Objectives To determine the acceptability and feasibility of the use of a robotic walking aid to support the work of physiotherapists in reducing fear of falling in the rehabilitation of elderly patients with 'psychomotor disadaptation' (the most severe form of post-fall syndrome). Study design 20 participants with psychomotor disadaptation admitted to an academic rehabilitation ward were randomised to receive physiotherapist care supported by the SafeWalker® robotic walking aid or standard care only, for ten days. SafeWalker® supports the body weight whilst securing postural stability without relying on upper body strength or high cognitive demand. Main outcome measures The primary outcome was the feasibility and acceptability of rehabilitation sessions at five and ten days based on (i) questionnaires completed by patient and physiotherapist, (ii) the number of steps performed during sessions, (iii) replacement of a robotic session by a conventional one. Results The mean age of the participants was 85.2 years. They had lost their ability to perform some basic living activities. Patients in the intervention group found that the rehabilitation sessions were easier (p = 0.048). No robotic rehabilitation session had to be replaced by conventional rehabilitation. There were no statistical differences between the two groups on the other outcome measures. Conclusion We demonstrated the feasibility and acceptability of the use of a robotic walking aid from the perspective of both older individuals and physiotherapists. This could fill the gap between devices that fully compensate for walking and those which allow patients to maintain residual mobility.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.maturitas.2018.11.008DOI Listing

Publication Analysis

Top Keywords

robotic walking
16
walking aid
16
fear falling
8
ten days
8
outcome measures
8
feasibility acceptability
8
rehabilitation sessions
8
rehabilitation
7
robotic
6
aid
4

Similar Publications

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.

Sensors (Basel)

January 2025

Centre for Automation and Robotics (CAR UPM-CSIC), Escuela Técnica Superior de Ingeniería y Diseño Industrial (ETSIDI), Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain.

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations.

View Article and Find Full Text PDF

Gait disturbance is one of the most common symptoms in patients with Parkinson's disease (PD) that is closely associated with poor clinical outcomes. Recently, video-based human pose estimation (HPE) technology has attracted attention as a cheaper and simpler method for performing gait analysis than marker-based 3D motion capture systems. However, it remains unclear whether video-based HPE is a feasible method for measuring temporospatial and kinematic gait parameters in patients with PD and how this function varies with camera position.

View Article and Find Full Text PDF

To reduce hip joint muscles' activation during walking with an active hip exoskeleton. Few studies examine the optimal active assistance timing of the hip exoskeleton based on muscle activation characteristics. Sixteen gender-balanced healthy adults (mean age 28.

View Article and Find Full Text PDF

Human-Inspired Gait and Jumping Motion Generation for Bipedal Robots Using Model Predictive Control.

Biomimetics (Basel)

January 2025

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu 808-0135, Japan.

In recent years, humanoid robot technology has been developing rapidly due to the need for robots to collaborate with humans or replace them in various tasks, requiring them to operate in complex human environments and placing high demands on their mobility. Developing humanoid robots with human-like walking and hopping abilities has become a key research focus, as these capabilities enable robots to move and perform tasks more efficiently in diverse and unpredictable environments, with significant applications in daily life, industrial operations, and disaster rescue. Currently, methods based on hybrid zero dynamics and reinforcement learning have been employed to enhance the walking and hopping capabilities of humanoid robots; however, model predictive control (MPC) presents two significant advantages: it can adapt to more complex task requirements and environmental conditions, and it allows for various walking and hopping patterns without extensive training and redesign.

View Article and Find Full Text PDF

Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!