Introduction: Spinal cord injuries, traumas, natural aging, and strokes are the main causes of arm impairment or even a chronic disability for an increasing part of the population. Therefore, robotic devices can be essential tools to help individuals afflicted with hand deficit with the activities of daily living in addition to the possibility of restoring hand functions by rehabilitation. Because the surface electromyography (sEMG) control paradigm has recently emerged as an interesting intention control method in devices applied to rehabilitation, the concentration in this study has been devoted to sEMG-controlled hand robotic devices, including gloves and exoskeletons that are used for rehabilitation and for assistance in daily activities.

In order to enhance the efficacy of the prosthetic and orthotic robotic devices controlled by surface electromyography (sEMG) signals, muscle activity detection algorithms need to be independent of the amplitude variation in the sEMG signal to make these devices more feasible and reliable for the disabled people. A new algorithm has been developed to detect the presence of muscle activities in weak and noisy sEMG signals. The algorithm does not employ any amplitude features in the detection process and employs only frequency features of the sEMG signal; therefore it is amplitude independent and can detect muscle activities in signals that have low signal to noise ratio. A new zero crossing technique has been developed as a new frequency feature called the Adaptive Zero Crossing (AZC) which is used to minimize false alarms and enhances the detection process. This new feature in addition to the mean of the Mean Instantaneous Frequency (MMIF) of the signal is used to detect the presence of the muscle activities in the sEMG signals.

No abstract

No abstract

To make robotic hand devices controlled by surface electromyography (sEMG) signals feasible and practical tools for assisting patients with hand impairments, the problems that prevent these devices from being widely used have to be overcome. The most significant problem is the involuntary amplitude variation of the sEMG signals due to the movement of electrodes during forearm motion. Moreover, for patients who have had a stroke or another neurological disease, the muscle activity of the impaired hand is weak and has a low signal-to-noise ratio (SNR). Thus, muscle activity detection methods intended for controlling robotic hand devices should not depend mainly on the amplitude characteristics of the sEMG signal in the detection process, and they need to be more reliable for sEMG signals that have a low SNR. Since amplitudeindependent muscle activity detection methods meet these requirements, this paper investigates the performance of such a method on people who have had a stroke in terms of the detection of weak muscle activity and resistance to false alarms caused by the involuntary amplitude variation of sEMG signals; these two parameters are very important for achieving the reliable control of robotic hand devices intended for people with disabilities. A comparison between the performance of an amplitude-independent muscle activity detection algorithm and three amplitude-dependent algorithms was conducted by using sEMG signals recorded from six hemiparesis stroke survivors and from six healthy subjects. The results showed that the amplitude-independent algorithm performed better in terms of detecting weak muscle activity and resisting false alarms.