Multiple Inertial Measurement Unit Combination and Location for Center of Pressure Prediction in Gait - Wu, Chao-Che; Chen, Yu-Jung; Hsu, Che-Sheng; Wen, Yu-Tang; Lee, Yun-Ju (Frontiers in Bioengineering and Biotechnology, 2020)
Center of pressure (COP) during a gait cycle indicates crucial information regards to fall risk such as balance capacity. The drawbacks of conventional research instruments include inconvenient use during the activities of daily living and expensive costs. The present study illustrated the promising fall relevant information predicted by acceleration and angular velocity data from different placement sensors with machine learning techniques. This approach is inspired by the emerging machine learning technique, specifically the long short-term memory (LSTM), which is often used in time series data and aims to decrease the burdensome of the user while using the novel wearable technology. The Jaccard similarity coefficient, which implies the consistency of profile alignment between prediction and real situation, achieved 94% accuracy in the walking direction. Furthermore, the number of sensors used and the placement influenced the feasibility of an application. The outcome revealed that the accuracy could exceed 90% with only one sensor placed on foot in walking direction, and the toe would be the best location to recommend for sensor placement. To examine the performance of machine learning, the current study employed two parameters from different perspectives. One is a commonly used parameter, which represented the error and the other investigated the similarity between the prediction and golden truth. From a similarity perspective, the parameter can be used as a metric to assess the consistency of profile alignment.