Using accelerometer and EMG signals to estimate arm motion

Using accelerometer and EMG signals to estimate arm motion Dissertation example
Masters
Dissertation
Engineering and Construction
Pages 7 (1757 words)
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The purpose of this study is to understand the feasibility of predicting arm movement trajectories based on features that are extracted from EMG signals, as a result of muscular action, and accelerometer input. as a result of acceleration of limbs to movement…

Introduction

Using accelerometer and EMG signals to estimate arm motion

This study investigates a means to overcome this degradation through use of EMG signals combined with accelerometer signals to measure the upper arm static and dynamic acceleration. Both EMG signal and accelerometer inputs are fed into an artificial neural network. The artificial neural network continuously predicts arm movement trajectories. An offline time-delay Artificial Neural network (TDANN) is employed to predict the movement trajectories of the arm. The accuracy of prediction was judged by using a set of goniometer readings which provides the changes in the angles of the upper limb. All data was processed in the Matlab environment. The TDANN deployed was developed in the neural network toolbox present within the Matlab environment. The developed neural network was optimized and trained with different sets of inputs, and the results for each of the trails was noted. The results obtained clearly demonstrated that accelerometers are able to enhance pattern recognition and thus provide better prosthesis control. Neural Network Optimization and Prediction Performance The neural network structure used for the study is the TDANN. TDANN is a neural network architecture whose primary purpose is to function on continuous data. The major advantage of using TDANN on continuous data is its ability to adapt the network’s weights and activation function online by use of back propagation error method. ...
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