A Tensor based Approach using Multilinear SVD for Hand Gesture Recognition from sEMG signals

With the increasing number of sensors/ channels, hand gesture recognition from multiple channels has attracted the attention of the researchers in recent years. The analysis of a variety of gestures is still a challenging task in real-time applications such as in wearable devices because of the large number of channels used. This paper proposes a tensor-based approach using multilinear singular value decomposition (MLSVD) for hand gesture recognition where all available channels were used during training whereas only a single channel was used for recognition of new gestures. Tensor decompositions have very limited use in gesture recognition using surface Electromyography (sEMG) despite these signals naturally have multi-way structures. The sEMG data of different subjects were first segmented to reshape it as a fourth-order tensor of the form channels × time × gestures × subjects. The MLSVD was applied to model the tensor and extract features, which were then fed into various classifiers such as support vector machine (SVM), K-nearest neighbors (KNN), TreeBagger (TB), and dictionary learning (DL) classifiers in order to compare their performance. The proposed method was evaluated on three publicly available databases (NinaPro, CapgMyo (DB-a, DB-b and DB-c), CSL-HDEG) by performing intra-session, inter-session and inter-subject evaluations on each database. The experimental results indicated that the proposed method achieved the best accuracy (Acc) using DL, achieving Acc of 77.6% and 89.6% with CapgMyo DB-b and CSL-HDEMG databases, respectively, during inter-session evaluation, and 75.2%, 75.4%, 68.3%, and 67.7% with NinaPro, CapgMyo DB-b, CapgMyo DB-c, and CSL-HDEMG databases, respectively, during inter-subject evaluation. The proposed method performed better during both inter-session and inter-subject evaluations than the state-of-the-art methods.