A Flexible Cognitive Built Wearable Platform Using Multimodal Therapeutic Sensors and Reinforcement Learning Technique for Upper-Limb Stroke Rehabilitation
Oct 2026· Acupuncture & Electro-Therapeutics Research· 13 references
Stroke Rehabilitation and Recovery
Abstract
Stroke is one of the most common long-term motor disabilities which requires a personalized rehabilitation strategy capable of delivering reliable therapeutic interventions with continuous assessment. This paper proposes the integration of the inertial measurement unit, force-sensing technology, and electromyography (EMG), along with an artificial intelligence (AI)-based, cognitive–adaptive control platform and intelligent framework within a closed loop for precision neurorehabilitation. This research proposes a methodology to continuously monitor patients’ motor functions and compensate for movements, dynamically optimize rehabilitation, and detect fatigue intensity in real time. This proposed architecture combines deep learning-based movement analysis with multimodal sensor fusion and reinforcement learning-based adaptive control. This is done alongside cloud-based facility analytics for long-term longitudinal assessment and clinic medical assistant therapy management, and a lightweight edge computing module for feature extraction and cognitive decision-making. A total of 36 post-stroke patients with upper-limb hemiplegia were used in an 8-week clinical study, in which Fugl–Meyer assessment (FMA) scores increased by an average of 87.9%, EMG muscle activation by 42.8%, and therapy efficiency improved by 31.6%. Therapy efficiency improved by 32.8% and 21.9% rehabilitation reduction when compared with traditional physiotherapy. The composite performance index has rapidly increased to 0.949, indicating overall rehabilitation efficiency, with repeated measures analysis of variance confirming a statistically significant trend. Thus, the home-based stroke recovery with substantial potential for research constrains the healthcare environment with smart adaptive and real-time patient-specific optimization, which is proposed in this AI framework as an interpretable clinical deployment solution.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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