Single Lead EMG signal to Control an Upper Limb Exoskeleton Using Embedded Machine Learning on Raspberry Pi

Post-stroke can cause partial or complete paralysis of the human limb. Delayed rehabilitation steps in post-stroke patients can cause muscle atrophy and limb stiffness. Post-stroke patients require an upper limb exoskeleton device for the rehabilitation process. Several previous studies used more than one electrode lead to control the exoskeleton. The use of many electrode leads can lead to an increase in complexity in terms of hardware and software. Therefore, this research aims to develop single lead EMG pattern recognition to control an upper limb exoskeleton. The main contribution of this research is that the robotic upper limb exoskeleton device can be controlled using a single lead EMG. EMG signals were tapped at the biceps point with a sampling frequency of 2000 Hz. A Raspberry Pi 3B+ was used to embed the data acquisition, feature extraction, classification and motor control by using multithread algorithm. The exoskeleton arm frame is made using 3D printing technology using a high torque servo motor drive. The control process is carried out by extracting EMG signals using EMG features (mean absolute value, root mean square, variance) further extraction results will be trained on machine learning (decision tree (DT), linear regression (LR), polynomial regression (PR), and random forest (RF)). The results show that machine learning decision tree and random forest produce the highest accuracy compared to other classifiers. The accuracy of DT and RF are of 96.36±0.54% and 95.67±0.76%, respectively. Combining the EMG features, shows that there is no significant difference in accuracy (p-value 0.05). A single lead EMG electrode can control the upper limb exoskeleton robot device well

Brain-machine interfaces for rehabilitation in stroke: A review

BACKGROUND: Motor paralysis after stroke has devastating consequences for the patients, families and caregivers. Although therapies have improved in the recent years, traditional rehabilitation still fails in patients with severe paralysis. Brain-machine interfaces (BMI) have emerged as a promising tool to guide motor rehabilitation interventions as they can be applied to patients with no residual movement. OBJECTIVE: This paper reviews the efficiency of BMI technologies to facilitate neuroplasticity and motor recovery after stroke. METHODS: We provide an overview of the existing rehabilitation therapies for stroke, the rationale behind the use of BMIs for motor rehabilitation, the current state of the art and the results achieved so far with BMI-based interventions, as well as the future perspectives of neural-machine interfaces. RESULTS: Since the first pilot study by Buch and colleagues in 2008, several controlled clinical studies have been conducted, demonstrating the efficacy of BMIs to facilitate functional recovery in completely paralyzed stroke patients with noninvasive technologies such as the electroencephalogram (EEG). CONCLUSIONS: Despite encouraging results, motor rehabilitation based on BMIs is still in a preliminary stage, and further improvements are required to boost its efficacy. Invasive and hybrid approaches are promising and might set the stage for the next generation of stroke rehabilitation therapies.This study was funded by the Bundesministerium für Bildung und Forschung BMBF MOTORBIC (FKZ13GW0053)andAMORSA(FKZ16SV7754), the Deutsche Forschungsgemeinschaft (DFG), the fortüne-Program of the University of Tübingen (2422-0-0 and 2452-0-0), and the Basque GovernmentScienceProgram(EXOTEK:KK2016/00083). NIL was supported by the Basque Government’s scholarship for predoctoral students

DESIGN AND DEVELOPMENT OF 3D PRINTED MYOELECTRIC ROBOTIC EXOSKELETON FOR HAND REHABILITATION

The development of dynamic rehabilitation devices can be evaluated as a research fast-growing field. Indeed, robot-assisted therapy is an advanced new technology mainly in stroke rehabilitation. Although patients benefit from this enormous development of technology, including the presence of rehabilitation robots, the therapeutic field still suffering a lack in hand robotic rehabilitation devices. In this context, this work proposes a new design of a 3D printed hand exoskeleton for the stroke rehabilitation. Based on the EMG signals measured from the muscles responsible for the hand motion, the designed mechatronic system detects the intention of hand opening or hand closing from the stroked subject. Based on an embedded controller and five servomotors, the low cost robotic system is able to drive in real time three degrees of freedom (DOFs) for each finger. The real tests with stroked subjects showed that the designed hand exoskeleton architecture has a positive effect on the motion finger range and mainly in the hand ability to perform some simple tasks. The case studies showed a good recovery of the motor functions and consequently the developed system efficiency

Real-Time Control of a Multi-Degree-of-Freedom Mirror Myoelectric Interface During Functional Task Training

Motor learning mediated by motor training has in the past been explored for rehabilitation. Myoelectric interfaces together with exoskeletons allow patients to receive real-time feedback about their muscle activity. However, the number of degrees of freedom that can be simultaneously controlled is limited, which hinders the training of functional tasks and the effectiveness of the rehabilitation therapy. The objective of this study was to develop a myoelectric interface that would allow multi-degree-of-freedom control of an exoskeleton involving arm, wrist and hand joints, with an eye toward rehabilitation. We tested the effectiveness of a myoelectric decoder trained with data from one upper limb and mirrored to control a multi-degree-of-freedom exoskeleton with the opposite upper limb (i.e., mirror myoelectric interface) in 10 healthy participants. We demonstrated successful simultaneous control of multiple upper-limb joints by all participants. We showed evidence that subjects learned the mirror myoelectric model within the span of a five-session experiment, as reflected by a significant decrease in the time to execute trials and in the number of failed trials. These results are the necessary precursor to evaluating if a decoder trained with EMG from the healthy limb could foster learning of natural EMG patterns and lead to motor rehabilitation in stroke patients.This study was funded by the Eurostars Project E! 113928 Subliminal Home Rehab (SHR), BMBF (Bundesministerium für Bildung und Forschung) (FKZ: SHR 01QE2023; and REHOME 16SV8606), Fortüne-Program of the University of Tübingen (2452-0-0/1), Ministry of Science of the Basque Country (Elkartek: MODULA KK-2019/00018) and H2020- FETPROACT-EIC-2018-2020 (MAIA 951910)

Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients

The motor impairment occurring after a stroke is characterized by pathological muscle activation patterns or synergies. However, while robot-aided myoelectric interfaces have been proposed for stroke rehabilitation, they do not address this issue, which might result in inefficient interventions. Here, we present a novel paradigm that relies on the correction of the pathological muscle activity as a way to elicit rehabilitation, even in patients with complete paralysis. Previous studies demonstrated that there are no substantial inter-limb differences in the muscle synergy organization of healthy individuals. We propose building a subject-specific model of muscle activity from the healthy limb and mirroring it to use it as a learning tool for the patient to reproduce the same healthy myoelectric patterns on the paretic limb during functional task training. Here, we aim at understanding how this myoelectric model, which translates muscle activity into continuous movements of a 7-degree of freedom upper limb exoskeleton, could transfer between sessions, arms and tasks. The experiments with 8 healthy individuals and 2 chronic stroke patients proved the feasibility and effectiveness of such myoelectric interface. We anticipate the proposed method to become an efficient strategy for the correction of maladaptive muscle activity and the rehabilitation of stroke patients.This study was funded by the Baden-Württemberg Stiftung (GRUENS ROB-1), the Deutsche Forschungsgemeinschaft (DFG, Koselleck), the Fortüne-Program of the University of Tübingen (2422-0-0), and the Bundes Ministerium für Bildung und Forschung BMBF MOTORBIC (FKZ 13GW0053), AMORSA (FKZ 16SV7754), Gipuzkoa Regional Government (INKRATEK), Ministry of Science of the Basque Country (Elkartek: EXOTEK). A. Sarasola-Sanz’s work was supported by La Caixa-DAAD scholarship and N. Irastorza-Landa’s work by the Basque Government and IKERBASQUE, Basque Foundation for Science, Bilbao, Spain