Personal emergency response (PER) system

• US 8,525,687 B2
• Filed: 09/14/2012
• Issued: 09/03/2013
• Est. Priority Date: 06/30/2006
• Status: Expired due to Fees

• Current Assignee: BT Wearables LLC (Empire IP LLC)
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Primary Examiner(s): POPE, DARYL C
• Application Number:
  US13/617,172
• Publication Number:
  US 20130009783A1
• Time in Patent Office: 354 Days
• Field of Search: 340/506, 340/3.1, 340/539.1, 340/539.11, 340/539.13, 340/573.6
• US Class Current: 340/669
• 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

Systems and methods for identifying an activity of an object includes identifying each elemental motion of a sequence of elemental motions of a device attached to the object; and identifying the activity of the object, comprising matching the sequence of identified elemental motions of the device with a library of stored sequences of elemental motions, wherein each stored sequence of elemental motions corresponds with an activity.

Claims

1. A method of identifying an activity of an object, comprising:

identifying each elemental motion of a sequence of elemental motions of a device attached to the object; and

identifying the activity of the object, comprising matching the sequence of identified elemental motions of the device with a library of stored sequences of elemental motions, wherein each stored sequence of elemental motions corresponds with an activity; and

making a telephone call through a telephone base station to request help 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.

2. The method of claim 1, wherein identifying each elemental motion comprises:

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.

3. The method of claim 1, further comprising refining identification of the activity, comprising analyzing timing of the identified activity.

4. The method of claim 3, wherein timing comprises at least one of an hour of a day, a day of a week, a week of a month, a month of a year.

5. The method of claim 1, further comprising refining identification of the activity, comprising analyzing at least one identified location of the identified activity.

6. The method of claim 1, further comprising refining identification of the activity, comprising analyzing a rate of change of a location of the object.

7. The method of claim 1, further comprising refining identification of the activity, comprising analyzing patterns of a plurality of identified activities.

8. The method of claim 1, further comprising identifying a behavior of the object.

9. The method of claim 8, wherein identifying a behavior of the object comprises:

identifying each activity of a sequence of activities of the animate or inanimate object; and

identifying the behavior of the object, comprising matching the sequence of identified activities of the object with a stored sequences of activities, wherein each stored sequence of activities corresponds with an identified behavior.

10. The method of claim 9, further comprising tracking at least one behavior of the animate or inanimate object over time.

11. The method of claim 10, further comprising identifying patterns of the at least one behavior.

12. The method of claim 11, further comprising grouping the patterns of objects based on a common parameter between the objects.

13. The method of claim 10, further comprising identifying changes in at least one behavior of the object.

14. The method of claim 10, further comprising sending an alert upon identification of predetermined set of behavior changes.

15. The method of claim 1, further comprising performing a time domain analysis on an acceleration signal to identify signatures of the acceleration signal.

16. An apparatus for identifying an activity of an object, comprising:

a controller operative to identify each elemental motion of a sequence of elemental motions of device attached to the object; and

the controller operative to identify the activity of the object, comprising matching the sequence of identified elemental motions of the object with stored sequences of elemental motions, wherein each stored sequence of elemental motions corresponds with an activity, wherein the controller initiates a telephone call to request help 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.

17. The apparatus of claim 16, wherein identifying each elemental motion comprises:

an accelerometer operative to generate an acceleration signature based on sensed acceleration of the device;

the controller operative 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;

the controller operative to identify the type of motion of the object based on the matching of the acceleration signature with the stored acceleration signature.

18. The apparatus of claim 16, wherein at least a portion of the controller is included within a device, and at least a portion of the controller is located external to the device.

19. The apparatus of claim 16, wherein the controller refines identification of the activity, comprising analyzing patterns of a plurality of identified activities.

20. A system for identifying an activity of an animate or inanimate object, comprising:

means for identifying each elemental motion of a sequence of elemental motions of an device attached to the animate or inanimate object; and

means for identifying the activity of the animate or inanimate object, comprising matching the sequence of identified elemental motions of the device with a library of stored sequences of elemental motions, wherein each stored sequence of elemental motions corresponds with an activity;

wherein the means for identifying each elemental motion comprises;

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

means for 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; and

means for identifying the type of motion of the device based on the matching of the acceleration signature with the stored acceleration signature; and

a base station coupled to a telephone network to initiate a telephone call to request help 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.