IPTGNet: an adaptive multi-task recognition strategy for human locomotion modes

Complexities in processing human motion are possessed by lower limb exoskeletons. In this paper, a multi-task recognition model, IPTGNet, is proposed for the human locomotion modes. Temporal convolutional network and gated recurrent unit are parallelly fused through the dynamic tuning of hyperparameters using the improved particle swarm optimization algorithm. The experimental results demonstrate that faster and more stable convergence is achieved by IPTGNet with a recognition rate of 99.47% and a standard deviation of 0.42%. Furthermore, a finite state machine is utilized for incorrection of transition states. An innovative multi-task recognition of lower limb exoskeleton is provided by this paper.