There are multiple attempts to decrease costs in the healthcare system while maintaining a high treatment quality. Digital therapies receive increasing attention in clinical practice, mainly relating to home-based exercises supported by mobile devices, eventually in combination with wearable sensors. The aim of this study was to determine if patients following total hip arthroplasty (THA) could benefit from gait training on crutches conducted by a mobile robot in a clinical setting. Method: This clinical trial was conducted with 30 patients following total hip arthroplasty. Fifteen patients received the conventional physiotherapy program in the clinic (including 5 min of gait training supported by a physiotherapist). The intervention group of 15 patients passed the same standard physiotherapy program, but the 5-min gait training supported by a physiotherapist was replaced by 2 × 5 min of gait training conducted by the robot. Length of stay of the patients was set to five days. Biomechanical gait parameters of the patients were assessed pre-surgery and upon patient discharge. Results: While before surgery no significant difference in gait parameters was existent, patients from the intervention group showed a significant higher absolute walking speed (0.83 vs. 0.65 m/s, p = 0.029), higher relative walking speed (0.2 vs. 0.16 m/s, p = 0.043) or shorter relative cycle time (3.35 vs. 3.68 s, p = 0.041) than the patients from the control group. Conclusion: The significant higher walking speed of patients indicates that such robot-based gait training on crutches may shorten length of stay (LOS) in acute clinics. However, the number of patients involved was rather small, thus calling for further studies.

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?