Algorithms for gait measurement with 3-axes accelerometer/gyro in mobile devices

1. A method for characterizing movements of a subject using a gait pattern, the method comprising:

locating a center of gravity of said subject for placement of a mobile device having a tri-axial accelerometer;

measuring using the tri-axial accelerometer at a predetermined data sampling rate, acceleration data in x, y, and z directions of the center of gravity of said subject;

identifying a gait cycle from the measured acceleration data, which comprises:

identifying a time of a heel strike which corresponds to a maximum downward velocity of the center of gravity by performing a integration on the vertical-direction acceleration data, and

identifying a time of a toe-off which corresponds to a minimum height of the center of gravity by performing a double integration on the vertical-direction acceleration data;

wherein the gait cycle comprising at least one stride or at least two steps of said subject, and

wherein said at least one stride or at least two steps comprise at least one heel strike followed by at least one stance phase and at least one swing phase;

time-marking, based on the measured acceleration data, two or more key events in the gait cycle, the key events comprising the at least one heel strike and at least one mid-single leg support;

converting the measured acceleration data in all directions to a time dependent power spectrum, which comprises computing a Dynamic Instability Index (DII) at each time-marked event in the gait cycle and constructing time variations of DII to generate the time dependent power spectrum;

extracting from the measured acceleration data a phase relationship between accelerations along different orientations at each time-marked event in the gait cycle; and

generating a graphical representation on the phase relationships between accelerations along different orientations;

wherein the gait pattern of said subject comprises the time dependent power spectrum and the graphical representation of the phase relationships between accelerations along different orientations.

2. The method according to claim 1 wherein the at least one stance phase describes at least one sequence when at least one landing limb of said subject remains on a surface and pushes backwards against said surface until at least one toe-push starts the at least one swing phase.

3. The method according to claim 1 wherein said acceleration data further comprise magnitude, timing, duration and direction of energy flow of said acceleration data.

4. The method according to claim 1 wherein said time dependent power spectrum is expressed either in high time resolution determined by the acceleration data rate or by averaging over a preset interval.

5. The method according to claim 1 wherein said at least one phase relationship between accelerations along different orientations comprises the phase relationship between surge (forward) and heave (vertical) acceleration orientations.

6. The method according to claim 1 wherein said method has uses comprising characterizing normal gaits and abnormal gaits, the abnormal gaits further comprising Leg Length Discrepancy Syndrome, knee tremors, high knee lift, and gaits from hip replacement patients and Parkinson disease patients.

7. The method according to claim 1 wherein said subject are human.

8. The method according to claim 1 wherein said subject are juveniles.

9. An apparatus for characterizing movements of a subject using a gait pattern, comprising:

a mobile device having a tri-axial accelerometer located approximately at a center of gravity of said subject and configured to measure using the tri-axial accelerometer at a predetermined data sampling rate, acceleration data in x, y, and z directions of the center of gravity of said subject; and

one or more processors configured to:

receive the measured acceleration data;

identify a gait cycle from the measured acceleration data, which comprises:

identifying a time of a heel strike which corresponds to a maximum downward velocity of the center of gravity by performing a integration on the vertical-direction acceleration data, and

identifying a time of a toe-off which corresponds to a minimum height of the center of gravity by performing a double integration on the vertical-direction acceleration data;

wherein the gait cycle comprising at least one stride or at least two steps of said subject, and

wherein said at least one stride or at least two steps comprise at least one heel strike followed by at least one stance phase and at least one swing phase;

time-mark, based on the measured acceleration data, two or more key events in the gait cycle, the key events comprising the at least one heel strike and at least one mid-single leg support;

convert the measured acceleration data to a time dependent power spectrum, the conversion comprising computing a Dynamic Instability Index (DII) at each time-marked event in the gait cycle and constructing tine variations of DII to generate the time dependent power spectrum;

extract from the measured acceleration data a phase relationship between accelerations along different orientations at each time-marked event in the gait cycle; and

generate a graphical representation on the phase relationships between accelerations along different orientations;

wherein the gait pattern of said subject comprises the time dependent power spectrum and the graphical representation of the phase relationships between accelerations along different orientations.

10. The apparatus according to claim 9 wherein the at least one stance phase describes at least one sequence when at least one landing limb of said subject remains on a surface and pushes backwards against said surface until at least one toe-push starts the at least one swing phase.

11. The apparatus according to claim 9 wherein said acceleration data comprise magnitude, timing, duration and direction of energy flow of said acceleration data.

12. The apparatus according to claim 9 wherein said time dependent power spectrum is expressed either in high time resolution determined by the acceleration data rate or by averaging over a preset interval.

13. The apparatus according to claim 9 wherein said at least one phase relationship between accelerations along different orientations comprises the phase relationship between surge (forward) and heave (vertical) acceleration orientations.

14. The apparatus according to claim 9 wherein said apparatus has uses comprising characterizing normal gaits and abnormal gaits, the abnormal gaits further comprising Leg Length Discrepancy Syndrome, knee tremors, high knee lift, and gaits from hip replacement patients and Parkinson disease patients.

15. The apparatus according to claim 9 wherein said subject are human.

16. The apparatus according to claim 9 wherein said subject are juveniles.