ReviewJournal of rehabilitation and assistive technologies engineering
Technological advances in lower-limb tele-rehabilitation: A review of literature.
Review in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Effective Interventions in Restoring Mobility and Relieving Pain After Lower Limb Amputation.Journal of orthopaedics and sports medicine · 2026Article
- Effective real-time self-rehabilitation exercise monitoring and correctness system for low back pain management.Scientific reports · 2026Article
- Fully Screen-Printed Pressure Sensing Insole-From Proof of Concept to Scalable Manufacturing.Sensors (Basel, Switzerland) · 2026Article
- Feasibility and usability of a wearable sensor-based telerehabilitation program for home-based exercise in patients with lower limb amputation: a pilot study.Frontiers in rehabilitation sciences · 2026Article
- Toward Home-Based Telerehabilitation for Cerebral Palsy Patients: A Qualitative Study on Feasibility, Barriers and Facilitators.Health science reports · 2025Article
- Validation of a Commercially Available IMU-Based System Against an Optoelectronic System for Full-Body Motor Tasks.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tele-rehabilitation is a healthcare practice that leverages technology to provide rehabilitation services remotely to individuals in their own homes or other locations. With advancements in remote monitoring and Artificial Intelligence, automatic tele-rehabilitation systems that can measure joint angles, recognize exercises, and provide feedback based on movement analysis are being developed. Such platforms can offer valuable information to clinicians for improved care planning. However, with various methods and sensors being used, understanding their pros, cons, and performance is important. This paper reviews and compares the performance of recent vision-based, wearable, and pressure-sensing technologies used in lower limb tele-rehabilitation systems over the past 10 years (from 2014 to 2023). We selected studies that were published in English and focused on joint angle estimation, activity recognition, and exercise assessment. Vision-based approaches were the most common, accounting for 42% of studies. Wearable technology followed at approximately 37%, and pressure-sensing technology appeared in 21% of studies. Identified gaps include a lack of uniformity in reported performance metrics and evaluation methods, a need for cross-subject validation, inadequate testing with patients and older adults, restricted sets of exercises evaluated, and a scarcity of comprehensive datasets on lower limb exercises, especially those involving movements while lying down.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.