A Tensor Approach for Activity Recognition and Fall Detection Using Wearable Inertial Sensors

Tensors are high order muti-way arrays which extend vectors and matrices to higher, greater than three, dimensions. Rather than vectorizing data, tensors allow its representation in its original raw form, hence offering better understanding and interpretation of the underlying data structure. In this paper, we propose an activity recognition and fall detection approach which makes use of tensors as the mean for wearable inertial sensors data representation. To reduce the tensor size and only keep important information, we project the tonsorial data into a lower dimensional space using multilinear principle component analysis. To evaluate the proposed approach, we carry out experiments on a publicly available large fall and activities dataset. The results show the defectiveness of tensorizing data compared to its processing through concatenated vectors.