Background: Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision.
Objective: The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment.
Methods: A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience.
Results: Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation.
Conclusions: The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/THC-171403 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Ultra-Flexible High-Linearity Silicon Nanomembrane Synaptic Transistor Array.
Adv Mater
January 2025
School of Electronic and Computer Engineering, Peking University, Shenzhen, 518055, China.
The increasing demand for mobile artificial intelligence applications has elevated edge computing to a prominent research area. Silicon materials, renowned for their excellent electrical properties, are extensively utilized in traditional electronic devices. However, the development of silicon materials for flexible neuromorphic computing devices encounters great challenges.
View Article and Find Full Text PDFQuantum machine learning for Lyapunov-stabilized computation offloading in next-generation MEC networks.
Sci Rep
January 2025
Purwanchal Campus Institute of Engineering, Tribhuvan University, Kirtipur, Nepal.
Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov optimization theory to propose a novel quantum machine learning framework for stabilizing computation offloading in next-generation MEC systems. Our approach leverages hybrid quantum-classical neural networks to learn optimal offloading policies that maximize network performance while ensuring the stability of data queues, even under dynamic and unpredictable network conditions.
View Article and Find Full Text PDFTask offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning.
Sci Rep
January 2025
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China.
Mobile edge computing offloads compute-intensive tasks generated on mobile wireless devices (WD) to edge servers (ES), which provides mobile users with low-latency computing services. Opportunistic computing offloading is effective to enhance computing performance in dynamic edge network environments; however, careless offloading of tasks to ESs can lead to WDs preempting network computing resources with limited bandwidth, thereby resulting in inefficient allocation of computing resources. To address these challenges, this paper proposes the density clustering and ensemble learning training-based deep reinforcement learning (DCEDRL) method for task offloading decision-making in mobile edge computing (MEC).
View Article and Find Full Text PDFA Study on Real-time Detection of Rice Diseases in Farmlands Based on Multi-dimensional Data Fusion.
Plant Dis
January 2025
Biotechnology, plant protection, Nongsheng Group C735, Zijin Campus, Zhejiang University, Hangzhou, Zhejiang, China, 310058;
To meet the need of crop leaf disease detection in complex scenarios, this study designs a method based on the computing power of mobile devices that ensures both detection accuracy and real-time efficiency, offering significant practical application value. Based on a comparison with existing mainstream detection models, this paper proposes a target detection and recognition algorithm, TG_YOLOv5, which utilizes multi-dimensional data fusion on the YOLOv5 model. The triplet attention mechanism and C3CBAM module are incorporated into the network structure to capture connections between spatial and channel dimensions of input feature maps, thereby enhancing the model's feature extraction capabilities without significantly increasing the parameter count.
View Article and Find Full Text PDFBiologically inspired microlens array camera for high-speed and high-sensitivity imaging.
Sci Adv
January 2025
Department of Bio and Brain engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
Nocturnal and crepuscular fast-eyed insects often exploit multiple optical channels and temporal summation for fast and low-light imaging. Here, we report high-speed and high-sensitive microlens array camera (HS-MAC), inspired by multiple optical channels and temporal summation for insect vision. HS-MAC features cross-talk-free offset microlens arrays on a single rolling shutter CMOS image sensor and performs high-speed and high-sensitivity imaging by using channel fragmentation, temporal summation, and compressive frame reconstruction.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!