Bio-signal based control in assistive robots: a survey

Recently, bio-signal based control has been gradually deployed in biomedical devices and assistive robots for improving the quality of life of disabled and elderly people, among which electromyography (EMG) and electroencephalography (EEG) bio-signals are being used widely. This paper reviews the deployment of these bio-signals in the state of art of control systems. The main aim of this paper is to describe the techniques used for (i) collecting EMG and EEG signals and diving these signals into segments (data acquisition and data segmentation stage), (ii) dividing the important data and removing redundant data from the EMG and EEG segments (feature extraction stage), and (iii) identifying categories from the relevant data obtained in the previous stage (classification stage). Furthermore, this paper presents a summary of applications controlled through these two bio-signals and some research challenges in the creation of these control systems. Finally, a brief conclusion is summarized

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal. Pre- processing of sEMG signals is extremely desirable before the classification because they allow a more consistent and precise evaluation in the above applications. This article provides a segment-by-segment overview of: (1) Techniques for eliminating artifacts from sEMG signals from the lower limb. (2) A survey of existing datasets of lower limb sEMG. (3) A concise description of the various techniques for processing and classifying sEMG data for various applications involving lower limb activity. Finally, an open discussion is presented, which may result in the identification of a variety of future research possibilities for human lower limb activity recognition. Therefore, it is possible to anticipate that the framework presented in this study can aid in the advancement of sEMG-based recognition of human lower limb activity

Hard-wired epimysial recordings from normal and reinnervated muscle using a bone-anchored device

Background: A combined approach for prosthetic attachment and control using a transcutaneous bone-anchored device and implanted muscle electrodes can improve function for upper-limb amputees. The bone-anchor provides a transcutaneous feed-through for muscle signal recording. This approach can be combined with targeted muscle reinnervation (TMR) to further improve myoelectric control. Methods: A bone-anchored device was implanted trans-tibially in n = 8 sheep with a bipolar recording electrode secured epimysially to the peroneus tertius muscle. TMR was carried out in a single animal: the peroneus tertius was deinnervated and the distal portion of the transected nerve to the peroneus muscle was coapted to a transected nerve branch previously supplying the tibialis anterior muscle. For 12 weeks (TMR) or 19 weeks (standard procedure), epimysial muscle signals were recorded while animals walked at 2 km·h−1. Results: After 19 weeks implantation following standard procedure, epimysial recording signal-to-noise ratio (SNR) was 18.7 dB (± 6.4 dB, 95% CI) with typical recordings falling in the range 10–25 dB. Recoveries in gait and muscle signals were coincident 6 weeks post-TMR; initial muscle activity was identifiable 3 weeks post-TMR though with low signal amplitude and signal-to-noise ratio compared with normal muscle recordings. Conclusions: Following recovery, muscle signals were recorded reliably over 19 weeks following implantation. In this study, targeted reinnervation was successful in parallel with bone-anchor implantation, with recovery identified 6 weeks after surger

Feasibility of using combined EMG and kinematic signals for prosthesis control : A simulation study using a virtual reality environment

Acknowledgment This study was partly supported by a UK Medical Research Council Centenary Award to Keele University.Peer reviewedPublisher PD

Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures

We provide a novel method to infer finger flexing motions using a four-channel surface electromyogram (EMG). Surface EMG signals can be recorded from the human body non-invasively and easily. Surface EMG signals in this study were obtained from four channel electrodes placed around the forearm. The motions consist of the flexion of five single fingers (thumb, index finger, middle finger, ring finger, and little finger) and three multi.finger motions. The maximum likelihood estimation was used to infer the finger motions. Experimental results have shown that this method can successfully infer the finger flexing motions. The average accuracy was as high as 97.75%. In addition, we examined the influence of inference accuracies with the various arm postures

An exploration of grip force regulation with a low-impedance myoelectric prosthesis featuring referred haptic feedback

Abstract Background Haptic display technologies are well suited to relay proprioceptive, force, and contact cues from a prosthetic terminal device back to the residual limb and thereby reduce reliance on visual feedback. The ease with which an amputee interprets these haptic cues, however, likely depends on whether their dynamic signal behavior corresponds to expected behaviors—behaviors consonant with a natural limb coupled to the environment. A highly geared motor in a terminal device along with the associated high back-drive impedance influences dynamic interactions with the environment, creating effects not encountered with a natural limb. Here we explore grasp and lift performance with a backdrivable (low backdrive impedance) terminal device placed under proportional myoelectric position control that features referred haptic feedback. Methods We fabricated a back-drivable terminal device that could be used by amputees and non-amputees alike and drove aperture (or grip force, when a stiff object was in its grasp) in proportion to a myoelectric signal drawn from a single muscle site in the forearm. In randomly ordered trials, we assessed the performance of N=10 participants (7 non-amputee, 3 amputee) attempting to grasp and lift an object using the terminal device under three feedback conditions (no feedback, vibrotactile feedback, and joint torque feedback), and two object weights that were indiscernible by vision. Results Both non-amputee and amputee participants scaled their grip force according to the object weight. Our results showed only minor differences in grip force, grip/load force coordination, and slip as a function of sensory feedback condition, though the grip force at the point of lift-off for the heavier object was significantly greater for amputee participants in the presence of joint torque feedback. An examination of grip/load force phase plots revealed that our amputee participants used larger safety margins and demonstrated less coordination than our non-amputee participants. Conclusions Our results suggest that a backdrivable terminal device may hold advantages over non-backdrivable devices by allowing grip/load force coordination consistent with behaviors observed in the natural limb. Likewise, the inconclusive effect of referred haptic feedback on grasp and lift performance suggests the need for additional testing that includes adequate training for participants.http://deepblue.lib.umich.edu/bitstream/2027.42/116041/1/12984_2015_Article_98.pd