Wireless monitoring

• US 9,549,691 B2
• Filed: 04/23/2014
• Issued: 01/24/2017
• Est. Priority Date: 05/24/2007
• Status: Active Grant

• Current Assignee: Bao Tran
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Primary Examiner(s): Heller, Tammie K
• Application Number:
  US14/260,228
• Publication Number:
  US 20140235965A1
• Time in Patent Office: 1,007 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2560/0412 Low-profile patch shaped housings
• CPC Class Codes: A61B 2560/045 with a separable interface unit, e.g. for communication
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• 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 5/0059 using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/02042 Determining blood loss or bleeding, e.g. during a surgical procedure
• 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 5/1075 for measuring dimensions by non-invasive methods, e.g. for determining thickness of tissue layer A61B8/0858 takes precedence
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• 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 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/242 Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
• 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 5/4023 : Evaluating sense of balance
• CPC Class Codes: A61B 5/447 : specially adapted for aiding the prevention of ulcer or pressure sore development, i.e. before the ulcer or sore has developed
• CPC Class Codes: A61B 5/4875 : Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/4881 : Determining interstitial fluid distribution or content within body tissue measuring analytes in interstitial fluid in-vivo A61B5/1451
• CPC Class Codes: A61B 5/681 : Wristwatch-type devices
• CPC Class Codes: A61B 5/7203 : for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7257 : using Fourier transforms
• CPC Class Codes: A61B 5/726 : using Wavelet transforms
• 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 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7465 : Arrangements for interactive communication between patient and care services, e.g. by using a telephone network telemetry of measured physiological signal A61B5/0002
• 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 8/08 : Detecting organic movements or changes, e.g. tumours, cysts, swellings A61B8/02 - A61B8/06 take precedence
• CPC Class Codes: A61B 8/565 : involving data transmission via a network
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 40/63 : for local operation

Abstract

A monitoring system includes a Doppler radar formed with an IEEE 802 protocol transmitter and an IEEE 802 protocol receiver to detect motion, wherein frequency waves are transmitted by the IEEE 802 protocol transmitter and reflected waves are received by the IEEE protocol 802 receiver; and an analyzer to perform Doppler operations using the IEEE 802 protocol transmitter and receiver, wherein the analyzer calibrates a training Doppler radar signal during a training phase to develop a model and wherein the analyzer applies the model with the Doppler radar signal during an operational phase to determine position.

Claims

1. A monitoring system, comprising:

a Doppler radar formed with an IEEE 802 protocol transmitter and an IEEE 802 protocol receiver to detect body motion of a person, wherein frequency waves are transmitted by the IEEE 802 protocol transmitter and reflected waves are received by the IEEE 802 protocol receiver as part of a multiple input, multiple output (MIMO) wireless adapter chip set coupled to a plurality of antennas and a plurality of radios which send data over two transceivers simultaneously, wherein the Doppler radar is configured to generate an operational Doppler radar signal; and

an analyzer configured to perform Doppler operations using the IEEE 802 protocol transmitter and receiver, wherein the analyzer calibrates a training Doppler radar signal with a training body position during a training phase to develop a model and wherein the analyzer is configured to use the model with the operational Doppler radar signal during an operational phase to determine a body position of the person.

2. The system of claim 1, further comprising non-transitory computer medium configured to identify one person from another based on heart rate signature characteristics.

3. The system of claim 1, further comprising one of:

an EMG detector, EEG detector, an electromagnetic detector, an ultrasonic detector, an optical detector, a Hidden Markov Model (HMM) recognizer, a dynamic time warp (DTW) recognizer, a neural network, a fuzzy logic engine, or a Bayesian network.

4. The system of claim 1, further comprising a non-transitory computer medium configured to detect patient physical activity patterns and recognize body motion patterns for falls.

5. The system of claim 1, further comprising an in-door positioning system coupled to one or more mesh network appliances to provide location information.

6. The system of claim 1, further comprising a call center coupled to the analyzer to provide a human response.

7. The system of claim 1, wherein the 802 protocol comprises one of:

an 802.11_protocol, an 802.15 protocol, an 802.16 protocol, a WiFi protocol, or a WIMAX®

(Worldwide Interoperability for Microwave Access) protocol.

8. The system of claim 1, further comprising a wireless router and wherein the wireless router comprises one of:

an 802.11 router, an 802.16 router, a WiFi router, a WiMAX®

router, a Bluetooth®

router, or an X10 router.

9. The system of claim 1, further comprising a mesh network appliance coupled to a power line to communicate X10 data to and from a mesh network.

10. The system of claim 1, further comprising a non-transitory computer medium configured to cause the IEEE 802 transmitter to transmit voice.

11. The system of claim 1, further comprising wireless sensors configured to be positioned on the person that allow a reflected signal to be isolated from radar clutter, and that sense additional body data not derived from the operational Doppler radar signal.

12. The system of claim 1, further comprising a security alarm coupled to the Doppler radar.

13. The system of claim 1, further comprising a non-transitory computer medium to reconstruct an image of a moving object on an opposite side of a wall from the analyzer.

14. The system of claim 1, wherein the analyzer is configured to track arm and leg movements to determine muscle weakness reflective of a health problem.

15. A monitoring system for a person, comprising:

a wireless local area network (WLAN) transceiver operating as a Doppler radar to wirelessly detect body movement of the person as part of a multiple input, multiple output (MIMO) wireless adapter, wherein the WLAN transceiver is configured to generate an operational Doppler radar signal; and

a processor coupled to the WLAN transceiver to monitor body position, wherein the processor calibrates a training Doppler radar signal with a training body position during a training phase to develop a model and wherein the processor is configured to use the model with the operational Doppler radar signal during an operational phase to estimate body position of the person.

16. The system of claim 15, wherein the processor is configured to detect body movements due to breathing or to heart movement.

17. The system of claim 15, wherein the processor is configured to identify one person from another using each person'"'"'s rib cage signature.

18. The system of claim 15, wherein the processor is configured to monitor the person for physical activity patterns and recognize body motion patterns leading to falls.