METHOD, SYSTEM AND APPARATUS FOR FALL DETECTION

• US 20140276238A1
• Filed: 03/15/2013
• Published: 09/18/2014
• Est. Priority Date: 03/15/2013
• Status: Abandoned Application

• Current Assignee: Ivan Osorio
• Original Assignee: Ivan Osorio
• Inventors: Osorio, Ivan
• Application Number:
  US13/836,059
• Publication Number:
  US 20140276238A1
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/026 Measuring blood flow A61B3/1233, A61B3/1241 take precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/16 Devices for psychotechnics using teaching or educational appliances G09B1/00 - G09B7/00; Testing reaction times ; Devices for evaluating the psychological state
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G08B 21/043 detecting an emergency event, e.g. a fall
• CPC Class Codes: G08B 21/0446 worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait

Abstract

Methods, systems, and apparatuses are provided for detecting fall events of a person. Fall events are falls that are likely to occur, are occurring, or have occurred. Fall detectors and fall detector systems detect fall events of the person. Data relating to the person are received from sensors and analyzed to perform fall detection. Data relating to the person includes accelerations and forces experience by the person, changes in body position of the person, movements of the person, and body signals and sounds of the person. Neurological tests are administered to determine levels of responsiveness and awareness of the person in response to detections. Warnings are issued, and safety measures are deployed, in response to detections. Data relating to fall events are recorded and logged. Fall event histories based upon the logged data and fall detection algorithm performance are used to improve future fall detection and prediction.

Claims

1. A method for detecting a fall, comprising:

receiving data from at least one sensor associated with a person;

analyzing the received data to detect changes therein;

detecting the person is falling or that a fall is imminent based on the analysis of the received data;

issuing a warning;

administering to the person, in response to the detection, a neurological test requiring the person to manually or orally input a level of responsiveness or awareness; and

logging to a memory at least of one a date and time of the fall or one or more results of a test administered to the person.

2. The method of claim 1, wherein administering the neurological test further comprises at least one of:

administering an orientation test,

administering a motor test, or

administering a memory test.

3. The method of claim 1, wherein the received data is data detected by at least one of a tri-axial accelerometer, a clinometer, a force transducer, a gyroscope, a body signal sensor, an optical sensor, a motor sensor or a sound sensor, and

wherein the received data is at least one of a change in acceleration along at least one axis of the tri-axial accelerometer, change in incline or decline detected by the clinometer, a measure of force detected by the force transducer, a sway or a loss of balance detected by the gyroscope, the clinometer, or the tri-axial accelerometer, a body signal detected by the body signal sensor, a body movement detected by the optical sensor, a muscle state detected by the motor sensor, or a sound associated with the person detected by the sound sensor.

4. The method of claim 1, further comprising receiving and analyzing electromyogram (EMG) data to determine the person is falling or that a fall is imminent.

5. The method of claim 4, wherein the fall detection is further based on EMG data measured from at least one anti-gravitatory muscle where the EMG data deviates above a first threshold indicating abnormally high muscle tone or below a second threshold indicating abnormally decreased muscle tone.

6. The method of claim 5, wherein the fall detection is further based on a force of contraction on at least one anti-gravitatory muscle or at least one of its antagonists.

7. The method of claim 1, further comprising administering one or more additional neurological tests,

wherein the neurological test is a responsiveness test,

wherein the one or more additional neurological tests are at least one of an awareness test or a cognitive level test, and

wherein the one or more additional neurological tests are administered in response to the person not failing the responsiveness test.

8. The method of claim 7, wherein the one or more additional neurological tests are re-administered to the person based on at least one of motor activity of the person, one or more sounds associated with the person, or a time interval.

9. The method of claim 1, further comprising categorizing possible causes of the fall based on a type of fall, a presence or absence of breaking arm movements, a body part that first makes contact with a surface, a body position before the fall, a body position during the fall, a direction of the fall, a body position immediately after the fall, a type of kinetic activity before the fall, an amount of time elapsed since the last kinetic activity of the person, a type and/or a level of kinetic activity, autonomic activity before, during or after the fall, or neurologic activity before, during or after the fall.

10. The method of claim 1, wherein detecting a person is falling or that a fall is imminent is based at least upon a value of the received data crossing a threshold or deviating from a baseline value by a predetermined amount.

11. The method of claim 10, further comprising adjusting the threshold or the baseline value based upon at least on one of a fall history of the person or past performance of an algorithm used for fall detection.

12. The method of claim 1, wherein the fall detection is further based on a decrease in autonomic signals below a first autonomic baseline or above a second autonomic baseline.

13. The method of claim 1, further comprising ranking a severity of the fall based on at least one of a result of the responsiveness test, motion data, a presence of pain or severity pain, a force of impact, a site of impact on the body of the person, or a distance traveled by the body of the person after a first impact;

wherein the motion data comprises a duration of time spent falling, a duration of time spent lying down after the fall, or one or more changes in acceleration during the fall.

14. The method of claim 13, further comprising automatically reporting at least one of a location of the person or the ranking of the severity of the fall to a remote entity after detecting the fall.

15. The method of claim 1, further comprising sensing a force of impact on a part of the body of the person resulting from the fall.

16. A method for detecting whether a person has fallen, comprising:

receiving a measure of force from a sensor associated with the person;

analyzing the measure of force with respect to time;

detecting the person has fallen based on the analysis of the measure of force with respect to time;

administering to the person, in response to the detection, a neurological test requiring the person to manually or orally input a level of responsiveness;

logging to a memory at least one of, a date and time of the fall, a force of impact or results of the neurological test; and

issuing a warning in response to the detection that the person has fallen.

17. The method of claim 16, wherein the sensor is a force transducer or a tri-axial accelerometer.

18. The method of claim 16, further comprising:

logging an amount of time spent in an unresponsive state or an amount of time spent lying on the ground.

19. The method of claim 16, further comprising:

automatically reporting at least one of a location of the person or a severity of the fall to a remote entity after detecting the fall.

20. A fall detection system, comprising:

at least one sensor associated with a user;

a fall detection unit coupled to the at least one sensor, and configured to filter and analyze a measure of force determined by the at least one sensor and determine at least one of the user falling, the user has fallen, or a fall of the user is imminent;

a neurological unit coupled to the fall detection unit and configured to administer a neurological test to the user in response to the at least one of the user falling, the user having fallen, or a fall of the user being imminent;

a user-input unit coupled to the neurological unit and configured to receive a neurological test input from the user; and

a communication unit coupled to the fall detection unit and to the neurological unit, and configured to communicate with at least one of a care-giver station, an emergency medical technology station, or a remote entity.

21. The fall detection system of claim 20, wherein the at least one sensor is at least one of a tri-axial accelerometer or a force transducer.

22. The fall detection system of claim 20, further comprising a logging unit configured to log a date, a time, an amount of time spent in an unresponsive state, an amount of time spent lying on the ground, and/or the measured force associated with a detected fall.

23. The fall detection system of claim 20, wherein the user-input unit comprises:

a communication interface configured to solicit the user to reply to the neurological test; and

at least one of;

a touch sensitive interface configured to receive the neurological test input from the user, or

a sound sensitive interface configured to receive the neurological test input from the user.

24. A fall detection system, comprising:

at least one of a tri-axial accelerometer, a clinometer, a force transducer, or a gyroscope attached to a user;

a fall detection unit coupled to the at least one of the tri-axial accelerometer, the clinometer, the force transducer, or the gyroscope, and configured to filter and analyze motion data or force data received from the at least one of the tri-axial accelerometer, the clinometer, the force transducer, or the gyroscope and determine if the user is falling or that a fall is imminent;

a neurological unit coupled to the fall detection unit and configured to administer a responsiveness test to the user; and

a user-input unit coupled to the neurological unit and configured a receive a responsiveness test input from the user.

25. The fall detection system of claim 24, further comprising:

one or more shock absorbing devices placed on at least one of the head, neck, chest, or one or more knees of the user, wherein the one or more shock absorbing devices are configured to be automatically deployed based on a signal or command from the fall detection unit in response to determining that the user is falling or that a fall is imminent.

26. The fall detection system of claim 25, further comprising a body data signal unit configured to receive electromyogram (EMG) data and transmit the EMG data to the fall detection unit;

and

wherein the fall detection unit is configured to verify a fall is occurring based on;

at least one of the motion data or the force data received from at least one of the tri-axial accelerometer, the clinometer, or the force transducer, and

at least one of the EMG data or the motion data received from at least one of the clinometer or the gyroscope.