Methods for the diagnosis and treatment of neurological disorders

1. A system for determining a neurological disorder in a subject, comprising:

a computer comprising an audio/visual device and configured to output a cue on the audio/visual device;

a motion sensor configured for placement on a limb of the subject and further configured to capture movements and micro-movements of the limb of the subject in response to the cue on the audiovisual device;

programming instructions configured to cause the computer to:

determine a series of sensor data representing the captured movements and micro-movements as a function of time, wherein the movements and micro-movements include one or more intended movements,

estimate parameters of a continuous Gamma distribution family for each series of sensor data, wherein the parameters define a Gamma parameter space,

determine a position, in the Gamma parameter space, of estimated parameters corresponding to series of sensor data associated with the one or more intended movements, and

analyze a motion pattern associated with movements and micro-movements of the limb of the subject in response to the cue on the audio/visual device to determine whether there is a presence of a neurological disorder in the subject by analyzing the position of estimated parameters in the Gamma parameter space corresponding to series of sensor data associated with the one or more intended movements.

2. The system of claim 1 , wherein the programming instructions for estimating the parameters of the continuous Gamma distribution family comprise programming instructions configured to estimate a shape and scale parameter of the continuous Gamma distribution family.

3. The system of claim 2 , wherein the programming instructions for estimating the parameters of the continuous Gamma distribution family comprise programming instructions configured to:

display the estimated shape and scale parameters of the continuous Gamma distribution family on a Gamma parameter plane with confidence intervals, wherein an inherent variability in the motion pattern is represented and tracked on the Gamma parameter plane.

4. The system of claim 1 , wherein the programming instructions for estimating the parameters of the continuous Gamma distribution family comprise programming instructions configured to:

estimate one or more moments of the continuous Gamma distribution family; and

display the estimated one or more moments of the Gamma distributions on a multi-dimensional space, wherein the motion pattern is represented in the multi-dimensional space.

5. The system of claim 1 , wherein each motion sensor is an electro-magnetic sensor.

6. The system of claim 1 , further comprising one or more image sensors configured to capture the bodily movements and micro-movements of the subject over time, wherein the programming instructions for determining the series of sensor data comprise additional programming instructions configured to include additional data that represent the bodily movements and micro-movements captured from the one or more image sensors.

7. The system of claim 1 , wherein programming instructions for estimating the parameters of the continuous Gamma distribution family are configured to use a maximum likelihood estimation.

8. The system of claim 1 , wherein the programming instructions for determining whether there is a presence of neurological disorder in the subject comprise programming instructions configured to cause the computer to determine whether the estimated parameters of the continuous Gamma distribution family correspond to an exponential distribution or a Gaussian distribution.

9. The system of claim 1 , further comprising additional programming instructions configured to determine whether the one or more series of sensor data correspond to an intended motion or a spontaneous motion based on a type of the estimated Gamma distribution family.

10. The system of claim 1 , wherein the programming instructions for analyzing the motion pattern further comprise programming instructions configured to cause the computer to classify whether the motion pattern corresponds to a typical motion pattern or an atypical motion pattern.

11. The system of claim 1 , wherein the motion sensor configured for placement on a limb of the subject is configured for placement on a digit corresponding to the limb.

12. The system of claim 1 , wherein the one or more movements and micro-movements correspond to one or more of the following: intended movements of the limb of the subject or spontaneous movements of the limb of the subject.

13. The system of claim 1 , wherein the audio/visual device is selected from one or more of the following: a robot, a three-dimensional animate, a speaker device, a touch-sensitive user interface, or a display device.

14. The system of claim 1 , wherein the presence of the neurological disorder in the subject is characterized by a position of the estimated parameters in an upper left quadrant of the Gamma parameter space.

15. The system of claim 1 , wherein the programming instructions for determining whether there is a presence of neurological disorder in the subject comprise programming instructions configured to cause the computer to determine whether the estimated parameters of the continuous Gamma distribution family correspond to a skewed distribution.

16. A method for determining a neurological disorder in a subject, comprising:

outputting, by a computer, a cue on an audio/visual device;

capturing, by a motion sensor configured for placement on a limb of the subject, one or more movements and micro-movements of the limb of the subject in response to the cue on the audio/visual device, wherein the movements and micro-movements include one or more intended movements;

determining, by the computer, a series of sensor data representing the captured movements and micro-movements as a function of time;

estimating, by the computer, parameters of a continuous Gamma distribution family for each series of sensor data, wherein the parameters define a Gamma parameter space;

determining, by the computer, a position, in the Gamma parameter space, of the estimated parameters corresponding to series of sensor data associated with the one or more intended movements; and

analyzing, by the computer, a motion pattern associated with movements and micro-movements of the limb of the subject in response to the cue on the audio/visual device to determine whether there is a presence of a neurological disorder in the subject by analyzing the position of the estimated parameters in the Gamma parameter space corresponding to series of sensor data associated with the one or more intended movements.

17. The method of claim 16 , wherein estimating the parameters of the continuous Gamma distribution family comprises estimating a shape and scale parameter of the continuous Gamma distribution family.

18. The method of claim 17 , wherein estimating the parameters of the continuous Gamma distribution family comprises displaying the estimated shape and scale parameters of the continuous Gamma distribution family on a Gamma parameter plane with confidence intervals, wherein an inherent variability in the motion pattern is represented and tracked on the Gamma parameter plane.

19. The method of claim 16 , wherein estimating the parameters of the continuous Gamma distribution family comprises:

estimating, by the computer, one or more moments of the continuous Gamma distribution family; and

displaying, by the computer, the estimated one or more moments of the continuous Gamma distribution family on a multi-dimensional space, wherein the motion pattern is represented in the multi-dimensional space.

20. The method of claim 16 , wherein each motion sensor is an electro-magnetic sensor.

21. The method of claim 16 , wherein determining the series of sensor data further comprises:

capturing, by one or more image sensors, the bodily movements and micro-movements of the subject over time; and

including, by the computer, in the series of sensor data additional data representing the captured bodily movements and micro-movements of the subject from the one or more image sensors.

22. The method of claim 16 , wherein estimating the parameters of the continuous Gamma distribution family comprises using a maximum likelihood estimation.

23. The method of claim 16 , wherein determining whether there is a presence of neurological disorder in the subject comprises determining whether the estimated parameters of the continuous Gamma distribution family correspond to an exponential distribution or a Gaussian distribution.

24. The method of claim 16 , further comprising determining whether the one or more series of sensor data correspond to an intended motion or a spontaneous motion based on a type of the estimated Gamma distribution family.

25. The method of claim 16 , wherein analyzing the motion pattern further comprises classifying whether the motion pattern corresponds to a typical motion pattern or an atypical motion pattern.

26. The method of claim 16 , wherein the motion sensor configured for placement on a limb of the subject is configured for placement on a digit corresponding to the limb.

27. The method of claim 16 , wherein the one or more movements and micro-movements correspond to one or more of the following: intended movements of the limb of the subject or spontaneous movements of the limb of the subject.

28. The method of claim 16 , wherein the audio/visual device is selected from one or more of the following: a robot, a three-dimensional animate, a speaker device, a touch-sensitive user interface, or a display device.

29. The method of claim 16 , wherein the presence of the neurological disorder in the subject is characterized by a position of the estimated parameters in an upper left quadrant of the Gamma parameter space.