Environment-Aware Locomotion Mode Transition Prediction System

Current research suggests the emergent need to predict locomotion mode (LM) transitions to allow a natural and smooth response of lower limb active assistive devices such as prostheses and orthosis for daily life locomotion assistance. The purpose of this work is to develop an automatic, user-independent system using an environment-aware strategy to predict LM transitions. We applied an infrared laser system to measure the distance between the user and the terrain ahead. A three-layer decision tree with heuristic decision rules only dependent on infrared laser features was implemented to predict LM transitions. The prediction system was validated with 10 healthy subjects that performed 8 LM transitions in different terrains (level-terrain, stairs, and ramps). The results showed a prediction accuracy above 80% for all LM transitions, achieving 100% prediction success for transitions ramp/stair descend to level-terrain. All the LM transitions were predicted with high prediction time (> 0.73 seconds) which empowers its integration on assistive devices control strategies. The prediction system accurately and time-effectively predicts 8 different LM transitions only using the infrared laser sensor. It approached indoor and outdoor terrains, relevant for daily-life locomotion applications, and was more polyvalent and effective than previous environment-aware systems.