Abstract
Stroke is one of the prime reasons for paralysis throughout the world caused due to impaired nervous system and resulting in disability to move the affected body parts. Rehabilitation is the natural remedy for recovering from paralysis and enhancing the quality of life. Brain Computer Interface (BCI) controlled assistive technology is the new paradigm, providing assistance and rehabilitation for the paralysed. But, most of these devices are error prone and also hard to get continuous control because of the dynamic nature of the brain signals. Moreover, existing devices like exoskeletons brings additional burden on the patient and the caregivers and also results in mental fatigue and frustration. To solve these issues Artificial Muscle Intelligence with Deep Learning (AMIDL) system is proposed in this paper. AMIDL integrates user intentions with artificial muscle movements in an efficient way to improve the performance. Human thoughts captured using Electroencephalogram (EEG) sensors are transformed into body movements, by utilising microcontroller and Transcutaneous Electrical Nerve Stimulation (TENS) device. EEG signals are subjected to pre-processing, feature extraction and classification, before being passed on to the affected body part. The received EEG signal is correlated with the recorded artificial muscle movements. If the captured EEG signal falls below the desired level, the affected body part will be stimulated by the recorded artificial muscle movements. The system also provides a feature for communicating human intentions as alert message to caregivers, in case of emergency situations. This is achieved by offline training of specific gesture and online gesture recognition algorithm. The recognised gesture is transformed into speech, thus enabling the paralysed to express their feelings to the relatives or friends. Experiments were carried out with the aid of healthy and paralysed subjects. The AMIDL system helped to reduce mental fatigue, miss-operation, frustration and provided continuous control. The thrust of lifting the exoskeleton is also reduced by using light weight wireless electrodes. The proposed system will be a great communication aid for paralysed to express their thoughts and feelings with dear and near ones, thereby enhancing the quality of life.
Highlights
The recent survey by reeve foundation revealed the impact of paralysis on world population, affecting approximately 5.4 million people [1], [2]
The clinical trials to investigate the effectiveness of Brain Computer Interface (BCI) training sessions on stroke patients with upper limb paralysis are carried out
The linear control of upper limb is demonstrated using motor imagery based BCI and Functional Electrical Stimulation (FES), support is provided to the arm using passive exoskeleton
Summary
The recent survey by reeve foundation revealed the impact of paralysis on world population, affecting approximately 5.4 million people [1], [2]. Antelis et al demonstrated upper limb movement of the paralyzed using EEG signals [5]. The clinical trials to investigate the effectiveness of BCI training sessions on stroke patients with upper limb paralysis are carried out. Human intentions measured through cortical potentials were used to control upper-limb exoskeleton movements. Hybrid BMI system based on sensorimotor cortical desynchronization (ERD) and electromyography (EMG) activity was designed to control upper limb movements. The linear control of upper limb is demonstrated using motor imagery based BCI and Functional Electrical Stimulation (FES), support is provided to the arm using passive exoskeleton. The self-induced EEG variations based on ERD/ERS is utilized for controlling upper limb movements. In case of ambiguity or inadequate EEG signal, the periodic activation of the affected body part will be taken care by the artificial muscle movements. The rest of the paper is organized into four sections in which section 2 describes different existing methods used in BCI controlled upper limb movements
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