A fuzzy convolutional attention-based GRU network for human activity recognition.

Human activity recognition has become a pillar of today intelligent Human–Computer Interfaces as it typically provides more comfortable and ubiquitous interaction. This paper proposes a novel fuzzy-based deep learning-based algorithm to predict future sequences of activities from a given sequence of daily living activities of a subject wearing a lower limb exoskeleton. The engineering application concerns the challenging task of recognizing locomotion activities of the wearer in real-time, which is needed to ensure appropriate control of the robot during daily living activities. Indeed, real-time locomotion activity recognition is very challenging for controlling lower-limb exoskeletons. The model proposes a new adaptive kernel, based on the data features derived from the fuzzy rules on the input sequences to enrich the features of the activity sequences. Then, a CNN is applied to extract local subsequences from the whole sequences to identify local patterns in the convolution window. Finally, an attention-based GRU is incorporated into the model to extract meaningful parts of the time-series sequences. The results show high accuracy in the estimation of the transition between gait modes which is critical to ensure smooth control of the exoskeleton. The performance of the model is evaluated using the dynamic activity data gathered from different subjects. The proposed model outperforms the traditional models used in the literature. [ABSTRACT FROM AUTHOR]