You are currently viewing a new version of our website. To view the old version click .
Biosensors
  • This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
  • Review
  • Open Access

25 December 2025

Active Rehabilitation Technologies for Post-Stroke Patients

,
,
,
,
,
,
,
,
and
1
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
2
Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK
3
Brain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
4
Department of Physical Medicine and Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing 100084, China
This article belongs to the Special Issue Development Trends of AI-Enabled Biomedical Biosensors

Abstract

Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active rehabilitation methods can respond to patients’ motor intentions in real-time and significantly increase motivation and engagement, leading to efficient utilization of critical recovery windows and better rehabilitation outcomes. In this review, we focus on the physiological basis of active rehabilitation, including mechanisms of neuroplasticity, and discuss recent advances in intent detection and feedback devices. We also examine treatment options for different stages of stroke recovery, providing a comprehensive reference for engineers to design optimized rehabilitation techniques and for clinicians to select appropriate rehabilitation protocols. These developments create new opportunities to improve the lives of stroke patients and offer greater hope for their recovery.

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.