Modified Nonnegative Matrix Factorization Using the Hadamard Product to Estimate Real-Time Continuous Finger-Motion Intentions

Abstract

In daily life, most hand movements involve the simultaneous activation of multiple fingers. Models generated by semiunsupervised learning in which only individual finger activation data are used in training have recently been suggested for simultaneous and proportional control of prosthetic robot hands. Although training with many datasets should be avoided, simultaneous activation data need to be used, for example, when the model estimation of the simultaneous activation is very poor or highly coupled among the degrees-of-freedoms (DOFs). In this paper, we propose a method for generating a model using any type of activation data (individual, simultaneous, or both) by modifying the nonnegative matrix factorization (NMF) with the Hadamard product (HP). The model provided by this method is called NMF-HP. NMF-HP has two advantages: First, it can use simultaneous activation data for training. Second, NMF-HP decouples coupled DOFs by forcing nonactive DOFs to be zero during the training phase. NMF-HP was tested in two cases (trained with only individual activation data and trained with both individual and simultaneous activation data) in offline and online experiments. In the offline test, NMF-HP outperformed the conventional semiunsupervised models for the simultaneous activation of the fingers. In the online test, NMF-HP was significantly better than NMF in the estimation of finger-motion intentions. This result contrasts with that of a previous study in which performance in the online test revealed a little difference between the models, possibly due to the human-embedded control. Thus, the result of this paper indicates that using simultaneous activation data and reducing the coupling among DOFs may be effective in enhancing the performance of the real-time control of a prosthetic robot hand.