Personal emergency response (PER) system

• US 8,531,291 B2
• Filed: 12/28/2011
• Issued: 09/10/2013
• Est. Priority Date: 10/16/2005
• Status: Active Grant

• Current Assignee: BT Wearables LLC (Empire IP LLC)
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Primary Examiner(s): POPE, DARYL C
• Application Number:
  US13/338,269
• Publication Number:
  US 20120092157A1
• Time in Patent Office: 622 Days
• Field of Search: 340/506, 340/540, 340/3.1, 340/573.1, 340/539.1, 340/539.11, 340/539.13
• US Class Current: 340/540
• CPC Class Codes: A43B 3/42 where power is generated by conversion of mechanical movement to electricity, e.g. by piezoelectric means
• CPC Class Codes: A43B 3/46 Global positioning system [GPS] sensors
• CPC Class Codes: A43B 3/48 with transmitting devices, e.g. GSM or Wi-Fi®
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0008 Temperature signals
• CPC Class Codes: A61B 5/0013 Medical image data A61B1/00011, A61B6/56, A61B8/56 take precedence
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1117 Fall detection
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• 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 5/6803 : Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A61B 5/6806 : Gloves
• CPC Class Codes: A61B 5/6807 : Footwear
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/6826 : Finger
• CPC Class Codes: A61B 5/6891 : Furniture
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 7/00 : Instruments for auscultation
• CPC Class Codes: A61B 7/04 : Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: A61B 7/045 : Detection of Korotkoff sounds A61B5/0225 takes precedence; measuring blood pressure A61B5/021
• CPC Class Codes: A61B 8/00 : Diagnosis using ultrasonic, sonic or infrasonic waves imaging of objects using sonar G01S15/00
• CPC Class Codes: A61B 8/06 : Measuring blood flow measuring volume flow in general G01F, e.g. G01F1/66, G01F1/72; measuring speed of fluids in general G01P5/00
• CPC Class Codes: A61B 8/0808 : for diagnosis of the brain
• CPC Class Codes: A61B 8/488 : involving Doppler signals
• CPC Class Codes: A61B 8/565 : involving data transmission via a network
• CPC Class Codes: G08B 21/02 : Alarms for ensuring the safety of persons
• CPC Class Codes: G08B 21/0423 : detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 21/0446 : worn on the body to detect changes of posture, e.g. a fall, inclination, acceleration, gait
• CPC Class Codes: G08B 21/0453 : worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 21/0461 : integrated or attached to an item closely associated with the person but not worn by the person, e.g. chair, walking stick, bed sensor
• CPC Class Codes: G08B 21/0476 : Cameras to detect unsafe condition, e.g. video cameras
• CPC Class Codes: G08B 21/0484 : Arrangements monitoring consumption of a utility or use of an appliance which consumes a utility to detect unsafe condition, e.g. metering of water, gas or electricity, use of taps, toilet flush, gas stove or electric kettle
• CPC Class Codes: G08B 21/0492 : Sensor dual technology, i.e. two or more technologies collaborate to extract unsafe condition, e.g. video tracking and RFID tracking
• CPC Class Codes: G08B 25/016 : Personal emergency signalling and security systems emergency non-personal manually actuated alarm activators G08B25/12
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/13 : delivered from dispensers
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 80/00 : ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: H04M 2250/12 : including a sensor for measuring a physical value, e.g. temperature or motion telephones including GPS signal receivers H04M2250/10
• CPC Class Codes: H04M 3/5116 : for emergency applications
• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A system includes one or more sensors mounted on a mobile patient; a wireless transceiver to communicate with a remote station; and a processor coupled to the sensor and the wireless transceiver to request assistance if the processor detects a fall by the mobile patient.

Claims

1. A system, comprising:

one or more sensors mounted on a patient;

a wireless transceiver to communicate with a remote station connected to a telephone communication network; and

a processor coupled to the sensor and the wireless transceiver to request assistance if the processor detects a fall by the patient using body signature sequences and automatically requests assistance for the patient using a telephone, and wherein the processor also accepts a physical patient actuation with a button to request assistance, and wherein sequences of human motions are classified into groups of similar postures represented by model-states with a posture model depicting inter-relationships among model-states.

2. The system of claim 1, wherein the processor distinguishes between a fall and normal body movement.

3. The system of claim 1, wherein the processor automatically notifies a third party for assistance upon detecting the fall without a user pressing a button to call for help.

4. The system of claim 1, wherein the processor captures movement activities and comparing the movement activities to fall patterns.

5. The system of claim 1, wherein the processor determines an in-door position of the patient using signals between the wireless transceiver and the remote station.

6. The system of claim 1, wherein the processor determines a fall based on detected motions including one or more changes in height or one or more accelerations associated with movements by the patient.

7. The system of claim 1, wherein the processor waits for a predetermined period to detect if the patient can recover from the fall and automatically requests assistance if the patient is unable to recover from the fall.

8. The system of claim 1, comprising a call center coupled to the processor to provide a human response.

9. The system of claim 1, wherein the processor off-loads processing of accelerometer outputs to the remote computer to conserve battery power.

10. The system of claim 1, wherein the processor includes code to generate an acceleration signature based on sensed acceleration;

code to match the acceleration signature with at least one of a plurality of stored acceleration signatures, wherein each stored acceleration signatures corresponds with a type of motion; and

code to identify the type of motion based on the matching of the acceleration signature with the stored acceleration signature.

11. A method to monitor a person, comprising:

capturing motions from the person using a wearable device worn by the person;

processing sensor outputs on a processor to determine body signature sequences;

determining a body pattern associated with a dangerous condition from the sensor outputs; and

requesting assistance through a telephone call if a dangerous condition is detected, and wherein sequences of human motions are classified into groups of similar postures represented by model-states with a posture model depicting inter-relationships among model-states.

12. The method of claim 11, wherein the person'"'"'s motions form a sequence of elemental motions, comprising:

identifying each elemental motion of a sequence of elemental motions of the accelerometer; and

identifying the activity of the patient, comprising matching the sequence of identified elemental motions with a library of stored sequences of elemental motions, wherein each stored sequence of elemental motions corresponds with an activity.

13. The method of claim 11, comprising identifying each elemental motion by:

generating an acceleration signature based on sensed acceleration of the device;

matching the acceleration signature with at least one of a plurality of stored acceleration signatures,

wherein each stored acceleration signatures corresponds with a type of motion;

identifying the type of motion of the device based on the matching of the acceleration signature with the stored acceleration signature.

14. The method of claim 11, further comprising refining identification of the activity, comprising analyzing one of:

timing of the identified activity, at least one identified location of the identified activity, a rate of change of a location of the patient, or patterns of a plurality of identified activities.

15. A method of automatically requesting assistance for a mobile patient, comprising:

attaching a mobile device with one or more accelerometers on the patient to detect patient motion;

processing accelerometer outputs on a processor;

determining a fall based on detected patient motion; and

automatically requesting assistance from the mobile device on behalf of the patient with a telephone call if help is needed and allowing the patient to manually push a button to request help, and wherein sequences of human motions are classified into groups of similar postures represented by model-states with a posture model depicting inter-relationships among model-states.

16. The method of claim 15, comprising detecting if the patient has fallen based on a plurality of accelerometer signals and indicating the fall if the accelerometer signals indicate that the patient is unable to get up.

17. The method of claim 15, comprising notifying an emergency service for assistance automatically without a user pressing a button to call for help.

18. The method of claim 15, comprising detecting the fall by analyzing changes in height and accelerations associated with the changes in height.

19. The method of claim 15, further comprising:

determining if the patient has fallen based on a plurality of sensor signals indicating changes in height or accelerations associated with the changes in height;

waiting for a predetermined period to detect if the patient can independently recover from the fall; and

requesting assistance if the sensor signals indicate that the patient is unable to independently recover from the fall.

20. A method to provide personal emergency response for a patient, comprising:

wearing a mobile device with a help button and at least one or more accelerometers on the patient to detect patient motion;

determining a fall based on detected motions including one or more changes in height and one or more accelerations associated with movements by the patient;

waiting for a predetermined period to detect if the patient can recover from the fall and automatically requesting assistance with a telephone call if the patient is unable to recover from the fall; and

requesting assistance if the patient pushes the help button, and wherein sequences of human motions are classified into groups of similar postures represented by model-states with a posture model depicting inter-relationships among model-states.

21. The method of claim 20, comprising calling the patient to determine if the patient needs assistance and requesting help if the patient fails to respond to the call.

22. The method of claim 20, further comprising determining a severity of the fall based on the predetermined period or based on one or more accelerometers'"'"' output magnitudes.

23. The method of claim 21, comprising off-loading processing of accelerometer outputs to a remote computer.