A Time-Scalable Posture Detection Algorithm for Paraplegic Patient Rehabilitation Using Exoskeleton-Type Wearable Robots

Abstract

Traditionally, paraplegic patients have relied on a wheelchair to travel. However, new developments in walking assistance technology have led to promising exoskeleton-type wearable robots that can help paraplegic patients walk. Operation of this new robotic device requires that patients have appropriate training to ensure safe and optimal use. Here, we propose an algorithm that can optimize rehabilitation outcomes by comparing posture data generated during the rehabilitation of a paraplegic patient wearing a body-tracking sensor with reference posture data. The proposed algorithm guarantees a certain level of accuracy when comparing rehabilitation and reference posture data. It can also correct for timescale differences between reference and rehabilitation data to ensure a high level of accuracy. Compared with other algorithms that perform similar functions, this algorithm can accommodate different postures, including those associated with walking, and has the advantage of being able to derive the desired results by setting usability features in an intuitive way.