen that it is less than a threshold value (eg, 0.1 (612)), and accordingly, it can be confirmed that the user is in a state of motionlessness, such as a state of rest or a state of mental work. In addition, referring to the second curve 613 , which is heart rate-related information calculated based on the sensor signal of the optical sensor 310 , in the drawing, for a specified time, for example, several seconds to several minutes or more (eg, between time 615 and time 616 ). section) It can be seen that it increases significantly above the second threshold (eg, 1.2 (614)), so when combined with the analysis of the change in the first curve 601, the user is under mental stress in a state of no movement. You can confirm that you are in a mental working state.

Referring to FIG. 6C , the first curve 621, which is motion information calculated based on the sensor signal of the motion sensor 320, has motion information for a specified time, for example, several seconds to several minutes or more (eg, a section after time 625). It can be seen that the first threshold value (eg, 0.1 (623)) is significantly increased, and accordingly, it can be confirmed that the user is in an active state in which movement is active. Also in the drawing, referring to the second curve 622, which is heart rate related information calculated based on the sensor signal of the optical sensor 310, for a specified time, for example, several seconds to several minutes or more (eg, a section after time 676) 2 Threshold value (eg, 1.2 (624)) increased significantly, indicating that both motion information and heart rate-related information increased, confirming the state of physical work. In addition, analyzing the change patterns of the first curve 621 and the second curve 623 in the drawing, it can be seen that the pattern of increase similarly occurs after a certain rest period. Accordingly, based on the change pattern of the curves in addition to the analysis of each curve, it can be confirmed that the user is in a physical work state in which he is under physical stress while in motion.

According to various embodiments, in operation 503 , the processor 120 acquires a sensor signal of the optical sensor 310 and/or the motion sensor 320 for a user according to the determined physical task or mental task, and the obtained Based on the sensor signal, the mental task intensity according to the determined physical task or mental task may be calculated.

According to various embodiments, when it is confirmed that the user is in a physical work state, the processor 120 performs physical work based on motion information derived from a sensor signal of the optical sensor 310 and/or the motion sensor 320 and heart rate related information. strength can be calculated. For example, the processor 120 may include a step frequency, a moving speed and/or an average speed for a specified time of the user calculated based on a sensor signal of the motion sensor 320 , and/or an optical sensor The intensity of physical work is calculated using at least one of a heart rate, average heart rate, and/or heart rate variability calculated based on the sensor signal of 310 as an index value between 0 and 100, for example, through a specified machine learning model. can For example, physical activity intensity can be indexed based on oxygen consumption (eg VO2 value) or maximum oxygen consumption (eg % of VO2 max value). For example, the memory 130 may include a step frequency, a moving speed, and a sensor signal of the optical sensor 310 for a specified time of the user calculated based on the sensor signal of the motion sensor 320 . Based on the calculated heart rate and/or user information, it is possible to store an algorithm or a machine learning model for calculating the intensity of physical work designed to calculate a value close to the oxygen consumption or maximum oxygen consumption measured through the above-described gas analyzer. The processor 120 may calculate the oxygen consumption or the maximum oxygen consumption using an algorithm or a machine learning model.

According to various embodiments, in the mental task state, the processor 120 may calculate the mental task intensity based on at least one of a heart rate calculated based on a sensor signal of the optical sensor 310, a resting heart rate, and/or a heart rate variability. can For example, mental work intensity can be indexed based on a stress index. For example, the stress index may be proportional to the heart rate, may be inversely proportional to the resting heart rate, and may be calculated as a value proportional to the heart rate variability.

According to an embodiment, the processor 120 uses at least one of a heart rate calculated based on a sensor signal of the optical sensor 310 , a resting heart rate, and/or a heart rate variability, to generate a machine learning model stored in the memory 130 . Through this, for example, the mental stress index may be calculated as an index value between 0 and 100.

According to an embodiment, the mental task intensity may be calculated based on at least one of a heart rate calculated based on a sensor signal of the optical sensor 310 , a resting heart rate, and/or a heart rate variability. For example, mental work intensity can be indexed based on a stress index. For example, the stress index may be proportional to the heart rate, may be inversely proportional to the resting heart rate, and may be calculated as a value proportional to the heart rate variability.

According to an embodiment, the processor 120 sets the physical work intensity (eg, a value indexed from 0 to 100 as oxygen consumption or maximum oxygen consumption) calculated from the user's physical work state to a worker performing the same task (eg: It can be analyzed by comparing with similar workers in the same industry).

According to one embodiment, the processor 120 calculates the mental task intensity (eg, a value indexed from 0 to 100 as a stress index) calculated from the mental task state of the user, and a worker (eg, similarity in the same industry) performing the same task. labor workers) and can be analyzed.

According to an embodiment, the processor 120 may calculate the physical stress resistance or the mental stress resistance based on the calculated change in the work intensity.

7A and 7B are diagrams for explaining an operation of calculating the stress resistance of physical work according to various embodiments of the present disclosure;

According to various embodiments, the processor 120 may calculate the physical stress resistance based on a change in the intensity of physical work over time. For example, if the intensity of physical work decreases and the recovery time is short after performing physical work, the resistance to physical stress may be high. For example, if the intensity of physical work decreases and it takes a long time to recover after performing physical work, the resistance to physical stress may be low.

Referring to FIG. 7A , for a user (A), a first curve 701 , which is motion information calculated based on a sensor signal of the motion sensor 320 , is designated for a specified time, for example, several seconds to several minutes or more (eg time). It can be seen that the motion information (section 705 to time 706) has significantly increased above the first threshold (eg, 0.1 (702)), and accordingly, it can be confirmed that the user is in an active state in which movement is active. Also in the drawing, referring to the second curve 703 that is heart rate related information calculated based on the sensor signal of the optical sensor 310, for a specified time, for example, several seconds to several minutes or more (eg, from time 705 to time 706 ). section) substantially increased significantly above the second threshold (eg, 1.2 (704)), indicating that both motion information and heart rate-related information have increased, so that it can be confirmed that the state is physically working. It can be seen from the figure that the change patterns of the first curve 701 and the second curve 703 also exhibit similarly increasing and falling patterns. Accordingly, based on the change pattern of the curves in addition to the analysis of each curve, it can be confirmed that the user is in a physical work state in which he is under physical stress while in motion.

According to an embodiment, in the drawing, the first curve 701 that is motion information calculated based on the sensor signal of the motion sensor 320 for the user A in the drawing is after a specific time (eg, a section from time 706 to later). It can be known that the first threshold value (eg, 0.1 (702)) has fallen or less, and accordingly, it can be confirmed that the user is in a resting state in which the movement is stopped. In addition, referring to the second curve 703 , which is heart rate-related information calculated based on the sensor signal of the optical sensor 310 , in the drawing, it substantially decreases after a specific time (eg, a section after time 706 ) to the second threshold value. (Example: 1.2 (704)) or lower, it can be seen that it is a recovery period after a physical work state.

Referring to FIG. 7B , for the user (B), the first curve 711, which is motion information calculated based on the sensor signal of the motion sensor 320, is designated for a specified time, for example, several seconds to several minutes or longer (eg, time). It can be seen that the motion information (section from 715 to time 716) has significantly increased above the first threshold (eg, 0.1 (702)), and accordingly, it can be confirmed that the user is in an active state in which movement is active. In addition, referring to the second curve 712 , which is heart rate-related information calculated based on the sensor signal of the optical sensor 310 in the drawing, for a specified time, for example, several seconds to several minutes or more (eg, from time 715 to time 716 ). section) substantially increased significantly above the second threshold (eg, 1.2 (704)), indicating that both motion information and heart rate-related information have increased, so that it can be confirmed that the state is physically working. It can be seen from the figure that the change patterns of the first curve 711 and the second curve 713 also similarly show an increase and a decrease pattern. Accordingly, based on the change pattern of the curves in addition to the analysis of each curve, it can be confirmed that the user is in a physical work state in which he is under physical stress while in motion.

According to an embodiment, in the drawing, the first curve 711 that is motion information calculated based on the sensor signal of the motion sensor 320 for the user B in the drawing is after a specific time (eg, a section from time 716 to later). It can be known that the first threshold value (eg, 0.1 (702)) has fallen or less, and accordingly, it can be confirmed that the user is in a resting state in which the movement is stopped. In addition, referring to the second curve 712 , which is heart rate related information calculated based on the sensor signal of the optical sensor 310 , in the drawing, the second threshold value is substantially decreased after a specific time (eg, a section after time 706 ). (Example: 1.2 (704)) or lower, it can be seen that it is a recovery period after a physical work state.

Looking at the characteristics of the recovery period after physical work of the user (A) of FIG. 7A and the user (B) of FIG. 7B , the recovery time of the user (A) is relatively longer than that of the user (B). long can be seen. According to various embodiments, the processor 120 may calculate the user's physical stress resistance by using a relational expression having a high physical stress resistance when the recovery time is short by cumulatively evaluating the recovery time after the physical work state. have.

According to an embodiment, when there are 50 subjects to measure resistance to physical stress, and their average recovery time is measured to be 90 seconds, the average recovery time may be set to 90 seconds as a reference recovery time for physical work at that time. The physical stress resistance may be calculated, for example, as a value obtained by dividing the reference recovery time by the recovery time of the corresponding user. For example, if the recovery time of user (B) is 60 seconds, the physical stress resistance value may be 90/60=1.5, and if the recovery time of user (A) is 120 seconds, the physical stress resistance value is 90/ It can be calculated as 120=0.75. It can be seen that the physical stress resistance of the user (A) of FIG. 7A is lower than that of the user (B) of FIG. 7B .

8A and 8B are diagrams for explaining an operation of calculating a stress resistance of a mental task according to various embodiments of the present disclosure;

According to various embodiments, the processor 120 may calculate the mental stress resistance based on a change in mental task intensity with time. For example, if the recovery time for mental task intensity after mental task performance is short, mental stress resistance may be high. For example, a short period of time during which mental task intensity increases at the onset of mental task may result in higher mental stress resistance. For example, if mental task intensity takes longer to recover after mental task performance, mental stress resistance may be low. For example, if mental task intensity takes a long time to increase at the onset of mental task, mental stress resistance may be low.

Referring to FIG. 8A , for the user (A), a third curve representing the mental stress index, which is the mental work intensity calculated using the heart rate-related information calculated based on the sensor signal of the optical sensor 310 in the mental work state Referring to (801), it can be seen that for a certain period of time (eg, the interval from time 805 to time 808), it substantially increases significantly above the third threshold (eg, 20 (802)), indicating that mental work stress is being experienced. . In particular, in the time interval (eg, the interval from time 806 to time 807), the mental stress index is significantly higher above the fourth threshold (eg, 60 (804)), and in the before and after intervals, the stress index is, for example, the third threshold value The section (a) that increases from the fourth threshold to the third threshold (a) and the section (a) that decreases from the fourth threshold to the third threshold are the section (a) in which the stress index increases after the onset of the mental work state and the stress index after the mental work state, respectively It can be seen that is the section (b) in which is lowered.

According to one embodiment, in the drawing, the longer the section (a) in which the stress index according to the mental work state increases and the section (b) in which the stress index decreases after the mental work state is longer, the lower the ability to resist mental stress, and the shorter these sections are. The higher the score, the better the ability to resist mental stress.

According to an embodiment, the mental stress resistance may be calculated based on the sum of the section (a) in which the stress index increases and the section (b) in which the stress index decreases after the mental work state.

Referring to FIG. 8B , for the user (B), a third curve representing a mental stress index, which is a mental work intensity calculated using heart rate-related information calculated based on a sensor signal of the optical sensor 310 in a mental work state Referring to (811), it can be seen that for a certain period of time (eg, from time 815 to time 818), it substantially increases significantly above the third threshold (eg, 20 (802)), and it can be seen that the person is under mental work stress. . In particular, the time interval (eg, the interval from time 816 to time 817) has a significantly higher mental stress index above the fourth threshold (eg, 60 (804)), and in the anterior and posterior intervals, the stress index is, for example, the third threshold The interval (a) increasing from the fourth threshold to the third threshold and the interval (b) decreasing from the fourth threshold to the third threshold are the interval (a) in which the stress index increases after the onset of the mental work state and the stress index after the mental work state, respectively. It can be seen that is the section (b) in which is lowered.

According to one embodiment, in the drawing, the longer the section (a) in which the stress index according to the mental work state increases and the section (b) in which the stress index decreases after the mental work state is longer, the lower the ability to resist mental stress, and the shorter these sections are. The higher the score, the better the ability to resist mental stress.

According to an embodiment, a time (a+b) that is the sum of the period (a) in which the stress index increases and the period (b) in which the stress index decreases after the mental work state is referred to as a recovery time, and mental stress resistance is calculated based on this can do.

According to one embodiment, for example, when there are 50 subjects to measure mental stress resistance, and their average recovery time is measured to be 200 seconds, the reference recovery time for mental tasks at that time can be set to 200 seconds, which is the average recovery time. have. The mental stress resistance may be calculated, for example, as a value obtained by dividing the reference recovery time by the recovery time of the corresponding user. For example, if the recovery time of user (B) is 150 seconds, the physical stress resistance value may be 200/160=1.25, and if the recovery time of user (A) is 250 seconds, the mental stress resistance value is 200/ It can be calculated as 250=0.8. It can be seen that the mental stress resistance of the user (A) of FIG. 8A is lower than that of the user (B) of FIG. 8B .

According to various embodiments, in operation 505 , the processor 120 may provide work status related information to the user based on the work intensity.

According to various embodiments, the processor 120 may provide the work state related information to the user in consideration of the physical stress resistance or the mental stress resistance in addition to the work intensity.

According to various embodiments, the processor 120 drives the optical sensor 310 and/or the motion sensor 320 according to the determination of the physical task state or the mental task state to monitor the user and measure the physical task intensity and/or mental task intensity. Based on the calculation, the work status related information may be provided to the first electronic device 101 and/or the second electronic device 102 .

According to an embodiment, the work status-related information may include various information on physical work intensity and/or mental work intensity according to user monitoring (eg, intensity, change in intensity over time, maximum intensity and/or various statistics related to intensity) trend) may be included.

According to an embodiment, the work status-related information includes an alarm to the user (eg, an immediate work stop and/or rest suggestion) based on various information on the physical work intensity and/or mental work intensity according to user monitoring. Guide information may be included.

According to an embodiment, the work status-related information includes various information on physical work intensity and/or mental work intensity according to user monitoring, work relocation and/or task relocation assigned to a user based on physical stress resistance and/or mental stress resistance. / or may include guide information for adjustment.

According to an embodiment, the first electronic device 401 may transmit work status information about the user to the second electronic device 402 to share, for example, the user's work status information with a manager in addition to the user.

According to an embodiment, when the user starts work, the first electronic device 401 may start monitoring the user's work state and work intensity. For example, the first electronic device 401 is interlocked with the start, stop, and/or termination of an external electronic device (eg, the user's work computer) for access to and/or work of the user's work place, and the user's work It can be set to start, stop and/or end monitoring actions for status and work intensity.

According to various embodiments, the first electronic device 401 may display work status information about the user on a display (eg, the display module 160 of FIG. 1 or FIG. 3 ). Also, the second electronic device 402 may display work status information about the shared user on a display (eg, the display module 160 of FIG. 1 or FIG. 3 ).

9 to 12 are diagrams for explaining examples of an operation of providing information related to a work state according to various embodiments of the present disclosure; Hereinafter, an operation of providing work-related information of a user according to various embodiments will be described in detail with reference to FIGS. 9 to 12 .

According to an embodiment, when the user starts work, the first electronic device 401 starts monitoring the user's work state and work intensity to calculate work intensity, and statistical values for changing values of work intensity based on the stress resistance can be calculated.

According to an embodiment, when the user's average work intensity is high and the stress resistance is high, for example, it can be analyzed as a situation in which a task with high work intensity itself is assigned, and since the user's stress resistance is high, the user's stress resistance is high. It can be judged that there is a high possibility of adaptation, and it can be judged that there is a need for management of injuries or overwork until they adapt to work.

According to an embodiment, when the user's average work intensity is high and the stress resistance is low, for example, it can be analyzed as a situation in which a task with high work intensity itself is assigned, and since the user's stress resistance is low, the user's stress resistance is low. It may be judged that the possibility of adaptation is low, replacement or relocation to another job may be recommended, and if this is not possible, it may be determined that continuous careful management is necessary.

According to an embodiment, when the average work intensity of the user is low and the stress resistance is high, for example, it can be analyzed as a situation in which a job with low work intensity itself is assigned. It can be judged that there is a high possibility of adapting to the job of a person, and replacement or relocation to another job with high intensity can be recommended.

According to an embodiment, when the user's average work intensity is low and the stress resistance is low, for example, a situation in which the work intensity itself is assigned to a low task may be analyzed, and since the user's stress resistance is low, the user's stress resistance is suddenly high It can be judged that the possibility of adaptation is low when the task of the person is assigned, and it can be judged that the existing task is maintained but careful management of the task intensity is necessary.

9 shows a first screen 900 providing user A's work status related information, a second screen 910 providing user B's work status related information, and a third screen providing user C's work status related information. A screen 920 may be displayed.

According to an embodiment, the first screen 900 may include a first graph 901 indicating the intensity of the physical work of the user A and a second graph 902 indicating the intensity of the mental work. For example, the first graph 901 and the second graph 902 show the average physical work intensity 903 and the average mental work intensity 904, which are the average work intensity during working hours, respectively, and the highest physical work intensity which is the highest work intensity ( 905) and a baseline physical task intensity 907 and a baseline mental task intensity 908 that are the highest mental task intensity 906 and/or baseline task intensity. For example, during a given business hour, user (A)'s average physical workload intensity (903) is 30% and average mental workload intensity (904) is 20%, so that average physical workload intensity (903) is equal to average mental workload intensity (904) It is somewhat higher than that of , but it is lower than the standard physical work intensity (907) and the standard mental work intensity (908), indicating that the work intensity is not high on average. For example, during the corresponding business hours, user (A)'s peak physical task intensity (905) and peak mental task intensity (906) are higher than the baseline physical task intensity (907) and baseline mental task intensity (908), resulting in temporary work intensity It can be seen that there is an increase in , and it can be seen that the distribution of tasks during the time period is not appropriate and the situation is concentrated at a specific time.

According to an embodiment, the second screen 910 may include a first graph 911 representing the intensity of physical work of the user B and a second graph 912 representing intensity of mental work. For example, the first graph 911 and the second graph 912 are the average physical work intensity 913 and the average mental work intensity 914, which are the average work intensity during working hours, respectively, and the highest physical work intensity which is the highest work intensity ( 915) and the highest mental task intensity (916) and/or baseline physical task intensity (917) and baseline mental task intensity (918), which are baseline task intensity. For example, during a given business hour, user (A)'s average physical workload intensity (913) is 10% and average mental workload intensity (914) is 40%, so that average physical workload intensity (913) is equal to average mental workload intensity (914). Although it is lower than the standard physical work intensity (917) and the standard mental work intensity (918), both are low, indicating that the work intensity is not high on average. For example, during the corresponding business hours, the highest physical work intensity (915) of user (A) is lower than the reference physical work intensity (917), indicating that the physical work intensity is low, but the highest mental work intensity (916) is the reference mental work intensity (916). It is higher than the task intensity (918), so it can be seen that the task intensity is temporarily increased, and it can be seen that the task distribution is unevenly driven during work hours.

According to an embodiment, the third screen 920 may include a first graph 921 indicating the intensity of physical work of the user A and a second graph 922 indicating the intensity of mental work. For example, the first graph 921 and the second graph 922 show the average physical work intensity (923) and the average mental work intensity (924), which are the average work intensity during the working hours, respectively, and the highest physical work intensity ( 925) and baseline physical task intensity (927) and baseline mental task intensity (928), which are peak mental task intensity (926) and/or baseline task intensity. For example, during a given business hour, user (A)'s average physical workload intensity (923) is 20% and average mental workload intensity (924) is 30%, so that average physical workload intensity (923) is equal to average mental workload intensity (924). Although it is somewhat lower than that of , it is lower than the standard physical work intensity (927) and standard mental work intensity (928), indicating that the work intensity is not high on average. For example, during that business hour, user (A)'s peak physical task intensity (925) and peak mental task intensity (926) were lower than baseline physical task intensity (927) and baseline mental task intensity (928), respectively, during that time. It can be seen that the distribution is appropriate.

10 shows a first screen 1010 and a second screen 1020 for providing work status related information of a user to the display of the first electronic device 401, and a second electronic device 402 for providing work status related information of the user. ) may be shown to provide a third screen 1040 to the display.

According to an embodiment, the first screen 1010 provided by the first electronic device 401 may include a first graph 1001 indicating the user's physical work intensity. For example, the first graph 1001 may represent, for example, an average physical work intensity 1003 during working hours as 20%, and may include a highest physical work intensity 1005 and a reference physical work intensity 1007 . have.

According to an embodiment, the second screen 1020 provided by the first electronic device 401 may include a second graph 1002 indicating the intensity of the user's mental work. For example, the second graph 1002 may represent an average mental task intensity 1004 during working hours, for example 30%, and may include a peak mental task intensity 1006 and a baseline mental task intensity 1008 . have.

According to an embodiment, the third screen 1040 provided by the second electronic device 402 displays the intensity of the user's physical work for each time period using, for example, a bar graph 1031, so that the user's body during work hours is displayed. It can monitor task intensity and provide guidance information such as task allocation, alarms and/or relocation.

According to an embodiment, the third screen 1040 provided by the second electronic device 402 indicates the intensity of the user's mental work for each time period using, for example, a bar graph 1032, so that the user's mind during work hours is displayed. It can monitor task intensity and provide guidance information such as task allocation, alarms and/or relocation.

According to an embodiment, by monitoring the trend of the user's physical work intensity by time period and the mental work intensity by time period through the third screen 1040, information on how the physical work and the mental work are distributed over time and , if a similar pattern occurs at a specific time during business hours, it is possible to determine the task in which the physical task or the mental task intensity is high. In addition, when the work intensity increases for a certain time or more or the intensity over a certain intensity is repeated, guide information including an alarm for recommending a break and/or work relocation to the user and/or manager may be provided.

11 is an example of a screen showing the user's work status related information according to the passage of time of the user. A second screen 1110 providing information related to the user's work status may be displayed at a first screen 1100 and a second time point 1110 (eg, after 6 hours and 20 minutes after the start of work).

According to an embodiment, the first screen 1100 may include a first graph 1101 indicating the intensity of the user's physical work and a second graph 1102 indicating the intensity of the mental work of the user at the first time point. For example, the first graph 1101 may represent, for example, 25% of the physical work intensity 1103 of the corresponding time point (eg, the first time point), and it may be confirmed that it is lower than the reference physical work intensity 1107. .

According to an embodiment, the second screen 1110 may include a first graph 1111 indicating the intensity of the user's physical work and a second graph 1112 indicating the intensity of the mental task at the second time point. For example, the second graph 1112 may represent, for example, 40% of the mental work intensity 1114 at the time point (eg, the second time point), and it can be confirmed that it is lower than the reference mental work intensity 1118 . .

12 illustrates a first screen 1200 , a second screen 1210 , and a third screen 1220 providing work status related information during the user's work hours.

According to an embodiment, the first screen 1200 includes a first graph 1201 indicating the intensity of physical work of the user, a second graph 1202 indicating the intensity of mental work, and an elapsed time 1209 from the start of the work to the present. can do. For example, the first graph 1201 may indicate that the average physical work intensity 1203 from the start of work to the present is 33%, and it can be seen that this value is lower than the reference physical work intensity 1207 . However, since it is confirmed that the total working hours (1209) from the start of work to the present is 8 hours and 10 minutes, exceeding the recommended time of 8 hours, a notification 1231 indicating that the recommended work time has been exceeded may be provided, and accordingly, the user may induce them to stop working.

According to an embodiment, the second screen 1210 displays a first graph 1211 representing the user's physical work intensity, a second graph 1212 representing the mental work intensity, and an elapsed time 1219 from the start of the work to the present. According to this, it can be seen that 48 minutes have elapsed from the start of the work to the present. For example, in the first graph 1211, it can be seen that the average physical work intensity 1213 of the 48-minute elapsed time from the start of the work to the present is 80%, and this value is higher than the reference physical work intensity 1217 value can be seen. Therefore, although the total work time 1219 from the start of the work to the present is 58 minutes, it is confirmed that the physical work intensity 1213 exceeds the reference intensity, and thus a notification 1232 indicating that the physical work intensity is high may be provided. Accordingly, it may induce the user to stop working or take a break.

According to an embodiment, the third screen 1220 displays a first graph 1221 indicating the intensity of the user's physical work, a second graph 1222 indicating the intensity of mental work, and an elapsed time 1229 from the start of the work to the present. may include For example, the second graph 1222 may represent an elapsed time 1229 since the start of the work, according to which 5 hours and 28 minutes have elapsed since the start of the work, and the average mental work intensity 1224 is 90%. It can be seen that this value is higher than the reference mental task intensity (1228). Thus, the total work hours 1229 from the start of the shift to the present are 5 hours and 28 minutes, but the mental work intensity 1224 is found to be above the baseline intensity, providing a reminder 1233 indicating that the mental work intensity is high, , it may induce the user to stop working or take a break.

The embodiments disclosed in this document are only presented as examples for easy explanation and understanding of technical content, and are not intended to limit the scope of the technology disclosed in this document. Therefore, the scope of the technology disclosed in this document should be construed to include all changes or modifications derived from the technical ideas of various embodiments disclosed in this document in addition to the embodiments disclosed herein.

Claims (20)

In an electronic device,
Memory;
motion sensor;
optical sensor; and
a processor operatively coupled to the memory, the motion sensor, and the optical sensor;
The memory, when executed, causes the processor to:
determine whether the user is in a physical work state or a mental work state based on a sensor signal of at least one of the motion sensor and the optical sensor for the user;
using at least one of the optical sensor and the biometric sensor to calculate physical work intensity or mental work intensity according to the determined physical work state or mental work state;
based on at least one of the calculated physical work intensity and the calculated mental work intensity, configured to provide work status related information to the user
electronic device.
The method of claim 1,
The work state-related information includes at least one of a maximum value or an average value of the physical work intensity for a specified time, or a maximum value or an average value of the physical work intensity.
3. The method of claim 2,
The processor is configured to determine that, based on the average value of the physical work intensity or the average value of the mental work intensity during the specified time, the average value of the physical work intensity increases by more than a first threshold value or the average value of the mental work intensity an electronic device configured to provide an alarm for task adjustment to the user when it increases above this second threshold.
The method of claim 1,
The processor is configured to calculate the physical work intensity based on at least one of a heart rate, a speed, a number of steps of the user derived based on measurement signals of the motion sensor and the bio-sensor, and body information of the user. .
The method of claim 1,
The processor is configured to calculate the mental task intensity based on at least one of a heart rate (HR), a resting heart rate, and a heart rate variability (HRV) of the user derived based on measurement signals of the motion sensor and the bio-sensor electronic device.
The method of claim 1,
The processor is configured to distinguish between the physical work state and the mental work state based on the motion information and heartbeat-related information derived from the measurement signal of the motion sensor and the measurement signal of the bio-sensor.
7. The method of claim 6,
The processor is configured to discriminate between the physical work state and the mental work state based on a change pattern of motion information and heartbeat-related information derived from a measurement signal of the motion sensor and a measurement signal of the bio-sensor.
The method of claim 1,
An electronic device for calculating a physical stress resistance based on a change in the physical work intensity over time, and calculating a mental stress resistance based on a time change in the mental work intensity.
9. The method of claim 8,
The processor is configured to monitor changes in the physical work intensity and the mental work intensity of the user based on the physical work intensity, the physical stress resistance, the mental work intensity, and the mental stress resistance, and Electronic devices that determine adaptability and management needs for work.
9. The method of claim 8,
The processor is configured to monitor changes in the physical work intensity and the mental work intensity of the user based on the physical work intensity, the physical stress resistance, the mental work intensity, and the mental stress resistance, and An electronic device that provides a replacement, relocation or adjustment guide to a job.
A method for an electronic device, comprising:
determining whether the user is in a physical work state or a mental work state based on a sensor signal of at least one of the motion sensor and the optical sensor for the user;
calculating a physical work intensity or mental work intensity according to the determined physical work state or mental work state by using at least one of the optical sensor and the biometric sensor; and
Based on at least one of the calculated physical work intensity and the calculated mental work intensity, the operation of providing work status related information to the user; a method comprising a.
12. The method of claim 11,
The work status-related information includes at least one of a maximum value or an average value of the physical work intensity for a specified time, or a maximum value or an average value of the physical work intensity.
13. The method of claim 12,
The providing operation may include, based on the average value of the physical work intensity or the average value of the mental work intensity during the specified time, the average value of the physical work intensity increases by more than a first threshold value or the average of the mental work intensity A method of providing an alarm for reconciliation of tasks to the user when the value increases above a second threshold.
12. The method of claim 11,
The calculating operation is a method of calculating the intensity of physical work based on at least one of a heart rate, a speed, a number of steps, and the user's body information derived based on measurement signals of the motion sensor and the bio-sensor .
12. The method of claim 11,
The calculating operation may include calculating the mental task intensity based on at least one of a heart rate (HR) of the user, a resting heart rate, and a heart rate variability (HRV) derived based on signals measured by the motion sensor and the bio-sensor. How to calculate.
12. The method of claim 11,
The determining operation is a method of discriminating the physical work state or the mental work state based on the motion information and heartbeat-related information derived from the measurement signal of the motion sensor and the measurement signal of the bio-sensor.
17. The method of claim 16,
The determining operation is a method of discriminating the physical work state or the mental work state based on a change pattern of motion information and heartbeat-related information derived from the measurement signal of the motion sensor and the measurement signal of the bio-sensor.
12. The method of claim 11,
The calculating operation is a method of calculating a physical stress resistance based on a change in the intensity of the physical work over time, and calculating a mental stress resistance based on a change in the intensity of the mental work over time.
19. The method of claim 18,
based on the physical work intensity, the physical stress resistance, the mental work intensity, and the mental stress resistance, monitor changes in the user's physical work intensity and the mental work intensity, and adapt to the user's work The method further comprising determining a possibility and a need for management.
19. The method of claim 18,
The providing operation includes monitoring changes in the physical work intensity and the mental work intensity of the user based on the physical work intensity, the physical stress resistance, the mental work intensity, and the mental stress resistance, and the user How to provide replacement, relocation, or adjustment guides to your work.

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