Activity detection and analytics

Patent metadata

Field	Value
Publication number	US-2015127298-A1
Application number	US-201414148571-A
Country	US
Kind code	A1
Filing date	Jan 6, 2014
Priority date	Nov 4, 2013
Publication date	May 7, 2015
Grant date	—

Abstract

A method and system for activity detection and analytics are disclosed. The method comprises determining a context and providing the determined context and one or more outputs from at least one sensor to an analytics engine to provide analytics results. The system includes at least one sensor and a processing system coupled to the at least one sensor, wherein the processing system includes an analytics engine that is configured to receive a determined context and one or more outputs from at least one sensor to provide analytics results.

First claim

1 . A computer implemented method comprising:
determining a context; and providing the determined context and one or more outputs from at least one sensor to an analytics engine to provide analytics results.

2 . The computer implemented method of claim 1 , wherein the context is determined by an activity recognition engine.

3 . The computer implemented method of claim 2 , wherein the activity recognition engine receives one or more outputs from at least one sensor.

4 . The computer implemented method of claim 3 , wherein the analytics engine and the activity recognition engine receive the one or more outputs from the same sensor.

5 . The computer implemented method of claim 3 , wherein the analytics engine and the activity recognition engine receive the one or more outputs from different sensors.

6 . The computer implemented method of claim 2 , further comprising:
classifying an activity by the activity recognition engine based on the determined context to provide further analytics results.

7 . The computer implemented method of claim 6 , wherein the classified activity includes any of biking, running, walking, driving, standing, sitting, and sleeping.

8 . The computer implemented method of claim 6 , wherein the analytics engine utilizes a change in threshold, frequency, and cut-off frequency to provide the analytics results.

9 . The computer implemented method of claim 8 , wherein the threshold is dynamically adjusted based upon the classified activity.

10 . The computer implemented method of claim 6 , wherein the analytics results are utilized by the activity recognition engine to determine the classified activity.

11 . The computer implemented method of claim 1 , wherein the at least one sensor is any of an accelerometer, gyroscope, pressure sensor, and other sensor.

12 . The computer implemented method of claim 1 , wherein the analytics engine is comprised of a software component and a hardware component.

13 . The computer implemented method of claim 1 , wherein the analytics results include any of steps per minute (SPM), number of steps, distance, speed, stride length, energy, calories, heart rate, and exercise counts.

14 . The computer implemented method of claim 2 , wherein any of the activity recognition engine and the analytics engine are capable of receiving user input and instructions.

15 . The computer implemented method of claim 6 , wherein a power management unit is controlled by any of the activity recognition engine, the determined context, the classified activity, the analytics engine, and the analytics results.

16 . The computer implemented method of claim 6 , wherein the at least one sensor is dynamically selected based on any of the activity recognition engine, the determined context, the classified activity, the analytics engine, and the analytics results.

17 . A device comprising:
at least one sensor; a processing system coupled to the at least one sensor, wherein the processing system includes an analytics engine that is configured to receive a determined context and one or more outputs from at least one sensor to provide analytics results.

18 . The device of claim 17 , wherein the processing system further comprises an activity recognition engine to determine the context and to provide the determined context to the analytics engine.

19 . The device of claim 18 , wherein the activity recognition engine receives one or more outputs from at least one sensor.

20 . The device of claim 19 , wherein the analytics engine and the activity recognition engine receive the one or more outputs from the same sensor.

21 . The device of claim 19 , wherein the analytics engine and the activity recognition engine receives the one or more outputs from different sensors.

22 . The device of claim 18 , wherein the activity recognition engine classifies an activity based on the determined context to provide further analytic results.

23 . The device of claim 22 , wherein the classified activity includes any of biking, running, walking, driving, standing, sitting, and sleeping.

24 . The device of claim 17 , wherein the analytics engine utilizes a change in threshold, frequency, and cut-off frequency to provide the analytics results.

25 . The device of claim 24 , wherein the threshold is dynamically adjusted based upon the classified activity.

26 . The device of claim 22 , wherein the analytics results are utilized by the activity recognition engine to determine the classified activity.

27 . The device of claim 17 , wherein the at least one sensor is any of an accelerometer, gyroscope, pressure sensor, and other sensor.

28 . The device of claim 17 , wherein the analytics engine is comprised of a software component and a hardware component.

29 . The device of claim 17 , wherein the analytics results include any of steps per minute (SPM), number of steps, distance, speed, stride length, energy, calories, heart rate, and exercise counts.

30 . The device of claim 18 , wherein any of the activity recognition engine and the analytics engine are capable of receiving user input and instructions.

31 . The device of claim 22 , further comprising a power management unit that is controlled by any of the activity recognition engine, the determined context, the classified activity, the analytics engine, and the analytics results.

32 . The device of claim 22 , wherein the at least one sensor is dynamically selected based on any of the activity recognition engine, the determined context, the classified activity, the analytics engine, and the analytics results.

Assignees

• Invensense Inc

Inventors

• Gangumalla Vamshi R
• Katingari Karthik

Classifications

• G01C22/006 Primary

  Pedometers · CPC title

• G06Q10/10

  Office automation; Time management · CPC title

• A61B5/1123

  Discriminating type of movement, e.g. walking or running (A61B5/1116, A61B5/112 take precedence) · CPC title

• A61B5/112

  Gait analysis · CPC title

• A61B5/02438

  with portable devices, e.g. worn by the patient · CPC title