SIGNATURE BASED MONITORING SYSTEMS AND METHODS

• US 20180160943A1
• Filed: 12/10/2014
• Published: 06/14/2018
• Est. Priority Date: 12/10/2013
• Status: Abandoned Application

• Current Assignee: 4Iiii Innovations Inc.
• Original Assignee: 4Iiii Innovations Inc.
• Inventors: Fyfe, Kiplinig, Milne, Caitlin, Zacher, Darren, Williams, Tom, Brilz, Victoria, Bakshi, Vipin
• Application Number:
  US15/102,262
• Publication Number:
  US 20180160943A1
• Time in Patent Office: Days
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2503/40 Animals
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0228 using calibration standards
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/083 Measuring rate of metabolism by using breath test, e.g. measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 : Gait analysis
• CPC Class Codes: A61B 5/1123 : Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/14532 : for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 : for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/369 : Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 : Electromyography [EMG]
• CPC Class Codes: A61B 5/443 : Evaluating skin constituents, e.g. elastin, melanin, water
• CPC Class Codes: A61B 5/4875 : Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A63B 2024/0012 : Comparing movements or motion sequences with a registered reference
• CPC Class Codes: A63B 2024/0025 : Tracking the path or location of one or more users, e.g. players of a game
• CPC Class Codes: A63B 2024/0065 : Evaluating the fitness, e.g. fitness level or fitness index
• CPC Class Codes: A63B 2024/0068 : Comparison to target or threshold, previous performance or not real time comparison to other individuals
• CPC Class Codes: A63B 2024/0071 : Distinction between different activities, movements, or kind of sports performed
• CPC Class Codes: A63B 2024/0093 : the load of the exercise apparatus being controlled by performance parameters, e.g. distance or speed
• CPC Class Codes: A63B 2071/065 : Visualisation of specific exercise parameters
• CPC Class Codes: A63B 22/0605 : performing a circular movement, e.g. ergometers
• CPC Class Codes: A63B 2220/05 : Image processing for measuring physical parameters
• CPC Class Codes: A63B 2220/30 : Speed
• CPC Class Codes: A63B 2220/40 : Acceleration
• CPC Class Codes: A63B 2220/72 : Temperature
• CPC Class Codes: A63B 2220/73 : Altitude
• CPC Class Codes: A63B 2220/74 : Atmospheric pressure
• CPC Class Codes: A63B 2220/78 : Surface covering conditions, e.g. of a road surface
• CPC Class Codes: A63B 2220/806 : Video cameras
• CPC Class Codes: A63B 2220/833 : Sensors arranged on the exercise apparatus or sports implement
• CPC Class Codes: A63B 2220/836 : Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/50 : Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/06 : heartbeat rate only
• CPC Class Codes: A63B 2230/10 : electroencephalographic signals
• CPC Class Codes: A63B 2230/202 : glucose
• CPC Class Codes: A63B 2230/40 : respiratory characteristics
• CPC Class Codes: A63B 2230/50 : temperature
• CPC Class Codes: A63B 2230/70 : body fat
• CPC Class Codes: A63B 2230/75 : calorie expenditure
• CPC Class Codes: A63B 24/0003 : Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
• CPC Class Codes: A63B 24/0006 : Computerised comparison for qualitative assessment of motion sequences or the course of a movement
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 71/06 : Indicating or scoring devices for games or players , or for other sports activities
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A system and a method determine an activity of a user. Sensor data is collected from a sensor within a pod worn by the user. The sensor data is matched to a signature definition corresponding to a known activity and the sensor. When the sensor data matches the signature definition, the activity of the user is determined. Sensed data, signature data, and/or matched signature data may be communicated to and from external devices. Signatures may be learned for known activities.

Claims

1. A method for determining an activity of a user, comprising:

collecting sensor data from a plurality of sensors associated with the user; and

matching, using a digital processor, the sensor data to a signature definition to determine whether the user is performing the activity, the signature definition correlated to expected sensor data from the plurality of sensors and corresponding to the activity.

2. The method of claim 1, wherein at least one of the plurality of sensors is located within a first pod configured to detect movement of the user.

3. The method of claim 2, wherein the sensors are selected from the group including:

an accelerometer, a microphone, a perspiration detector, a magnetic compass, a temperature sensor, an inclination sensor, a gyroscope, an oxygen sensor, an altimeter, a short-range radar, a short range sonar, a short range laser, a pressure sensor, an image sensor, an ambient light sensor, a Global Navigation Satellite System (GNSS) receiver, an electromyogram, a signal strength detector for one or more wireless signals, an electroencephalogram, a respiration sensor, a VO2 sensor, photoplethysmograph, and an RFID receiver.

4. The method of claim 3, the digital processor being configured with the first pod.

5. The method of claim 4, further comprising receiving the signature definition from a server, wherein the signature definition is one of a plurality of signature definitions stored within a signature database and based upon expected activity of the user.

6. The method of claim 4, further comprising receiving the signature definition from a second pod associated with a different user.

7. The method of claim 4, further comprising receiving the signature definition from a second pod associated with the user.

8. The method of claim 4, further comprising sending the signature definition to a second pod associated with a different user.

9. The method of claim 2, the digital processor being configured with a server communicatively coupled with the first pod to receive the sensor data.

10. The method of claim 1, further comprising communicating from the digital processor to a user interface device that interacts with the user.

11. The method of claim 10, wherein said communicating comprises utilizing wireless communications.

12. The method of claim 10, wherein the user interface device comprises at least one of multi-colored LEDs, a vibrator motor, and an audio codec.

13. The method of claim 1, further comprising generating an alarm based upon a matched signature definition and a predefined threshold.

14. The method of claim 1, wherein the signature definition comprises (a) a definition of an expected signal from at least one of the sensors when the user performs the activity, (b) a state indicative of the activity, (c) a magnitude of the activity, and (d) a truth score that indicates the accuracy of the magnitude.

15. The method of claim 14, further comprising determining a state of the user based upon a history of matched signature definitions.

16. The method of claim 15, further comprising determining confidence in the state based upon matched signature definition and said history.

17. The method of claim 15, wherein the state comprises one or more of:

position, orientation, calories burned, work, level of hydration, mood, level of fatigue, heart rate, heart rate variability, skin temperature, gait type, static position, crowd flow.

18. The method of claim 1, further comprising generating a match score indicative of confidence in said matching.

19. The method of claim 18, further comprising determining matched signature definition from a plurality of signature definitions based upon the match score.

20. The method of claim 1, the sensor sensing one or more of leg motion, walking, running, skiing, skating, arm motion, gestures, heart rate, heart rate variability, wrist motion, crawling, respiration, brain waves, equipment movement, wind speed, swimming strokes, bicycle velocity, bicycle cadence, and blood glucose level.

21. The method of claim 1, further comprising wirelessly sending information of the matched signature definition to a third party application running on a remote computer.

22. The method of claim 21, wherein the third party application utilizes an application programming interface (API) associated with the first pod that allows the third party application to communicate with the first pod to receive the information.

23. The method of claim 1, further comprising generating a map within the first pod based upon matched signature definition and previously matched signature definitions, the map indicating areas navigated by the first pod.

24. The method of claim 23, further comprising sending the map to a second pod to indicate, at the second pod, navigable space to a user thereof.

25. The method of claim 1, further comprising receiving a map from a server associated with an area proximate the first pod to indicate, at the first pod, navigable space within the area.

26. The method of claim 23 or 25, further comprising using the map to validate a location of the user, determined within the first pod based upon matched signature definition.

27. The method of claim 1, further comprising calibrating the signature definition within the first pod based upon a wirelessly received direct truth measurement.

28. The method of claim 1, further comprising validating matched signature definition based upon a history of recent signature definition matches.

29. A pod for determining activity of a user, comprising:

a plurality of sensors capable of generating sensor data based upon sensed characteristics of the user;

a memory capable of storing a signature definition based upon a known activity;

a processor coupled with the memory;

a match routine, comprising machine readable instructions stored within the memory and executed by the processor, capable of matching the sensor data with the signature definition to determine the activity; and

a transceiver capable of communicating the activity to an external device.

30. The pod of claim 29, the sensor comprising one or more of an accelerometer, a gyro, a GNSS, a pressure sensor, a light sensor, and a microphone.

31. The pod of claim 29, further comprising an attachment device for physically coupling the pod to a part of a user'"'"'s body.

32. The pod of claim 29, further comprising a wireless transceiver for communicating with other pods.

33. A system for determining when a user performs an activity, comprising:

a first pod configured with the user and having;

a first sensor for generating first sensor data indicative of characteristics of the user; and

a first transceiver for wirelessly transmitting the first sensor data;

a server comprising;

a processor;

a memory;

a second transceiver for receiving the first sensor data;

a first signature definition stored within the memory and corresponding to the activity and the first sensor; and

an algorithm having machine readable instructions that, when executed by the processor, are capable of matching the first sensor data to the first signature definition to determine if the user is performing the activity.

34. The system of claim 33, further comprising:

a second pod configured with the user, the second pod comprising;

a second sensor for generating second sensor data indicative of characteristics of the user; and

a third transceiver for wirelessly transmitting the second sensor data; and

the server further comprising a second signature definition stored within the memory and corresponding to the activity and the second sensor;

wherein the second transceiver is configured to receive the second sensor data and wherein the algorithm further comprises machine readable instructions that, when executed by the processor, are capable of matching the second sensor data to the second signature definition to determine if the user is performing the activity.