ABNORMAL MOTION DETECTOR AND MONITOR

• US 20150190085A1
• Filed: 01/16/2015
• Published: 07/09/2015
• Est. Priority Date: 05/18/2007
• Status: Active Grant

• Current Assignee: Smart Monitor Corp.
• Original Assignee: Smart Monitor Corp.
• Inventors: Nathan, Vaidhi, Gope, Chandan, Nathan, Anoo
• Granted Patent: US 10,595,766 B2
• 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 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1101 Detecting tremor
• CPC Class Codes: A61B 5/1107 Measuring contraction of parts of the body, e.g. organ, muscle apparatus for measuring work or force in general G01L
• 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 5/1122 of movement trajectories
• 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 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6891 Furniture
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

In an embodiment, a seizure monitor provides intelligent epilepsy seizure detection, monitoring, and alerting for epilepsy patients or people with seizures. In an embodiment, the seizure monitor may be a wearable, non-intrusive, passive monitoring device that does not require any insertion or ingestion into the human body. In an embodiment, the seizure monitor may include several output options for outputting the accelerometer/gyro or other motion sensor data and video data, so that the data may be immediately validated and/or remotely viewed. The device alerts are communicated to the outside care givers via wireless or wired medium. The device may also support recording of accelerometer or other motion sensor data and video data, which can be reviewed later for further analysis and/or diagnosis. The device and invention is also used and applicable for other body motion disorders or detection and diagnostics.

Claims

1. A method to detect a seizure, comprising:

electronically collecting motion data produced by a sensor physically associated with a person;

determining from the collected motion data via a processor communicatively coupled to the sensor at least one value characterizing the motion data;

comparing the at least one value characterizing the motion data to at least one corresponding value characterizing abnormal motion;

determining whether the seizure has occurred based on the comparing; and

activating a seizure alert signal when the determined seizure has occurred.

2. The method of claim 1, wherein the at least one corresponding value characterizing abnormal motion includes a threshold value for a frequency of oscillation of a body part.

3. The method of claim 1, wherein the at least one corresponding value characterizing abnormal motion includes a motion pattern model characterizing a seizure, and wherein the determining includes determining whether the motion data matches the motion pattern model characterizing the seizure within a predetermined tolerance.

4. The method of claim 1, further comprising:

retrieving historic motion data previously stored in a memory; and

applying the historic motion data to generate the at least one corresponding value characterizing abnormal motion.

5. The method of claim 1, wherein the motion data is video data, the method further comprising:

identifying at least one distinguishable feature in the video data;

determining locations of the distinguishable feature across multiple picture frames of the video data; and

determining a motion path for the distinguishable feature, the motion path for the distinguishable feature being the at least one value characterizing the motion, wherein the comparing includes comparing the motion path for the distinguishable feature to a motion path characterizing the seizure, and wherein the determining whether a seizure has occurred includes determining whether the motion path for the distinguishable feature matches the motion path characterizing the seizure within a predetermined tolerance.

6. The method of claim 1, wherein the motion data represents oscillatory motion.

7. The method of claim 6, wherein the at least one value characterizing the motion data represents a frequency of oscillation, and wherein the at least one corresponding value characterizing abnormal motion represents a predetermined threshold, and wherein determining whether the seizure has occurred is based on the frequency of oscillation being higher than the predetermined threshold.

8. The method of claim 7, wherein the sensor is an accelerometer.

9. The method of claim 7, wherein the sensor is a gyro sensor.

10. The method of claim 7, wherein the sensor is a combination of one or more accelerometers and gyro sensors.

11. The method of claim 7, the motion data is derived from an optical flow or feature point analysis.

12. The method of claim 7, wherein the motion data is derived from a plurality of motion vectors.

13. The method of claim 1, wherein activating the seizure alert includes wirelessly sending a message to a remote device.

14. The method of claim 13, wherein the message includes seizure data from a current seizure.

15. The method of claim 13, wherein the message includes a current location of the person.

16. The method of claim 1, wherein the sensor is arranged in a body-worn device and wherein the processor is in a smartphone.

17. The method of claim 1, wherein the sensor is arranged in a body-worn device and wherein the processor is in a remote Internet-connected computing device.

18. The method of claim 1, wherein the communicative coupling between the sensor and the processor includes passing at least some of the motion data through a smart phone.

19. The method of claim 1, further comprising:

attaching the sensor around a wrist of the person.

20. The method of claim 1, further comprising:

attaching the sensor as an accessory to apparel worn by the person.

21. A method to detect abnormal motion in a person, comprising:

producing first sensor data at a first time, the first sensor data representing a first physical state of a portion of a body of a person, the first sensor data produced by at least one of an accelerometer, a gyro sensor, and a camera;

producing second sensor data at a second time, the second time after the first time, the second sensor data representing a second physical state of the portion of the body of the person, the second sensor data produced by the at least one of the accelerometer, the gyro sensor, and the camera;

mathematically associating the first sensor data with the second sensor data to obtain a motion value;

comparing the motion value to an abnormal motion threshold; and

activating an abnormal motion signal based on the comparing.

22. The method of claim 21, wherein the first sensor data and the second sensor data are images of the portion of the body.

23. The method of claim 21, wherein the first sensor data and the second sensor data are digital values representing at least one of amplitude, frequency, and acceleration.

24. The method of claim 21, further comprising:

repeating, a plurality of times, the acts of producing first and second sensor data, the act of mathematically associating the first and second sensor data, and the act of comparing;

tracking at least one point of the portion of the body of the person; and

deriving oscillation information as the motion value.

25. The method of claim 21, further comprising:

repeating, a plurality of times, the acts of producing first and second sensor data, the act of mathematically associating the first and second sensor data, and the act of comparing;

computing at least one motion vector;

generating a signature representing the at least one motion vector, the signature being the motion value.

26. The method of claim 21, further comprising:

wirelessly communicating the abnormal motion signal to a remote device.

27. The method of claim 21, further comprising:

physically attaching the at least one of the accelerometer, the gyro sensor, and the camera to either the person or furniture the person is in contact with.

28. The method of claim 21, further comprising:

physically attaching the at least one of the accelerometer, the gyro sensor, and the camera to furniture.

29. The method of claim 21, wherein the abnormal motion is indicative of a motion disorder.

30. The method of claim 29, wherein the motion disorder is one of epilepsy, ataxia, dystonia, dyskinesia, Parkinson'"'"'s disease, chorea, tremor, tics, myoclonus, and restless leg syndrome.

31. A method, comprising:

capturing a stream of sensor data representing motion of a portion of a body of a person;

communicating at least some of the sensor data stream to a processing unit;

identifying, with the processing unit, a plurality of motion data values indicating motion of the portion of the body;

comparing at least some of the plurality of motion data values to an abnormal motion threshold; and

asserting an abnormal motion signal when the abnormal motion threshold is crossed.

32. The method of claim 31, wherein the stream of sensor data is produced by a device physically coupled to the person and wherein the processing unit is a central processor of a cell phone device.

33. The method of claim 31, wherein the stream of sensor data is produced by a device in proximity the person and wherein the processing unit is an Internet-connected computing device.

34. The method of claim 31, wherein the stream of sensor data is produced by a wrist-worn device and the communicating at least some of the sensor data stream is via a wireless communication circuit.

35. The method of claim 31, wherein the communicating includes passing data via a Bluetooth communication circuit.

36. The method of claim 31, wherein the communicating includes passing data via a cellular communication circuit.

37. The method of claim 31, further comprising:

tracking a path of motion of the portion of the body of the person; and

identifying oscillations of the portion of the body of the person.

38. The method of claim 31, wherein the abnormal motion threshold represents at least one of a frequency, an amplitude, an acceleration, and a change of position.

39. The method of claim 31, further comprising:

updating the abnormal motion threshold based on at least some of the plurality of motion data values.

40. The method of claim 31, further comprising:

activating at least one of a remote visual output and a remote audio output based on the abnormal motion signal.

41. The method of claim 31, wherein the abnormal motion threshold is representative of a motion disorder.

42. The method of claim 31, further comprising:

accepting a user input to clear an asserted abnormal motion signal.