Robot-Assisted Gait Self-Training: Assessing the Level Achieved

• Main Authors: Andrea Scheidig, Benjamin Schütz, Thanh Quang Trinh, Alexander Vorndran, Anke Mayfarth, Christian Sternitzke, Eric Röhner, Horst-Michael Gross
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Sensors
• Subjects: robot-assisted gait training; self-training; feedback to the patient; autonomous use; real clinical environment conditions
• Online Access: https://www.mdpi.com/1424-8220/21/18/6213

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists’ and patients’ point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients?