BIO‐inspired fuzzy inference system—For physiological signal analysis

When a person's neuromuscular system is affected by an injury or disease,Activities‐for‐Daily‐Living(ADL),such as gripping,turning,and walking,are impaired.Electroen-cephalography(EEG)and Electromyography(EMG)are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject,and they are used in Brain–Computer Interface(BCI)or robotic rehabilitation systems.However,existing BCI or robotic rehabilitation systems use signal classification technique limitations such as(1)missing temporal correlation of the EEG and EMG signals in the entire window and(2)overlooking the interrelationship between different sensors in the system.Furthermore,typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions;(3)their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals.A novel classification model,named BIOFIS is proposed,which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships.It ex-plores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory(LSTM)block.The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward,grip and raise,lower and release,and reverse.The system can achieve 98.6%accuracy for a 4‐way action using EEG data and 97.18%accuracy using EMG data.Moreover,even without the dominant signal,the accuracy scores were 90.1%for the EEG data and 85.2%for the EMG data.The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

高级离散时间控制 设计和应用

离散时间系统是按预先设定的算法规则，将输入离散时间信号转换为所要求的输出离散时间信号的特定功能装置。可以分为无记忆系统和记忆系统两类：如果系统的输出离散信号只决定于同一时刻的输入信号，而与过去的状态无关，这个系统称为无记忆系统；反之，如果系统与过去的工作状态有关，则称为记忆系统，例如含有寄存器的系统；也可以分为线性离散系统和非线性离散系统两类。