Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN

Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.

Response Evaluation of Rollover Recognition in Myoelectric Controlled Orthosis Using Pneumatic Rubber Muscle for Cancer Bone Metastasis Patient

The myoelectric controlled rollover support orthosis we have been developing for use in bone cancer metastasis requires high accuracy and quick response in signal processing to recognize movement. We quantitatively evaluated the response performance of recognizing rollover using our original Micro Macro Neural Network (MMNN) algorithm. Required response time was calculated as 60 ms by measuring contraction time for the muscle used in the orthosis to support rollover. TheMMNN recognized rollover 65 ms before it started. Rollover was recognized 5 ms after a myoelectric signal was generated, so the MMNN response was sufficient for the muscle to finish contraction before rollover started.

Repeatability analysis of rollover recognition in changing myoelectric electrode condition

Bone cancer metastasis patients feel severe pain while performing rollover movement. We have been developing a robotic trunk orthosis to support the rollover movement in bed for bone cancer metastasis patients. In this support system, the myoelectric signal is used to recognize the start timing of the rollover movement. However, the characteristics of the myoelectric signal can change easily from long-term use and due to electrode misalignment. In this paper, the effects of long-term use and electrode misalignment in rollover movement were analyzed. It was found that continuous usage of less than 18 hours was suitable. In addition, the electrode was needed to be attached around the ASIS from 0 to 23 (deg) to obtain a large potential and quick response signal.