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Human movement is dictated by muscle contractions and the forces muscles produce around joints. A complete understanding of human movement requires an understanding of forces generated by contracting muscles and their effects on joints. Essential to muscle contraction is the transmission of electrical impulses from the cerebral cortex, down the spinal chord, finally reaching the motor neurons. A muscle ' s electromyographical (EMG) signal has been used extensively to provide insight into the dynamics of muscle behavior. Specifically, EMG signal detection has proven the most useful in applications that involve the indication of muscle activation, the determination of muscular fatigue, and the relationship between the EMG signal and muscle force. However, since there has been no consistent use of EMG signal detection, the use of EMG signal analysis for the determination of muscle force has not been widely explored. Also, unwanted frequencies from neighboring muscles, electrical fields, and the equipment used to detect the EMG signal itself corrupt the signal of interest. This makes it very difficult to isolate a specific muscle and study its EMG signal-to-force relationship. The purpose of this study is to correlate a contracting muscle ' s EMG signal with the generated torque and an estimate of the muscle's fatigue using an artificial neural network. An artificial neural network is a form of artificial intelligence software that is able to learn and detect patterns in order to generalize and classify data. Artificial neural networks have the advantage that they are designed to .mimic a natural neural network. Using EMG signals and torque data collected from four human subjects while they performed maximal effort contractions of the knee joint, fatigue was assumed to increase

An artificial neural network to predict the effects of fatigue on the electromyographical signal and generated torque of knee extensor muscles