Abnormal motion detector and monitor

• US 8,075,499 B2
• Filed: 05/19/2008
• Issued: 12/13/2011
• Est. Priority Date: 05/18/2007
• Status: Active Grant

• Current Assignee: Smart Monitor Corp.
• Original Assignee: Vaidhi Nathan
• Inventors: Nathan, Vaidhi, Gope, Chanden, Nathan, Anoo
• Primary Examiner(s): HOEKSTRA, JEFFREY GERBEN
• Application Number:
  US12/154,085
• Publication Number:
  US 20090062696A1
• Time in Patent Office: 1,303 Days
• Field of Search: 600/587, 600/595, 702/127, 702141-154
• US Class Current: 600/587
• 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 and communicates to the outside care givers via wirelessly 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 body-worn portable device comprising:

a strap for strapping the body-worn portable device onto a person;

an input system for inputting detections parameters for a specific type of abnormal motion;

an accelerometer or gyro sensor for measuring motion data;

a memory storing settings for detecting the specific type of abnormal motion including at least characteristics of the specific type of abnormal motion;

an algorithm for analyzing the motion data measured, comparing the characteristics of the specific type of abnormal motion to the motion data measured, and determining whether to send an alert based on the comparing;

a processor that implements the algorithm and generates an indication that the abnormal motion may have occurred based on the algorithm;

a housing for enclosing the memory and the processor; and

a display being attached to the housing in a manner in which the settings for the specific type of abnormal motion or a status may be viewed,

the input system being attached to the housing in a manner in which settings for the specific type of abnormal motion may be entered by the person, and

wherein the specific type of abnormal motion includes a seizure.

2. The body-worn portable device of claim 1, the memory storing data indicating events that occurred and alerts that were sent.

3. The body-worn portable device of claim 1, the input system having at least

a first portion, which when manually activated, causes an alert to be sent;

and

a second portion, which when manually activated, causes a sending of an alert to be cancelled.

4. The body-worn portable device of claim 1

the strap straps the body-worn portable device onto a wrist of the body when worn on the body.

5. A body-worn portable device comprising:

a strap for strapping the body-worn portable device onto a person;

an input system for inputting detections parameters for a specific type of abnormal motion;

an accelerometer or gyro sensor for measuring motion data;

a memory storing settings for detecting the specific type of abnormal motion including at least characteristics of the specific type of abnormal motion;

an algorithm for analyzing the motion data measured, comparing the characteristics of the specific type of abnormal motion to the motion data measured, and determining whether to send an alert based on the comparing;

a processor that implements the algorithm and generates an indication that the abnormal motion may have occurred based on the algorithm;

a housing for enclosing the memory and the processor;

a display being attached to the housing in a manner in which the settings for the specific type of abnormal motion or a status may be viewed,

the input system being attached to the housing in a manner in which settings for the specific type of abnormal motion may be entered by the person;

a clock mounted within the housing; and

a display for displaying a time of day;

the clock being for determining a time of day to display on the display;

the input system having a portion via which the time can be changed and can be adjusted, and

wherein the specific type of abnormal motion includes a seizure.

6. A body-worn portable device comprising:

a strap for strapping the body-worn portable device onto a person;

an input system for inputting detections parameters for a specific type of abnormal motion;

an accelerometer or gyro sensor for measuring motion data;

a memory storing settings for detecting the specific type of abnormal motion including at least characteristics of the specific type of abnormal motion;

an algorithm for analyzing the motion data measured, comparing the characteristics of the specific type of abnormal motion to the motion data measured, and determining whether to send an alert based on the comparing;

a processor that implements the algorithm and generates an indication that the abnormal motion may have occurred based on the algorithm;

a housing for enclosing the memory and the processor;

a display being attached to the housing in a manner in which the settings for the specific type of abnormal motion or a status may be viewed,

the input system being attached to the housing in a manner in which settings for the specific type of abnormal motion may be entered by the person;

the characteristics of the specific type of motion including a first threshold and a second threshold;

the algorithm stored in the memory including at least one or more instructions for computing a time derivative of acceleration;

determining whether one or more peaks of a time derivative of the accelerations is above the first threshold;

determining a count of how many peaks occur within a window of time that are higher than the first threshold;

determining whether the count is greater than the second threshold; and

if the count is greater than the second threshold, generating an indication that the specific type of motion occurred, and

wherein the specific type of abnormal motion includes a seizure.

7. The body worn portable device of claim 6, the algorithm stored on memory further including at least receiving a user chosen value, via the input system, for the first threshold.

8. The wrist or body worn portable device of claim 7, the algorithm stored on memory further including at least receiving a user chosen value, via the input system, for the second threshold.

9. The body-worn portable device of claim 6, the characteristics of the specific type of motion including a third threshold and a fourth threshold;

the count being a first count;

the algorithm stored in the memory including at least one or more instructions for determining whether the one or more peaks of the time derivative of the acceleration is above the third threshold;

determining a second count of how many peaks occur within a window of time that are higher than the third threshold; and

determining whether the second count is greater than the fourth threshold; and

if the second count is greater than the fourth threshold, storing motion activity in the memory.

10. A system comprising:

a motion detector measuring motion data associated with a person;

a memory having stored thereon an algorithm including at least analyzing motion data, determining whether the motion data indicates that a specific type of motion has occurred based on the analyzing, and if it is determined that the specific type of motion has occurred, generating an indication that the specific type of motion occurred, based on the determining; and

a processor for implementing the algorithm to determine whether motion data collected by the motion detector corresponds to the specific type of motion;

the specific type of motion being an abnormal motion including a seizure.

11. The system of claim 10, the indication that the specific type of motion occurred being an alert, and the algorithm also including sending the alert.

12. The system of claim 10 where the specific type of motion includes at least a shake having at least a sudden to and fro movement.

13. The system of claim 10, the motion detector including at least a plurality of cameras, and the motion data being images of the person.

14. The system of claim 13, the algorithm including a method of determining an optical flow of images, and the analyzing of the motion data including analyzing the optical flow.

15. The system of claim 13, the algorithm including a method of determining motion vectors from images, and the analyzing of the motion data including analyzing the motion vectors.

16. The system of claim 10, wherein the motion detector includes at least an accelerometer or gyro sensor, and the motion data being output of the accelerometer or gyro sensor.

17. The system of claim 10, further comprising location determination hardware for determining a location of the person.

18. The system of claim 10, further comprising global positioning system hardware and global positioning system software for determining a location of the person.

19. The system of claim 10, the motion data including at least a threshold value for a frequency of oscillation of a body part;

the analyzing including at least comparing a value characterizing the motion to the threshold; and

the determining including at least determining whether the value crossed the threshold based on the comparing to determine whether the specific type of motion has occurred.

20. The system of claim 10, the motion data including one or more motion patterns characterizing a specific type of motion;

the analyzing including at least comparing a motion pattern derived from the data collected during the collecting to the one or more motion patterns characterizing a specific type of abnormal motion; and

the determining including at least determining whether the motion pattern derived matches within a predetermined tolerance of one of the one or more motion patterns characterizing the specific type of abnormal motion.

21. The system of claim 10, the analyzing including at least determining oscillatory motion;

and

determining one or more parameters characterizing the oscillatory motion.

22. The system of claim 21, the motion data including at least a frequency of oscillation;

the determining whether the motion data indicates that a specific type of motion has occurred including at least comparing the frequency of oscillation to a predetermined threshold; and

the determining one or more parameters characterizing the oscillatory motion including at least determining whether the frequency of oscillation is higher than the predetermined threshold

the algorithm further including at least activating of an alert if the frequency of oscillation is higher than the predetermined threshold.

23. The system of claim 10, the algorithm also including at least receiving training data related to several events, each event being known to include at least portion of a specific type of abnormal motion;

the analyzing including at least determining whether an unknown event is a specific type of abnormal motion based on the training data.

24. The system of claim 23, the training data also including at least one event known not to include a specific type of abnormal motion.

25. The system of claim 10, the analyzing including at least determining how many peaks in a magnitude of acceleration occur during a period of time;

the determining whether the motion indicates a specific type of motion includes at least determining if the number of peaks is greater than a threshold.

26. The system of claim 10, the analyzing including at least determining how many peaks in at least one component of acceleration occur during a period of time;

the determining whether the motion indicates a specific type of motion includes at least if the number of peaks is greater than a threshold.

27. The system of claim 10, the analyzing including at least determining how many jerks occur during a period of time;

the determining of whether the motion indicates a specific type of motion includes at least determining if the number of jerks is greater than a threshold, sending an alert.

28. The system of claim 27, each jerk of the jerks being an absolute value of a numerical estimate of a first derivative of a magnitude of acceleration.

29. The system of claim 10, the analyzing including at least computing a second derivative of acceleration of the one or more parts of the body during a specified window;

the determining of whether the motion indicates a specific type of motion includes at least determining how many second derivative within the specified window have crossed a threshold indicative of a specific type of motion.

30. The system of claim 10, the analyzing including at least representing the motion as a sum of basis functions multiplied by coefficients;

the determining of whether the motion indicates a specific type of motion includes at least determining if a magnitude of the one or more coefficients crosses a predetermined threshold that is known to be an indication of a specific type of motion.

31. The system of claim 10, the algorithm including rules for determining a specific type of motion.

32. The system of claim 10, the algorithm including a statistical model of data associated with a specific type of motion for determining a specific type of motion.

33. The system of claim 10, further comprising a neural network for determining whether data is indicative of a specific type of motion.

34. The system of claim 10, the algorithm including at least rules for determining a specific type of motion;

a statistical model of data associated with a specific type of motion for determining a specific type of motion; and

a training routine for learning behaviors associated with a specific type of motion; and

the system further comprising a neural network for determining whether data is indicative of a specific type of motion.

35. The system of claim 10, the indication that the specific type of motion occurred is an alert, the system further comprising:

a transmitter that transmits

the alert to a remote location associated with a concerned party to protect the health of an individual associated with specific type of motion, which is abnormal, the alert being in response to signals received from the processor resulting from the processor determining that a specific type of motion occurred based on the processor implementing the algorithm.