A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

Abstract

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.

Gait Phase Recognition for Exoskeleton Control Using Adaptive Neuro Fuzzy Inference System

“…The network was taught to detect the stance phase and swing phase. Its results showed a 2.8% error [15]. Despite the high efficiency of the method, it was possible to recognize only two phases of walking, which in the case of the support device control is insufficient.…”

“…use the collected data from all eight sensors of the exoskeleton foot to determine the stance and swing phase. If the data from each sensor exceeds the predetermined threshold, it means that the exoskeleton is in the stance phase [15]. The use of eight pressure sensors to determine two gait phases is definitely uneconomical, as such a system can be replaced with one sensor on each foot.…”

“…Significantly better results are obtained by using artificial neural networks using pressure sensor or accelerometer data [15]. The authors used four pressure sensors that became inputs for the multi-layer perceptron (MLP).…”

“…Similar neural network, which instead used accelerometer data, achieved significantly higher error values calculated with respect to gait pattern. Additionally, the accelerometers introduced recognition delays of over 120ms [15].…”

“…Much previous work uses at least four FSR sensors in their systems, which are typically located in four distinct places: under the heel, 1st, 4th or 5th metatarsal bones, and under the hallux [2][13] [15]. Jung et.…”