Recognition of walking environments and gait period by surface electromyography

Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic (EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography (sEMG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human-robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis (VM) and gastrocnemius (GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact (preHC) and 71.7% in pre-toe-off (preTO) for environment classification, and 68.0% for gait period classification, when using only the sEMG sensor. The two effective sEMG feature combinations were “mean absolute value (MAV), zero crossings (ZC)” and “MAV, waveform length (WL)”, and the “MAV, ZC” results were 80.0%, 77.1%, and 75.5%. These results suggest that the sEMG signal can be effectively used to control an exoskeleton robot.

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