Intelligent wearable monitor systems and methods

• US 7,981,058 B2
• Filed: 03/14/2005
• Issued: 07/19/2011
• Est. Priority Date: 03/12/2004
• Status: Active Grant

• Current Assignee: The Trustees of Dartmounth College (Dartmouth College)
• Original Assignee: The Trustees of Dartmounth College (Dartmouth College)
• Inventors: Akay, Metin
• Primary Examiner(s): Hindenburg; Max
• Assistant Examiner(s): DANEGA, RENEE A
• Application Number:
  US11/079,496
• Publication Number:
  US 20050240086A1
• Time in Patent Office: 2,318 Days
• Field of Search: 600/587, 600/595, 600/300
• US Class Current: 600/595
• CPC Class Codes: A61B 2560/045 with a separable interface unit, e.g. for communication
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/0024 for multiple sensor units attached to the patient, e.g. using a body or personal area network
• 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 5/1124 Determining motor skills
• CPC Class Codes: A61B 5/4082 Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6825 Hand
• 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

Abstract

An intelligent wearable monitoring system includes a wireless personal area network for extended monitoring of a patient'"'"'s motor functions. The wireless personal area network includes an intelligent accelerometer unit, a personal server and a remote access unit. The intelligent accelerometer unit measures acceleration data of the patient, in real-time. The personal server processes the acceleration data, applying linear and non-linear analysis, such as fractal analysis, to generate motor function information from the acceleration data. Motor function information is transmitted to a remote access unit for statistical analysis and formatting into visual representations. A data management unit receives the formatted motor function information and displays the information, for example, for viewing by the patient'"'"'s physician.

Claims

1. A method of monitoring the motor function of a patient, comprising:

capturing acceleration data from the patient with at least a first accelerometer and a second, biaxial accelerometer located on an appendage of the patient, the first accelerometer capturing objective acceleration data, and the second, biaxial accelerometer capturing subjective acceleration data relative to at least the first accelerometer;

wirelessly communicating the acceleration data to a personal server positioned on the patient at a location spaced from the accelerometers; and

processing the acceleration data by computing nonlinear parameters for the acceleration data to generate at least two levels of motor function information, the nonlinear parameters chosen from the group of a maximum likelihood estimator fractal, an approximate cross entropy method, and an average amount of mutual information measure.

2. The method of claim 1, further comprising formatting the motor function information by performing at least one of statistical analysis and data mining.

3. The method of claim 1, further comprising wirelessly transmitting the motor function information to a remote access unit, the remote access unit having one or more of a user interface, a transceiver, a processor and a non-volatile memory.

4. The method of claim 1, wherein capturing acceleration data from the patient comprises recording and preprocessing the acceleration data from the accelerometers with a microprocessor configured with the accelerometers in an intelligent accelerometer unit.

5. The method of claim 3, further comprising performing at least one of statistical analysis and data mining to format the motor function information into formatted information at the remote access unit.

6. The method of claim 5, further comprising wirelessly transmitting the formatted information to a data management unit, for one or both of storage and analysis.

7. The method of claim 6, further comprising:

displaying the formatted information at the data management unit; and

determining a level of functional impairment of the patient.

8. The method of claim 1, processing the acceleration data comprising one or more of:

dividing the acceleration data into epochs;

electronically marking the epochs;

attaching the time and date to the acceleration data, and

identifying and measuring physical activity.

9. The method of claim 1, the step of processing the acceleration data further comprising processing, identifying and measuring physical activity.

10. The method of claim 9, the step of processing the acceleration data further comprising assessing changes in the patient'"'"'s motor function.

11. The method of claim 1, the step of processing comprising processing at the personal server.

12. The method of claim 1, further comprising transmitting the motor function information to a remote access unit and performing data mining and analysis at the remote access unit, to format the motor function information.

13. The method of claim 12, further comprising wirelessly transmitting the formatted information to a data management unit.

14. The method of claim 13, further comprising displaying the formatted information at the data management unit.

15. The method of claim 14, further comprising determining a level of functional impairment of the patient.

16. The method of claim 15, wherein determining a level of functional impairment of the patient comprises one or both of diagnosing a neurological disorder and determining the severity of a neurological disorder.