Abstract
To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG–FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.
Highlights
There are still millions of amputees in the world
The bottom subgraph shows the comparison between the normalized FMG signal and the normalized envelope of the EMG signal; the correlation coefficients of this sample segment of the normalized FMG and EMG envelopes is 0.8763
Our design provides an excellent choice for prosthetic control, because high accuracy can be achieved by using simple TD features and we have demonstrated the performance of the sensor, there are still many aspects that need to be accessed
Summary
There are still millions of amputees in the world. Currently, the only way to recover is through a prosthesis. The idea of an intelligent prosthetic usually refers to the “intent control” of the prosthetic limbs, which means the user controls the prosthesis through the brain or healthy body movement intention rather than direct body motions [2]. Based on these high-level intent commands, there are a number of control methods and theories to accomplish the low-level control of rehabilitation and auxiliary robots [3,4], they work together to achieve full control of the prosthesis.
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