Neurological disorders often cause severe upper limb motor impairments that restrict independence and quality of life. Robot-assisted rehabilitation enables high-intensity, task-oriented, and quantifiable training. One key feature, gravity compensation (GC), reduces the muscular effort needed to lift the limb and supports voluntary movement by offsetting the weight of the arm. This systematic review aimed to identify the types of GC strategies used in upper limb rehabilitation robots and assess clinical evidence on their effectiveness for improving motor outcomes. A search of PubMed, Scopus, Web of Science, and IEEE Xplore (January 2005–May 2025) identified 60 eligible studies: 23 describing GC implementation and 40 reporting clinical results. GC was implemented into exoskeletons, end-effectors, and sling-suspension systems through passive mechanical designs or active, model-based, and adaptive control algorithms. However, few studies reported key technical parameters such as controller algorithms, loop frequency, or tuning procedures, and only one addressed the control system stability. Clinically, GC-assisted training improved arm movement and range of motion, with greater effects in participants with higher impairment levels. However, the functional gains were modest and not superior to conventional or other robotic therapies. Substantial heterogeneity in training protocols and participants’ demographics further limits direct comparison among GC strategies. Overall, the relative effectiveness of robot-assisted GC across devices remains unclear. Standardized reporting and more clinical trials are needed to compare GC strategies within and between different types of robots.
Mendez et al. (2026) studied this question.