Deep-Learning-Based Emergency Stop Prediction for Robotic Lower-Limb Rehabilitation Training Systems

Abstract

Robotic lower-limb rehabilitation training is a better alternative for the physical training efforts of a therapist due to advantages, such as intensive repetitive motions, economical therapy, and quantitative assessment of the level of motor recovery through the measurement of force and movement patterns. However, in actual robotic rehabilitation training, emergency stops occur frequently to prevent injury to patients. However, frequent stopping is a waste of time and resources of both therapists and patients. Therefore, early detection of emergency stops in real-time is essential to take appropriate actions. In this paper, we propose a novel deep-learning-based technique for detecting emergency stops as early as possible. First, a bidirectional long short-term memory prediction model was trained using only the normal joint data collected from a real robotic training system. Next, a real-time threshold-based algorithm was developed with cumulative error. The experimental results revealed a precision of 0.94, recall of 0.93, and F1 score of 0.93. Additionally, it was observed that the prediction model was robust for variations in measurement noise.

Deep Image Processing for Lower Limb Rehabilitation Training Action and Effect Recognition: GaitSet Algorithm and Full-Field Optical Flow Approaches

“…The criticality of lower limb movement in facilitating daily human activities cannot be understated. Activities ranging from walking and running to climbing stairs and jumping are contingent upon the coordinated actions of the lower limbs [6][7][8][9][10][11]. Consequently, for patients navigating the recovery journey post-surgery or injury, accurate and timely assessment of rehabilitation outcomes is crucial as it profoundly influences adjustments to the rehabilitation process and, ultimately, the realization of rehabilitation goals [12][13][14][15][16].…”

Intervention Effect of Rehabilitation Robotic Bed Under Machine Learning Combined With Intensive Motor Training on Stroke Patients With Hemiplegia

“…Nowadays, machine learning-based algorithms can train the human body model directly, which makes the prediction and recognition of human intentions more accurate in human–computer collaboration. The algorithm based on machine learning captures, learns, and predicts human actions by visual sensors to identify the operator's intention, so as to improve the coordination between patients and robots (Cha et al, 2021 ). Therefore, developing a lower limb rehabilitation robot with independent intellectual property rights, simple structure, low cost, and convenient operation will be of great significance to the development of the rehabilitation medical robot industry in China.…”