Activity Recognition Using Accelerometer Data

Patent metadata

Field	Value
Publication number	US-2016354014-A1
Application number	US-201515117943-A
Country	US
Kind code	A1
Filing date	Feb 12, 2015
Priority date	Feb 14, 2014
Publication date	Dec 8, 2016
Grant date	—

Abstract

A device for recognizing activity of an object. The device comprises a housing configured to be attached to the object and a processing unit disposed in the housing comprising a processor and a movement sensor. The movement sensor measures a signal related to movement of the object during a time window. The processor assigns at least one preliminary activity label to the time window based on at least one numerical descriptor computed from the signal. The processor then determines whether to perform additional analysis dependent upon at least the preliminary activity label. The processor then assigns a final activity label to the time window.

First claim

What is claimed is:

1 . A device for recognizing activity of an object, the device comprising:
a housing configured to be attached to the object; a processing unit disposed in the housing comprising a processor and a movement sensor; wherein the movement sensor measures a signal related to movement of the object during a time window; wherein the processor assigns at least one preliminary activity label to the time window based on at least one numerical descriptor computed from the measured signal; wherein the processor determines whether to perform additional analysis dependent upon at least the preliminary activity label; and and wherein the processor assigns a final activity label to the time window.

2 . The device of claim 1 , wherein if the processor does not perform additional analysis, the final activity label is the same as the preliminary activity label.

3 . The device of claim 1 , wherein the processor assigns the final activity label to the time window based on the preliminary activity label for the time window and at least one final activity label for at least one prior time window.

4 . The device of claim 1 , wherein the movement sensor is at least one of: an accelerometer, gyroscope, piezoelectric vibration sensor, geographical positioning sensor and a magnetic switch.

5 . The device of claim 1 , wherein the processing unit further comprises a location module.

6 . The device of claim 5 , wherein the processor is configured to estimate a location of the object using at least both of the signal from the movement sensor and data from the location module.

7 . The device of claim 1 , further comprising an emergency notification component.

8 . The device of claim 1 , wherein the device is an electronic monitoring bracelet.

9 . The device of claim 1 , wherein the movement sensor collects data at a rate in the range of 1 (one) Hz to 20 (twenty) Hz.

10 . The device of claim 1 , wherein the length of the time window is in the range of 2 (two) seconds to 10 (ten) seconds and contains a number of samples in the range of 8 to 1024 samples.

11 . The device of claim 1 , wherein at least two numerical descriptors are computed from the signal.

12 . The device of claim 2 , wherein the device transmits an alarm signal to a central monitoring system upon determination of a particular final activity label.

13 . The device of claim 1 , wherein the processor uses a decision tree algorithm to assign the preliminary activity label to the time window.

14 . The device of claim 1 , wherein the possible activity labels include at least one of: walking, driving, sleeping, sitting, running, eating, and bicycling.

15 . The device of claim 1 , wherein the performing of additional analysis is also dependent on a device state.

16 . The device of claim 1 , wherein the additional analysis includes computational escalation including at least one of the following algorithm techniques: neural networks, Bayesian analysis, random forest, support vector machine, and multi-level decision tree.

17 . The device of claim 1 , wherein the processor determines to perform additional analysis when the preliminary activity label is a commonly confused preliminary activity.

18 . A device for recognizing activity of an object, the device comprising:
a housing configured to be attached to the object; a processing unit disposed in the housing comprising a processor and a movement sensor; wherein the movement sensor measures a signal related to movement of the object during a time window; wherein the processor assigns at least one preliminary activity label and confidence indicator to the time window based on at least one numerical descriptor computed from the measured signal; wherein the processor determines whether to perform additional analysis dependent upon at least the confidence indicator; and wherein the processor assigns a final activity label to the time window.

19 . The device of claim 18 , wherein if the processor does not perform additional analysis, the final activity label is the same as the preliminary activity label.

20 . The device of claim 18 , wherein the processor assigns a final activity label to the time window based on the preliminary activity label for the time window and at least one final activity label for at least one prior time window.

21 . The method of claim 18 , wherein if the confidence indicator is below a predefined threshold, the processor performs additional analysis.

22 . The method of claim 18 , wherein the processor assigns more than one preliminary activity label with, each preliminary activity label having a confidence indicator within a predefined margin of each other, the processor performs additional analysis.

23 . The method of claim 18 , wherein the processor adjusts the predefined margin over time.

24 . A method of recognizing activity of an object, the method comprising:
measuring, with a movement sensor attached to the object, a signal related to movement of the object during a time window; assigning, with a processor, at least one preliminary activity label to the time window based on at least one numerical descriptor computed from the measured signal; determining whether to perform additional analysis dependent upon at least the preliminary activity label; and assigning a final activity label to the time window.

25 . The method of claim 24 , further comprising assigning a final activity label to the time window based on the preliminary activity label and at least one final activity label for at least one prior time window.

26 . The method of claim 24 , wherein the processor uses a decision tree algorithm to assign the preliminary activity label to the time window.

27 . The method of claim 24 , wherein the possible activity labels include at least one of: walking, driving, sleeping, sitting, running, eating, and bicycling.

28 . The method of claim 24 , wherein the performing of additional analysis is also dependent on a device state.

29 . The method of claim 1 , wherein the additional analysis includes computational escalation including at least one of the following algorithm techniques: neural networks, Bayesian analysis, random forest, support vector machine, and multi-level decision tree.

30 . The method of claim 1 , further comprising determining to perform additional analysis when the preliminary activity label is a commonly confused preliminary activity.

31 . A method of recognizing activity of an object, the method comprising:
measuring, with a movement sensor attached to the object, a signal related to movement of the object during a time window; assigning, with a processor, at least one preliminary activity label to the time window based on at least one numerical descriptor computed from the measured signal; determining whether to perform additional analysis dependent upon at least the preliminary activity label; and assigning a final activity label to the time window.

32 . A device for recognizing activity of an object, the device comprising:
a housing configured to be attached to the object; a processing unit disposed in the housing comprising a communication unit and a movement sensor; wherein the movement sensor measures a signal related to movement of the object during

Assignees

• 3M Innovative Properties Co

Inventors

• Lobner Eric C
• Howard James W
• Moore Richard J
• Schumacher Jennifer F
• Stankiewicz Brian J

Classifications

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• A61B5/6828

  Leg · CPC title

• A61B5/1118 Primary

  Determining activity level · CPC title

• A61B2562/0219

  Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches · CPC title

• A61B5/7282

  Event detection, e.g. detecting unique waveforms indicative of a medical condition (cough events A61B5/0823; seizures A61B5/4094; sleep apnoea A61B5/4818) · CPC title