ReviewMethodsX2025
Sensor-driven control strategies for post-stroke shoulder rehabilitation exoskeletons: A systematic review.
Review in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- From Signals to Remaining Useful Life: Multimodal Sensor Fusion for Fault Diagnosis and Prognostics-Methods, Pitfalls, and Reporting Standards.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensor-driven shoulder exoskeletons are emerging as promising tools for post-stroke rehabilitation, offering scalable, adaptive, and patient-specific motor assistance. This systematic review analyzes 32 studies published between 2015 and April 2025, focusing on the integration of sensor modalities and control strategies in upper-limb exoskeletons targeting the shoulder complex. The review categorizes sensor types, including electromyography (EMG), inertial measurement units (IMUs), force/torque sensors, and kinematic sensors, and evaluates their role in motion tracking, user-intent detection, and feedback regulation. Control strategies are classified into five main groups: force- and admittance-based interaction control, adaptive and assist-as-needed control, human-in-the-loop control, passive support and gravity compensation, and machine-learning-based predictive control. Motor-driven actuation was the most prevalent approach, often paired with advanced control architectures. While multimodal sensor fusion enhances system responsiveness and personalization, most implementations remain in early development or validation stages, with limited clinical deployment. Challenges include sensor drift, bioelectrical signal variability, system complexity, and the need for regulatory approval. The review concludes by highlighting future directions in AI-driven control, wearable sensing, and closed-loop neurorehabilitation. These trends point toward a new generation of intelligent, user-centered exoskeletons capable of delivering high-quality therapy in both clinical and home settings.
Indexed as
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.