CROWDSOURCING ACTIVITY DETECTION FOR GROUP ACTIVITIES

• US 20170094450A1
• Filed: 09/22/2016
• Published: 03/30/2017
• Est. Priority Date: 09/30/2015
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: TU, Xiaoyuan, KANDANGATH, Anil K.
• Application Number:
  US15/273,054
• Publication Number:
  US 20170094450A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• 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 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• 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 5/7275 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 5/749 Voice-controlled interfaces
• CPC Class Codes: G01S 19/13 Receivers
• CPC Class Codes: G01S 19/19 Sporting applications
• CPC Class Codes: G08C 17/02 using a radio link
• CPC Class Codes: G10L 15/00 Speech recognition G10L17/00 takes precedence
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: H04B 1/385 Transceivers carried on the body, e.g. in helmets
• CPC Class Codes: H04B 2001/3861 carried in a hand or on fingers
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/026 using orientation information, e.g. compass
• CPC Class Codes: H04W 4/027 : using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/80 : Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks
• CPC Class Codes: H04W 88/04 : adapted for relaying to or from another terminal or user

Abstract

In one aspect, the present disclosure relates to a method, including determining, by a wearable device, predicting, by a first wearable device, a first predicted activity of the first user using motion data received by motion sensors of the first wearable device; estimating, a first confidence level of the first predicted activity; receiving, by the first wearable device over a wireless communication channel from a second wearable device, a second predicted activity of a second user and a second confidence level of the second predicted activity; comparing the first predicted activity and the first confidence level with the second predicted activity and the second confidence level; and determining a first activity classification for the first user to be the second predicted activity when a second average confidence level associated with the second predicted activity is greater than a first average confidence level associated with the first predicted activity.

Claims

1. A method comprising:

predicting, by a first wearable device, a first predicted activity of the first user using motion data received by motion sensors of the first wearable device;

estimating, by the first wearable device, a first confidence level of the first predicted activity;

receiving, by the first wearable device over a wireless communication channel from a second wearable device, a second predicted activity of a second user and a second confidence level of the second predicted activity;

comparing, by the first wearable device, the first predicted activity and the first confidence level with the second predicted activity and the second confidence level; and

determining, by the first wearable device, a first activity classification for the first user to be the second predicted activity when a second average confidence level associated with the second predicted activity is greater than a first average confidence level associated with the first predicted activity.

2. The method of claim 1, wherein predicting is triggered by a first user action on a display of the first wearable device.

3. The method of claim 1, wherein predicting is triggered by receiving a voice command of the first user.

4. The method of claim 1, wherein motion data comprises accelerometer data.

5. The method of claim 1, wherein the second predicted activity is received from the second wireless device via one or more relaying devices.

6. The method of claim 5, wherein at least one of the relaying devices is a companion device.

7. The method of claim 1, wherein the predicting further uses positioning data received from a companion device.

8. The method of claim 7, wherein the positioning data is GPS data.

9. The method of claim 1, wherein the first confidence level is adjusted prior to comparing the first confidence level and second confidence level.

10. The method of claim 1, further comprising:

receiving, by the first wearable device over a wireless communication channel from a plurality of additional wearable devices, a plurality of predicted activities of a plurality of additional users and a corresponding plurality of confidence levels of the plurality of predicted activities;

wherein comparing further comprises comparing, by the first wearable device, the first predicted activity and the first confidence level with at least some of the plurality of predicted activities and the corresponding confidence levels; and

wherein determining the first activity classification is further based on the comparison of the first predicted activity and the first confidence level with at least some of the plurality of predicted activities and the corresponding confidence levels.

11. An apparatus comprising:

a display;

one or more motion sensors;

a wireless interface; and

at least one processor, wherein the at least one processor is configured to;

receive motion data from the one or more motion sensors;

predict a first predicted activity of the user of the apparatus using the received motion data;

estimate a first confidence level of the first predicted activity;

receive, via the wireless interface, a second predicted activity of a user of a second apparatus and a second confidence level of the second predicted activity;

compare the first predicted activity and the first confidence level with the second predicted activity and the second confidence level; and

determine a first activity classification for the first user to be the second predicted activity when a second average confidence level associated with the second predicted activity is greater than a first average confidence level associated with the first predicted activity.

12. The apparatus of claim 11, wherein the apparatus is a wrist-worn device.

13. The apparatus of claim 11, wherein the wireless interface is a mesh wireless network interface.

14. The apparatus of claim 11, wherein the wireless interface is a IEEE 802.15 interface.

15. The apparatus of claim 11, wherein the wireless interface connects to a companion device and wherein the companion device is a smartphone.

16. The apparatus of claim 11, further comprising a heart rate sensor, wherein the first predicted activity of the user is further predicted based on heart rate data of the user of the apparatus received by the at least one processor from the heart rate sensor.

17. The apparatus of claim 11, further comprising a microphone, wherein the at least one processor is further configured to trigger prediction after receiving a voice command of the user of the apparatus received by the at least one processor from the microphone.

18. The apparatus of claim 11, further comprising one or more of a barometer and altimeter, wherein the first predicted activity of the user is further predicted based on altitude data received by the at least one processor from the one or more of a barometer and altimeter.

19. The apparatus of claim 11, wherein at least one of the one or more motion sensors is a gyroscope.

20. The apparatus of claim 11, further comprising a GPS receiver, wherein the first predicted activity of the user is further predicted based on positioning data received by the at least one processor from the GPS receiver.