Using accelerometer and EMG signals to estimate arm motion Dissertation

Using accelerometer and EMG signals to estimate arm motion - Dissertation Example 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 p rovide 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. (Fougner, et al., 2011). The networks can be visualized as a feed forward neural network which is trained for time series prediction. The architecture has continuous inputs that are delayed and sent into the network. In this study, the inputs to this neural network architecture were delayed time series; that is the previous values of 10 channels for 4 for EMG and 6 channels for accelerometers. The measured goniometer signals served as desired output of the TDANN and also as the present state of the time series. The usage of one- layer time delay artificial neural n etwork which is a feed forward structure allow us to predict continuous trajectories which is advantageous for a coordinated and simultaneous control of multiple degrees of freedom in a natural manner. The use of delayed input signals enabled the neural network to capture dynamic input-output properties and account for the delay between the onset of the muscle activity and mechanical arm movement (the activation of the hardware motors in the prosthesis) (Fougner, et al., 2011). TDANN have also an advantage of rapid training time when compared to the dynamic neural networks with recurrent connections. We investigated using a TDANN to predict the elbow flexion degrees, wrist flexion degrees, wrist deviation degrees and forearm rotation degrees based on EMG information from the available intact muscles in transhumeral amputation patients. The EMG information was combined with the accelerometer information about the upper arm and the upper trunk orientations. A one layer time-delay arti ficial neural network (TDANN) was created using Matlab’s neural network toolbox; this network was used to capture the time-series data (EMG and accelerometer signals as an input with the goniometers and torsiometer signals as output). The size of the hidden layer was set by default to be 10 neurons and the network was trained then the hidden layer size was increased to 25 then to 35 and the performance of the network was monitored. TDANN with 35 neuron hidden layer size was then chosen. The network used 2 input delays to allow building a dynamic network, which has memory so