Method and apparatus for evaluating a movement pattern

• US 6,473,717 B1
• Filed: 09/07/2000
• Issued: 10/29/2002
• Est. Priority Date: 03/07/1998
• Status: Expired due to Fees

• Current Assignee: Claus-Frenz Claussen, Svetlozar Haralanov
• Original Assignee: Claus-Frenz Claussen, Svetlozar Haralanov
• Inventors: Claussen, Claus-Frenz, Haralanov, Svetlozar
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): TSAI, CAROL S W
• Application Number:
  US09/657,075
• Time in Patent Office: 782 Days
• Field of Search: 702/94, 702/95, 702/150, 702/151, 702/152, 702/153, 348/121, 348/135, 348/136, 345/473, 345/474, 345/475, 345/952, 345/953, 345/959, 356/620, 600/595, 382/103, 382/151, 382/291, 250/227.29, 364/559, 73/650.1
• US Class Current: 702/153
• 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 5/1127 using markers
• CPC Class Codes: A61B 5/4023 Evaluating sense of balance
• CPC Class Codes: A61B 5/6814 Head
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

A method for evaluating a head and trunk movement pattern of a subject includes configuring a plurality of markers to move together with the body of a subject. For each of the plurality of markers, a locus curve in three-dimensional space is detected as a function of time and the locus curve is stored as a data field of a measured data record that is common to the plurality of markers. The movement pattern of the body of the subject is characterized using characteristic variables derived from the measured data record. Reference variables are derived from a stored plurality of reference data records. Each of the characteristic variables is compared with the reference variables derived from the stored reference data records. Each of the characteristic variables is derived from a projection of the locus curve of at least one of the markers onto one of the three datum planes of a Cartesian coordinate system. It is thus possible to interpret the evaluated kinetic pattern and use this information to provide a diagnostic of the basic clinical picture, especially following psychic, psychosomatic and/or neurological disorders.

Claims

1. A method for evaluating a head and trunk movement pattern of a subject, which comprises:

configuring a plurality of markers to move together with the body of a subject;

for each of the plurality of markers, detecting a locus curve in three-dimensional space as a function of time and storing the locus curve as a data field of a measured data record that is common to the plurality of markers;

characterizing a movement pattern of the body of the subject using characteristic variables derived from the measured data record;

deriving reference variables from a stored plurality of reference data records;

comparing each of the characteristic variables with the reference variables derived from the stored reference data records;

deriving each of the characteristic variables from a projection of the locus curve of at least one of the plurality of markers onto one of the three datum planes of a Cartesian coordinate system; and

ascertaining at least one characteristic variable representing a length of one of the locus curves.

2. The method according to claim 1, which comprises:

using the locus curve of at least one of the plurality of markers to ascertain a plurality of sequences corresponding to a sway cycle of body sway; and

deriving at least one of the characteristic variables from a particular one of the sequences.

3. The method according to claim 2, which comprises deriving a mean and a standard deviation of the at least one characteristic variable from a plurality of the sequences.

4. The method according to claim 1, which comprises using a periodic nature of at least one of the locus curves to ascertain a characteristic variable representing a distribution of body sway, the distribution selected from the group consisting of a frequency distribution and an amplitude distribution.

5. The method according to claim 1, which comprises ascertaining a characteristic variable, representing a movement of the center of gravity of the body, from a distance between a starting position and a finishing position of at least one of the plurality of markers.

6. The method according to claim 1, which comprises ascertaining at least one characteristic variable, representing an orientation of a body part in space, using positions of the plurality of markers.

7. The method according to claim 1, which comprises ascertaining a characteristic variable, representing an attitude of a body part with respect to an attitude of another body part, from positions of the plurality of markers.

8. The method according to claim 1, which comprises ascertaining a characteristic variable representing a degree of correspondence between a pattern stored as a graphical element and a shape of at least one part of one of the locus curves.

9. The method according to claim 1, which comprises using fuzzy logic to ascertain at least one standardization factor indicating a degree of correspondence between the measured data record and at least one of the stored plurality of reference data records.

10. The method according to claim 1, which comprises:

storing the plurality of reference data records in a database;

associating a respective identifier based on a clinical picture with each of the plurality of reference data records; and

using respective correspondences between the measured data record and the plurality of stored data records to ascertain an identifier for the measured data record.

11. The method according to claim 10, which comprises using a neural network to perform the step of ascertaining the identifier for the measured data record.

12. The method according to claim 10, which comprises obtaining a self-learning knowledge base by adding the measured data record to the database using the identifier of the measured data record.

13. An apparatus for performing the method according to claim 1, comprising:

the plurality of markers for attachment to a head and a trunk of the body of the subject during the configuration step;

two receivers configured at right angles with respect to each other for detecting the locus curve for each of the plurality of markers, the receivers for providing signals; and

a data processing system including;

a processing stage for calculating the measured data record representing the locus curve from the signals of the two receivers;

the database for storing the reference data record;

an analysis module for deriving the characteristic variables from the measured data record and for deriving the reference variables from the plurality of reference data records; and

a comparison module comparing each of the characteristic variables with the reference variables to ascertain a degree of correspondence between the measured data record and at least one of the plurality of reference data records.

14. The apparatus according to claim 13, wherein the processing stage is configured to associate the locus curve for each of the plurality of markers with the data field of the measured data record.

15. The apparatus according to claim 13, wherein the processing stage includes a temporary data record storage device.

16. The apparatus according to claim 13, wherein the comparison module is configured to associate the measured data record with an identifier based on a clinical picture and to supply the data record to the database using the identifier.

17. The apparatus according to claim 13, wherein the data processing system is configured to supply the characteristic variables to an output module for displaying a movement pattern selected from the group consisting of a measured movement pattern and a reference movement pattern.