GB2566434A

GB2566434A - Sensor monitoring architecture

Info

Publication number: GB2566434A
Application number: GB1711297.0A
Authority: GB
Inventors: Jamal Waheed
Original assignee: Individual
Current assignee: Individual
Priority date: 2017-07-13
Filing date: 2017-07-13
Publication date: 2019-03-20
Also published as: GB201711297D0

Abstract

A system for monitoring a user 2 status comprises a controller 22 for integrating user location and sensor data. The user location data is provided by a location device for the user which may be statically positioned within a user environment 10. The user sensor data is provided by a sensor device 11, 15, 17, 21, 19 which may be fitted to (eg worn by) the user. In a second aspect, a system for monitoring a user status within an environment integrates location data of the user within the environment, sensed biometric data (eg pulse rate or blood pressure) associated with the user, and data sensed within the environment. The system may predict an increased risk of an event using predictive analytics and alert a caregiver. In a third aspect a system for monitoring a user status within an environment maps user location data to an area within the environment.

Description

SENSOR MONITORING ARCHITECTURE

The present disclosure relates to a sensor monitoring apparatus, architecture and process, particularly for the medical care of users in their homes.

Technologies exist for sensors providing biometric data of users to which the sensors are attached. Such sensors can, for example, detect the pulse rate, heart rate, blood pressure etc. of a user.

Such sensors can be provided to a user, to allow biometric data associated with the user to be sensed.

In addition to such sensors which give absolute information on biometric data of a user, sensing devices are also known which are used in particular for care of the elderly. These included, for example, sensing devices which can detected if a user has a fall. Such sensors are generally associated with a dedicated monitoring system.

An aim is to provide an improvement to the provision of sensors for providing remote monitoring to control the giving of care.

In general there is provided a system in which a location of a user is determined, and then partnered with sensor technology associated with the user.

The sensor technology may select any one of: a change in pace, speed, acceleration, or walking gait. The sensor technology may also provide biometric information, such as pulse rate. The sensor device may be provided by wearable technology or fixed sensor technology in the home.

There may be provided sensor technology for sensing any one of movement, pace, gait, or geo-location. The sensor technology may not need to be worn, and may instead be located in the comers of a room. The sensor technology may be either standalone, or built into other sensors such as smoke detectors.

Such sensors may then be integrated with other wearable or non-wearable technology to add additional information such as heart rate, respiratory rate, temperature, blood pressure, medication compliance.

The predictive power for detecting a clinical event early is increased.

If sensor technology is in a fixed location, it may include the ability to be able to identify the person in question using silhouette based recognition technology or some other form of identification mechanism to ensure it monitors only the person in question.

In general there may be provided a system and a method in which sensor data captured by one or more sensors attached to a user is integrated with sensor data captured by one or more sensors able to identify a characteristic of the user. The one or more sensors attached to the user may include a proprietary device such as FitBit® device. The one or more sensors attached to the user may include a wearable device. The one or more sensors able to identify a characteristic of the user may be positioned within the users environment, and not attached to the user, or may be attached to the user.

The invention is described further with reference to the accompanying Figures, in which:

Figures 1 illustrates a user equipped with exemplary sensors;

Figure 2 illustrates a plan of an exemplary living accommodation of a user;

Figure 3 illustrates an exemplary process for a monitoring system utilising sensors;

Figure 4 illustrates an exemplary architecture for a monitoring system utilising sensors;

Figure 5 illustrates an exemplary set of data captured for a monitoring system utilising sensors; and

Figure 6 illustrates an exemplary integration of sensing system;

Improvements are now described by way of reference to various examples and embodiments.

With reference to Figure 1, there is generally illustrated a user (person) denoted by reference numeral 2. In the illustrated example, the user is equipped with three sensors denoted by reference numerals 4, 6 and 8.

Sensor 4 may be a sensor which the user is wearing on a wristband, and may be a sensor which allows biometric data such as the pulse rate, heart rate, blood pressure, respiratory rate, temperature or such like of the user to be sensed.

The sensor may be a stand-alone dedicated biometric sensor for recording such information, or may be a device which is equipped to record such information, such as a watch-type device.

The sensor may be provided by integrating a third party sensing device, such as a sensor provided by FitBit®. In alternatives, ‘white label’ sensors may be integrated.

Sensor 6 may be a sensor which the user is wearing on a wristband, and may be a sensor which allows location data such as GPS positioning data to be sensed. The sensor may be a stand-alone GPS sensor for recording such information, or may be a device which is equipped to record such information, such as a watch-type device.

Sensor 8 represents a sensor which may be located on the body of a user, and which may be a sensor for detecting acceleration such as a sensor used to detect the speed and frequency of movement of a user to which the sensor is attached, or to detect if the user to which the sensor is attached has a fall. Sensor 8 illustrates that the sensor does not have to be provided by a watch-type device or otherwise by wristmounts. The sensor 8 may be attached to clothing, such as attached to a belt of the user.

Sensor 8 represents a sensor which may be used to track medication compliance.

In general, any number of sensors may be provided to monitor/track various events and/or conditions.

A sensor may be associated with a user in any other way. A sensor may be held to the user by a belt clip, by a lanyard, or may be a so-called wearable device mounted within clothing. In addition, a sensor may not be attached to a user, such as where a movement sensor is provided and positioned in a room.

The user 2 may be provided with any number of sensors.

Figure 2 illustrates an example plan layout of living accommodation of the user 2, generally illustrated by reference numeral 10. The exemplary living accommodation 10 includes a living room 12, a bathroom 14, a kitchen 16, a bedroom 18, and a hallway

20. Reference numeral 24 illustrates the door entrance to the living accommodation, which provides access to the hallway 20.

Provided in the living accommodation is a local control module 22. In the example, this is positioned in the hallway 20, but it may be positioned anywhere within the living accommodation 10.

Also shown in Figure 2 is the exemplary user 2, having a wrist-mounted communication device 26. The user 2 is positioned within the bedroom 18.

The wrist-mounted communication device 26 is configured to communicate with the local control module 22, to provide various information to the control module 22.

Preferably, the local control module 22 is programmed with a map of the living accommodation 10, and receives GPS information from the device 26. By receiving this GPS information, in conjunction with the map of the living accommodation, the control module is able to locate the user 2 within the rooms of the living accommodation, and build up a database of the movement of the user 2, including, for example, the time the user spends moving, the speed of the movement of the user, and the location of the user within the living accommodation, such as the identity of the rooms the user is in, and thereby the period of time of frequency the user is in each room.

The device 26 may be a GPS device, or may be a GPS device equipped with other sensing functionality, such that it also provides biometric data.

With reference to Figure 3, there is illustrated an exemplary process for utilising the device 26 and the local control module 22.

In the process of Figure 3, in a step 32 a map of living accommodation is configured. This includes mapping the living accommodation using GPS coordinates, and defining the rooms of the living accommodations according to their GPS coordinates. Thus a geo-fence may be created around each room of the living accommodation.

After step 32, a GPS map of the living accommodation is created. The creation of this map is specific to the living accommodation 10. For different living accommodations, different maps are created.

In a step 34, the GPS position data of the device 26 is tracked. The device 26 receives GPS position data using a standard GPS positioning system with which it is configured, and transmits that GPS positioning date to the local control module 22. The local control module 22 is thus configured to track the GPS data of the device 26.

In a step 36, the local control module 22 cross-references the stored map of the living accommodation with the GPS location data of the device 26. By crossreferencing this data, the control module is able to identify the location of the device 26 within the living accommodation 10, and to determine the room within the living accommodation within which the device 26 is accommodated.

In a step 38 the local control module 22 identifies the frequency with which the user enters the various rooms using this mapped data.

In a step 40 the local control module 22 identifies the duration of time which the user spends in the various rooms using this tracked data.

In a step 42 the local control module 22 identifies periods of movement and the duration of periods of movement of the user using this tracked data.

In a step 44 the local control module 22 identifies the speed of periods of movement of the user using this tracked data.

In a step 46 the local control module 22 identifies directions of periods of movement of the user using this tracked data.

In general, the process can be configured to carry out any further processing based on the data received from the device 26 as required.

With reference to Figure 4, there is illustrated an architecture of the control module 22.

As illustrated in Figure 4, the device 26 transmits information as denoted by signal 41. The information transmitted is, for example, GPS location data, and data (if any) recorded by sensors such as biometric data.

The local control module 22 includes a local interface 48, a network interface 52, a processor 50, a memory 56, a location mapping module 58, a speed determination module 60, a direction determination module 62, an event determination module 64, and an alarm generation module 66. All the elements of the control module 22 are interconnected via a communication bus 51.

The information transmitted by the device 26 is received by the local interface 48. The local interface 48 places this information on the communication bus 51. The information received by the local interface 48 is stored in the memory 54 under the control of the processor. The information stored in the memory is then retrieved by ones of the determination modules 56 to 62 in order for them to perform their determination, and the determined information is then also stored in the memory 54. The alarm generation module is able to generate alarms based on the information determined, or based on biometric data stored in the memory.

The local control module 22 is configured to include any appropriate modules as necessary to determined additional information based on the sensed data.

The processor 50 controls the network interface to transmit alarms and/or determined data to a network control module. The stored data may be intermittently transmitted to the network control module, or only alarms may be transmitted to the network controller.

With reference to Figure 5 there is illustrated a table of data 70 which may be built up for a user. This table is exemplary. Figure 5 illustrates a table for a particular user identified in filed 71 as “User #”.

In a row 72, raw data 73 is accumulated, having associated columns for GPS location 74 and time 75.

In a row 76, data is determined for the living room location 77 (example location 18 of Figure 2), having associated columns for time entered 78, time exited 79, and duration in the living room 80. This data may be determined from the raw GPS data in row 72.

In a row 81, data is determined for the living room location 82 (example location of Figure 2), having an associated column for frequency in a 24 hour period. This data may be determined from the raw GPS data in row 72.

In a row 84, data is determined for movement of the user85, having associated columns for 24 hour distance 86 and average speed in a 24 hour period 87. This data may be determined from the raw GPS data in row 72. This data may be determined from a specific user sensor device. This data may be determined from a combination of the raw GPS data in row 72, and from specific user sensor data.

Row 88 is an example for data which may be captured from a biometric sensor, such as hear rate data 89, for which column 9 includes an average for a 24 hour period.

Row 91 is an example of alarm conditions which may be monitored as denoted by column 92. Within row 92, row 93 denotes an acceleration and direction alarm condition 93, and row 94 denotes a heart rate alarm condition. These alarm conditions may be detected by actual sensors associated with the user, rather than being determined from sensor data returned.

As well as providing an alarm for a single component (parameters may be set for such an alarm), data from various sensor devices is integrated onto a single platform, and this data used to record a baseline of normal patterns of biometric data or behaviours. Deviations from these normal behaviour patterns will be used to develop probability of fall information using risk based algorithms so that the possible increased risk of a fall can be highlighted and alerted to caregivers before it occurs. If a fall actually occurs the sensors may detect lack of movement or static location and will alert caregivers, a control centre or emergency services as required. It may also connect to a smart home device such as Alexa or equivalent, so that the caregiver or monitoring centre can talk directly to the user.

With reference to Figure 6 there is illustrated an exemplary overall architecture 110 of a system.

In the exemplary architecture 110 there is shown a first local control module 100i, a second local control module IOO2, and an n^th local control module 100_n. Each local control module is connected to a network control module 102. The local control modules 100 are preferably connected to the network control module 102 wirelessly, but may be connected by a wired connection.

Each local control module 100 receives signals locally from one or more local devices of a user.

Local control module 10O2 receives a signal from a single sensor 104i of a user. The local control module 100i and the sensor 104_a may correspond to the local control module 22 and sensor device 26 respectively of Figure 2.

Local control module IOO2 receives signals from a sensor device1042c, which is itself connected to two additional local sensor devices 1042a and 1042b. The local sensor devices 1042a and 1042b may each have a connection to the local sensor device 1042c. This connection may, for example, be a wireless connection such as a Bluetooth® connection or may be a wired connection. For example the sensor device 1042c may be a GPS equipped device, and the sensors devices 1042a and 1042b may be, respectively, a biometric sensor device and an accelerometer sensor device, such as for detecting fall. In examples the GPS device may simply be a device for communicating to the local control module. In examples the GPS device may simply track GPS data to give a position of the user, with other data such as to detect speed of movement, acceleration etc. being detected by other sensor devices.

Local control module 100_n receives signals from a sensor device104_na and from a sensor device 104_nb. In this example both sensor devices of the user are equipped to transmit to the local control module 100_n.

In examples, the local control module may be provided by the GPS device associated with the user, with this GPS device transmitting to a network control module.

In examples, the local control module may be a mobile telephone device of the user.

The capture of data can be used to provide for monitoring of the user to provide care to the user.

Alarm conditions can be used to give an early indication of a condition. By receiving an early indication, an early treatment can be dispensed.

For example, a higher frequency of attendance to the bathroom may indicate an oncoming condition, and this can generate an appropriate alarm.

If an alarm is generated, a care worker can be despatched to the home of the user to check the user. Conditions which can be treated by, for example the drinking of more water can be identified and treated early.

The network control module 102 may be associated with a central control centre, at which alarms are processed to dispatch appropriate are workers or medical support.

The architecture as described may utilise a 3^rd party sensor device used by the user 2, such as a Garmin® device, FitBit® device etc. An integration platform may be provided in order to utilise the data provided by such devices in the system disclosed herein.

The processor 50 may implement an algorithm to integrate the data received from a device 26. Where the device 26 is a device such as a Garmin® device, FitBit® device etc., the processor may integrate data in a proprietary format.

As discussed above, the architecture and process as described may user a single sensor associated with a user, or multiple sensors. Preferably one user devices connects to the local control module, or one user device is the local control module. If the user has more than one sensor device, preferably only one of these devices is the local control module or communicate with the local control module, and the other sensors communicate with that device.

The architecture can addition incorporate sensors which are not linked to the user, but are linked to the user’s living accommodation. For example a sensor which can detected use of the fridge in the living accommodation. The architecture can thus integrate sensor information from points within the living accommodation. Such sensors may have a static location (such as being able to identify whether a fridge door is pended or closed) or may have a dynamic location (such as being able to identify the location of an object which is moved around the living accommodation, as well as its status).

In the example described above with reference to Figure 2, it is described that a sensor 26 provided on the user 2 can provide tracking information such as location, together with providing biometric sensing information. An identity of the user may also be recorded by this device.

In an alternative, as shown in Figure 2, there may be provided a number of static sensing devices. In Figure 2, there is a shown a static sensing device 11 located in the living room 12, a static sensing device 15 located in the bathroom 14, a static sensing device 17 located in the kitchen 16, a static sensing device 21 located in the hallway 20, and a static sensing device 19 located in the bedroom 18. The static sensing device 19 is positioned within a smoke detector 25. The static sensors may be located within anywhere in a room, such as in a corner of a room. The static sensors may be provided, as sensor 19, in another device. Thus the sensor technology may be standalone or built into other sensors.

Such static sensors can detected the movement, pace, gait, geo-location etc. of a user, without the user having to wear a device to do so.

Such static sensors can be connected to the local control module 22.

A user may then additionally have associated therewith sensors incorporated in wearable or non-wearable technology, which may for example be used to detect heart rate, respiratory rate, temperature, blood pressure medication compliance etc.

Thus the sensor 26 may detect information associated with the user, in combination with one or more static sensors provided in the environment. The sensor 26 may be termed a user sensor, and a sensor such as sensor 11 may be termed an environment sensor. A user sensor is attached to a user, whereas an environment sensor is attached within the environment, but detects characteristics of the user.

The local control module 22 may integrate a system comprising such user sensors and such environment sensors. In this way, predictive power for early detection of a clinical event is provided.

A sensor, such as environment sensor 11, provide din a fixed location may be configured to enable the identification of the user 2. This sensor may utilise silhouette based recognition technology or some other form of identifier to ensure only a particular person is monitored, or to identify the person being monitored.

In general, a sensor may be provided to provide sensed data regarding the environment. This may be whether a fridge door is open or closed. This may be the temperature of the environment, or a room within the environment.

In general, sensors can detect one or more of: position of a user; pace or movement of a user; behaviours of a user; use of a bathroom by a user; use of a fridge by a user; sleeping patterns of a user; heart rate of a user; respiratory rate of a user;

pulse rate of a user.

The architecture allows these to be integrated.

The invention has been described by way of particular examples and embodiments, and is not limited to the specifics of any example or embodiment given. Furthermore different examples or embodiments may be generally combined in different ways.

Claims

1. A system for monitoring a user status, comprising:

a location device for the user;

a sensor device for the user; and a controller for integrating data of the location device and the sensor device.

2. The system of claim 1 wherein the location device is a device provided within a user environment.

3. The system of claim 1 or claim 2 wherein the location device further identifies the user.

4. The system of any preceding claim wherein the location device is statically positioned within the user environment.

5. The system of any one of claims 1 to 3 wherein the sensor device is associated with the user.

6. The system of claim 4 wherein the sensor device is fitted to the user.

7. The system of any preceding claim wherein the sensor device is a proprietary device.

8. A system for monitoring a user status within an environment, comprising:

a memory for storing location data for the environment;

a location device for providing location data of the user within the environment;

a processor for mapping the user location to an area within the environment in dependence on the stored location data for the environment and the location data of the user.

9. The system of claim 8 wherein the location device is statically positioned within the environment.

10. A system for monitoring a user status within an environment, comprising:

a location device for providing location data of the user within the environment; a sensor device for providing sensed data associated with the user; and a processor for integrating the location data and the sensed data.

11. The system of claim 10 wherein the location device is statically positioned within the environment.

12. The system of claim 10 or claim 11 wherein the sensor device is dynamically positioned within the environment.

13. The system of claim 12 wherein the sensor device is attached to the user.

14. The system of any one of claims 10 to 13 wherein the sensor device determines biometric data of the user.

15. The system of any one of claims 10 to 14 further comprising a sensor device for providing sensed data associated with the environment.

16. A system for monitoring a user status within an environment, comprising:

a location device for providing location data of the user within the environment; a biometric sensor device for providing biometric sensed data associated with the user;

an environment sensor for providing environment sensed data within the environment; and a processor for integrating the location data, the biometric sensed data, and the environment data.

17. A system according to claim 6 further comprising a processor for analysing the data using predictive analytics in order to predict the increased risk of an event and alert a caregiver.