LOWER LIMB SPASTICITY MEASUREMENT METHOD

Abstract

A lower limb spasticity measurement method includes the step of setting the lower limbs of the person in a lower limb orthotic device of a gait activity machine, the step of starting up a motor of the gait activity machine to drive the lower limb orthotic device for lower limb activity, the step of getting a statistical distribution data from the output torque of the motor within a predetermined time and then calculating the statistical distribution data to obtain a threshold, and the step of determining whether the output torque of the motor is greater than the threshold or not, and then stopping motor if the output torque of the motor is greater than the threshold. Thus, the method of the invention can accurately measures spasticity in the lower limbs of a person without the use of sensors, effectively saving the cost of equipment.

Claims

1. A lower limb spasticity measurement method, comprising the steps: a) letting a person enter a gait activity machine, and then setting the lower limbs of said person in a lower limb orthotic device of said gait activity machine; b) starting up a motor of said gait activity machine to drive said lower limb orthotic device for the activity of the lower limbs of said person; c) getting a statistical distribution data from the output of the torque variation of said motor within a predetermined time, and then calculating said statistical distribution data to obtain a threshold; and d) determining whether the output torque of said motor is greater than said threshold or not, and then stopping said motor if the output torque of said motor is greater than said threshold.

2. The lower limb spasticity measurement method as claimed in claim 1, wherein said statistical distribution data is divided into a positive half cycle interval and a negative half cycle interval; the threshold for said positive half cycle interval and the threshold for said negative half cycle interval are defined as TH.sup.up=μ.sup.up±3σ.sup.up and TH.sup.down=μ.sup.down±3σ.sup.down respectively, in which TH.sup.up is the threshold of said positive half cycle interval; μ.sup.up is the mean value of said positive half cycle interval; σ.sup.up is the standard deviation of said positive half cycle interval; TH.sup.down is the threshold of said negative half cycle interval; μ.sup.down is the mean value of said negative half cycle interval; σ.sup.down is the standard deviation of said negative half cycle interval.

3. The lower limb spasticity measurement method as claimed in claim 2, wherein the threshold for said positive half cycle interval and the threshold for said negative half cycle interval are further defined as TH.sup.up=μ.sup.up±3σ.sup.up+Sv and TH.sup.down=μ.sup.down±3σ.sup.down−Sv respectively, in which Sv is a correction parameter that is obtained by calculating the operating speed of said motor and the stride length of said person using surface fitting technique.

4. The lower limb spasticity measurement method as claimed in claim 3, wherein the threshold for said positive half cycle interval and the threshold for said negative half cycle interval are further defined as TH.sup.up=μ.sup.up±3σ.sup.up+Sv+Sω.sup.up and TH.sup.down=μ.sup.down±3σ.sup.down−Sv+Sω.sup.down respectively, in which Sω.sup.up and Sω.sup.down are the sensitivity parameter for the gait activity machine in spasticity measurement.

5. The lower limb spasticity measurement method as claimed in claim 4, wherein said sensitivity parameter satisfies the equation of Sω.sup.up=(μ.sup.up−μ.sup.data)*ω and the equation of Sω.sup.down=(μ.sup.data−μ.sup.down)*ω, in which μ.sup.data is the overall mean value of the statistical distribution data; ω is the weight in the range from the most sensitive 0 to the least sensitive 1.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0009] FIG. 1 is a flow chart of a lower limb spasticity measurement method in accordance with the present invention.

[0010] FIG. 2 is a schematic structural view of a gait activity machine used in accordance with the present invention.

[0011] FIG. 3 is a scatter plot of motor torque versus time graph coordinates.

[0012] FIG. 4 is similar to FIG. 3, illustrating spasticity occurred in the lower limbs of the person.

[0013] FIG. 5 is a plot illustrating the surface correction of the correction parameter.

[0014] FIG. 6 is a scatter plot of motor torque versus time graph coordinates obtained after added correction parameter to the threshold.

[0015] FIG. 7 is a scatter plot of motor torque versus time graph coordinates obtained after added sensitivity parameter to the threshold.

DETAILED DESCRIPTION OF THE INVENTION

[0016] Referring to FIG. 1, a lower limb spasticity measurement method in accordance with the present invention comprises the steps as follows: [0017] a) Let a person enter a gait activity machine 10, as illustrated in FIG. 2, and then set the lower limbs of the person in a lower limb orthotic device 12. [0018] b) Start up a motor 14 of the gait activity machine 10 to drive the lower limb orthotic device 12, assisting activity of the lower limbs of the person. [0019] c) Get a statistical distribution data from the output of the torque variation of the motor 14 within a predetermined time, and then calculate the statistical distribution data to obtain a threshold.

[0020] As illustrated in FIG. 3, the statistical distribution data is divided into a positive half cycle interval and a negative half cycle interval. The projection of the positive half cycle interval and the projection of the negative half cycle interval on y-axis respectively form a normal distribution. A normal signal will fall within this normal distribution. Thereafter, use the concept of confidence interval to determine the data of one particular measurement point to be or not to be a normal signal. If the data of this particular measurement point is a normal signal, the data of this particular measurement point will fall within the range of the mean value of the positive, negative half cycle interval plus or minus three standard deviations. Therefore, in this step c), calculate the mean value and standard deviation of the positive and negative half cycle intervals, and then define the threshold for the positive and negative half cycle intervals using the concept of confidence interval, and thus, the following two equations are obtained:

TH.sup.up=μ.sup.up±3σ.sup.up

TH.sup.down=μ.sup.down±3σ.sup.down

in which TH.sup.up is the threshold of the positive half cycle interval; μ.sup.up is the mean value of the positive half cycle interval; σ.sup.up is the standard deviation of the positive half cycle interval; TH.sup.down is the threshold of the negative half cycle interval; μ.sup.down is the mean value of the negative half cycle interval; σ.sup.down is the standard deviation of the negative half cycle interval. [0021] d) Determine whether or not the output torque of the motor 14 is greater than the threshold? If the output torque is greater than the threshold, as indicated by P1 in FIG. 4, it means that the person gets spasticity. At this time, stop the motor 14 immediately. If the output torque is not greater than the threshold, it means the condition of the person is normal. At this time, let the motor 14 keep running.

[0022] On the other hand, since stride length varies widely from patient to patient and the operating speed of the motor 14 may also be differently set for different people, the present invention utilizes surface fitting technique to calculate the operating speed of the motor 14 and the stride length of the person so as to obtain a correction parameter (see FIG. 5). Thus, the threshold of the positive half cycle interval is corrected to become TH.sup.up=μ.sup.up±3σ.sup.up+Sv for the positive half cycle interval and TH.sup.down=μ.sup.down±3σ.sup.down−Sv for the negative half cycle interval, in which Sv is the correction parameter. Therefore, it can be seen from P2 in FIG. 6, it is assumed to change the operating speed of the motor 14 in the first 300 seconds, the threshold will be automatically corrected without needing recalibration.

[0023] Further, a sensitivity parameter can be added to the equation, enabling the threshold to be further corrected to become TH.sup.up=μ.sup.up±3σ.sup.up+Sv+Sω.sup.up for the positive half cycle interval and TH.sup.down=μ.sup.down±3σ.sup.down−Sv+Sω.sup.down for the negative half cycle interval, in which Sω.sup.up and Sω.sup.down are the sensitivity parameter for the gait activity machine in spasticity measurement. This sensitivity parameter satisfies the following equations:

Sω.sup.up=(μ.sup.up−μ.sup.data)*ω

Sω.sup.down=(μ.sup.data−μ.sup.down)*ω

in which μ.sup.data is the overall mean value of the statistical distribution data; ω is the weight in the range from the most sensitive 0 to the least sensitive 1. With this sensitivity parameter, it allows adjustment of the range of the threshold according to the condition of the person, as illustrated in FIG. 7, thus achieving the effect of changing the sensitivity of the measurement.

[0024] In conclusion, the lower limb spasticity measurement method of the invention utilizes the output torque of the motor 14 as signal source for measuring spasticity in the lower limbs of a person during activity without the use of additional sensors. Thus, the method of the invention effectively saves the cost of equipment. Further, during activity, the person can adjust the speed without re-calibration, and can also adjust the sensitivity of the measurement according to the person's personal needs, thereby enhancing the ease of use.