Real time biosignal-Driven illusion system for upper limb rehabilitation

This paper presents design and development of real time biosignal-driven illusion system: Augmented Reality based Illusion System (ARIS) for upper limb motor rehabilitation. ARIS is a hospital / home based self-motivated whole arm rehabilitation system that aims to improve and restore the lost upper limb functions due to Cerebrovascular Accident (CVA) or stroke. Taking the advantage of human brain plasticity nature, the system incorporates with number of technologies to provide fast recovery by re-establishing the neural pathways and synapses that able to control the mobility. These technologies include Augmented Reality (AR) where illusion environment is developed, computer vision technology to track multiple colors in real time, EMG acquisition system to detect the user intention in real time and 3D modelling library to develop Virtual Arm (VA) model where human biomechanics are applied to mimic the movement of real arm. The system operates according to the user intention via surface electromyography (sEMG) threshold level. In the case of real arm cannot reach to the desired position, VA will take over the job of real arm to complete the exercise. The effectiveness of the developed ARIS has evaluated via questionnaire, graphical and analytical measurements which provided with positive results

Augmented Reality based Illusion System with biofeedback

This paper presents the Augmented Reality based Illusion System (ARIS) with biofeedback for upper limb rehabilitation. It aims for fast recovery of motor deficit with motivational approach over traditional upper limb rehabilitation methods. The system incorporates with Augmented Reality (AR) technology to develop upper limb rehabilitation exercise and computer vision with color recognition technique to create 'Fool-the-Brain' concept for fast recovery of neural impairment due to various motor injuries. The rehabilitation exercise in ARIS is aiming to increase the shoulder joint range of motions by performing reaching movements and to strengthen the associated muscles. 'Fool-the-Brain' concept is introduced during performing rehabilitation exercise to perceive artificial visual feedback where user's real impaired arm is covered by Virtual Arm (VA). When the real arm cannot perform the required task, VA will take over the job of real one and will make the user perceives the sense that is still be able to accomplish the task with own effort. Evaluation has performed and results indicate that ARIS with biofeedback is a potential upper limb rehabilitation system for people with upper limb motor deficit. © 2014 IEEE

A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot

In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the adherence to the rehabilitation plan. Our system takes advantage of a low-cost hand-tracking device to provide a quantitative analysis of the performance of the patient. It also integrates a low-cost surface electromyography (sEMG) sensor in order to provide insight about which muscles are being activated while completing the exercises. It is also modular and can be deployed on a social robot. We tested our proposal in two different facilities for rehabilitation with high success. The therapists and patients felt more motivation while using HaReS, which improved the adherence to the rehabilitation plan. In addition, the therapists were able to provide services to more patients than when they used their traditional methodology.This work was funded by a Spanish Government PID2019-104818RB-I00 grant, supported by Feder funds. It was also supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

Real-time classification of finger movements using two-channel surface electromyography

The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively

Multimodal adaptive interfaces for 3D robot-mediated upper limb neuro-rehabilitation: An overview of bio-cooperative systems

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Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research