Applying Ensemble Neural Networks to an Inverse Problem Solution to Prosthetic Socket Pressure Measurement

Ensemble neural networks are commonly used as a method to boost performance of artificial intelligence applications. By collating the response of multiple networks with differences in composition or training and hence a range of estimation error, an overall improvement in the appraisal of new problem data can be made. In this work, artificial neural networks are used as an inverse-problem solver to calculate the internal distribution of pressures on a lower limb prosthetic socket using information on the deformation of the external surface of the device. Investigation into the impact of noise injection was studied by changing the maximum noise alteration parameter and the differences in network composition by altering the variance around this maximum noise value. Results indicate that use of ensembles of networks provides a meaningful improvement in overall performance. RMS error expressed as a percentage of the total applied load was 3.86% for the best performing ensemble, compared to 5.32% for the mean performance of the networks making up that ensemble. Although noise injection resulted in an improvement in typical network estimates of load distribution, ensembles performed better with low noise and low variance between network training patterns. These results mean that ensembles have been implemented in the research tool under developmen