A survey on the state of the art of force myography technique (FMG): analysis and assessment

Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique.

Review of electromyography onset detection methods for real-time control of robotic exoskeletons

BACKGROUND

Electromyography (EMG) is a classical technique used to record electrical activity associated with muscle contraction and is widely applied in Biomechanics, Biomedical Engineering, Neuroscience and Rehabilitation Robotics. Determining muscle activation onset timing, which can be used to infer movement intention and trigger prostheses and robotic exoskeletons, is still a big challenge. The main goal of this paper was to perform a review of the state-of-the-art of EMG onset detection methods. Moreover, we compared the performance of the most commonly used methods on experimental EMG data.

METHODS

A total of 156 papers published until March 2022 were included in the review. The papers were analyzed in terms of application domain, pre-processing method and EMG onset detection method. The three most commonly used methods [Single (ST), Double (DT) and Adaptive Threshold (AT)] were applied offline on experimental intramuscular and surface EMG signals obtained during contractions of ankle and knee joint muscles.

RESULTS

Threshold-based methods are still the most commonly used to detect EMG onset. Compared to ST and AT, DT required more processing time and, therefore, increased onset timing detection, when applied on experimental data. The accuracy of these three methods was high (maximum error detection rate of 7.3%), demonstrating their ability to automatically detect the onset of muscle activity. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset offline, reporting promising results.

CONCLUSIONS

This study organized and classified the existing EMG onset detection methods to create consensus towards a possible standardized method for EMG onset detection, which would also allow more reproducibility across studies. The three most commonly used methods (ST, DT and AT) proved to be accurate, while ST and AT were faster in terms of EMG onset detection time, especially when applied on intramuscular EMG data. These are important features towards movement intention identification, especially in real-time applications. Machine Learning methods have received increased attention as an alternative to detect muscle activation onset. However, although several methods have shown their capability offline, more research is required to address their full potential towards real-time applications, namely to infer movement intention.

Effectiveness of robotic exoskeletons for improving gait in children with cerebral palsy: A systematic review

BACKGROUND

Robotic exoskeletons have been developed to assist locomotion and address gait abnormalities in children with cerebral palsy (CP). These wearable assistive devices provide powered assistance to the lower-extremity joints, as well as support and stability.

RESEARCH QUESTION

Does exoskeleton-assisted walking improve gait in children with CP?

METHODS

The PRISMA guidelines were used to conduct this systematic review. Articles were obtained in a search of the following electronic databases: Embase, CINAHL Complete, PubMed, Web of Science and MEDLINE. Studies investigating spatiotemporal, kinematic, kinetic, muscle activity and/or physiological parameters during exoskeleton-assisted walking in children with CP were included. All articles were assessed for methodological quality using an adapted version of the Quality Assessment Tool for Before-After (Pre-Post) Studies with No Control Group, provided by the National Institutes of Health (NIH).

RESULTS

Thirteen studies were included. They involved the use of the following exoskeletons: tethered knee exoskeleton, pediatric knee exoskeleton (P.REX), untethered ankle exoskeleton, WAKE-Up ankle module, WAKE-Up ankle & knee module and unilateral ankle exosuit. Methodological quality varied, with key limitations in sample size and allocated time to adapt to the exoskeleton. There was a consensus that robotic exoskeletons improve gait given careful optimisation of exoskeleton torque and sufficient exoskeleton practice time for each participant. Improvements in gait included reduced metabolic cost of walking, increased walking speed, and increased knee and hip extension during stance. Furthermore, exoskeletons with an actuated ankle module were shown to promote normal ankle rocker function.

SIGNIFICANCE

Robotic exoskeletons have the potential to improve the mobility of CP children and may therefore increase community participation and improve quality of life. Future work should involve larger controlled intervention studies utilising robotic exoskeletons to improve gait in children with CP. These studies should ensure sufficient exoskeleton practice time for each participant.

Applications of Brain-Machine Interface Systems in Stroke Recovery and Rehabilitation

Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation.