Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network

Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data's temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Piezoelectric Sensor with a Helical Structure on the Thread Core

In this paper, we introduce a piezoelectric sensor curled on a thread core in a helical structure. In particular, a polyvinylidene fluoride film was curled and fixed on a thread core. A series of experiments were designed to deliver flexural loading to the piezoelectric sensor, to study its sensing characteristics. The experimental results show that the sensing output of the sensor is in phase with the applied flexural loading. In addition, the output voltage of the textile-based piezoelectric sensor was measured according to various flexural loadings. The flexural bending angle applied to the piezoelectric sensor is expected to be a power function of the voltage output. In addition, we demonstrate a smart textile by weaving the piezoelectric sensor.