ABSTRACT
Rapid and accurate recognition of human action and road condition is a foundation and precondition of implementing self-control of intelligent prosthesis. In this paper, a Gaussian mixture model and hidden Markov model are used to recognize the road condition and human motion modes based on the inertial sensor in artificial limb (lower limb). Firstly, the inertial sensor is used to collect the acceleration, angle and angular velocity signals in the direction of , and axes of lower limbs. Then we intercept the signal segment with the time window and eliminate the noise by wavelet packet transform, and the fast Fourier transform is used to extract the features of motion. Then the principal component analysis (PCA) is carried out to remove redundant information of the features. Finally, Gaussian mixture model and hidden Markov model are used to identify the human motion modes and road condition. The experimental results show that the recognition rate of routine movement (walking, running, riding, uphill, downhill, up stairs and down stairs) is 96.25%, 92.5%, 96.25%, 91.25%, 93.75%, 88.75% and 90% respectively. Compared with the support vector machine (SVM) method, the results show that the recognition rate of our proposed method is obviously higher, and it can provide a new way for the monitoring and control of the intelligent prosthesis in the future.
ABSTRACT
The purpose of this study is to compare the road conditions (straight road, winding road and hill road) with the hand grip pressure force and muscle fatigue for male and female drivers. Ten subjects were participated in this study. The force measurement and electromyography (EMG) responses were taken and evaluated by using the tactile grip and pressure measurement (Grip System) and Electromyography (EMG) device. The result indicated that the winding road produced more muscle fatigue and high hand grip pressure force compared than downhill road, hill up road, and straight road for both male and female subjects. The result compared the muscle fatigue and hand grip pressure force between the first 15 minutes and last 15 minutes of driving activity. The muscle fatigue increasingly high for the last 15 minutes compared to first 15 minutes. However, the hand grip pressure forces become high during the winding road for first 15 minutes of driving session. The muscle fatigue become high as the hand grip pressure force value is high. Furthermore, the male drivers exert higher hand grip pressure force and higher muscle fatigue compared to female drivers. This study can be used as a guideline for the future studies, primarily in solving the driving fatigue problem among the Malaysian’s drivers. The method of this study could also be used for early detection of driver fatigue issues. Indirectly, the findings could reduce the number of car accidents in Malaysia.