Mechanomyography and muscle function assessment: a review of current state and prospects

Electromechanical efficiency index of skeletal muscle and its applicability: a systematic review

Introduction: The electromechanical efficiency of skeletal muscle represents the dissociation between electrical and mechanical events within a muscle. It has been widely studied, with varying methods for its measurement and calculation. For this reason, the purpose of this literature review was to integrate the available research to date and provide more insights about this measure. Methods: A systematic search of the literature was performed across three online databases: PubMed, ScienceDirect, and SPORTDiscus. This yielded 1284 reports, of which 10 met the inclusion criteria. Included studies have used different methods to measure the electromechanical efficiency (EME) index, including electromyography (EMG), mechanomyography and tensiomyography (TMG). Results: The EME index was used to assess muscle conditions such as muscle atrophy, pain syndromes, or to monitor rehabilitation in patients with knee problems, fatigue and the effects of exercise and rehabilitation. TMG has been shown to be one of the most reliable methods to obtain the EME index, but its use precludes obtaining the index during voluntary muscle contractions. Conclusion: Standardizing the EME index is crucial for its diverse applications in clinical, sport, and rehabilitation contexts. Future research should prioritize standardization of measurement protocols for establishing the most repeatable, and reliable approach that can be used for inter-individual comparisons or for assessing an individual for multiple times over a longer period. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023440333 Identifier: CRD42023440333.

Biosignal-integrated robotic systems with emerging trends in visual interfaces: A systematic review

Human-machine interfaces (HMI) are currently a trendy and rapidly expanding area of research. Interestingly, the human user does not readily observe the interface between humans and machines. Instead, interactions between the machine and electrical signals from the user's body are obscured by complex control algorithms. The result is effectively a one-way street, wherein data is only transmitted from human to machine. Thus, a gap remains in the literature: how can information be effectively conveyed to the user to enable mutual understanding between humans and machines? Here, this paper reviews recent advancements in biosignal-integrated wearable robotics, with a particular emphasis on "visualization"-the presentation of relevant data, statistics, and visual feedback to the user. This review article covers various signals of interest, such as electroencephalograms and electromyograms, and explores novel sensor architectures and key materials. Recent developments in wearable robotics are examined from control and mechanical design perspectives. Additionally, we discuss current visualization methods and outline the field's future direction. While much of the HMI field focuses on biomedical and healthcare applications, such as rehabilitation of spinal cord injury and stroke patients, this paper also covers less common applications in manufacturing, defense, and other domains.

Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on Mechanomyography

Transcutaneous spinal cord stimulation (tSCS) provides a promising therapy option for individuals with injured spinal cords and multiple sclerosis patients with spasticity and gait deficits. Before the therapy, the examiner determines a suitable electrode position and stimulation current for a controlled application. For that, amplitude characteristics of posterior root muscle (PRM) responses in the electromyography (EMG) of the legs to double pulses are examined. This laborious procedure holds potential for simplification due to time-consuming skin preparation, sensor placement, and required expert knowledge. Here, we investigate mechanomyography (MMG) that employs accelerometers instead of EMGs to assess muscle activity. A supervised machine-learning classification approach was implemented to classify the acceleration data into no activity and muscular/reflex responses, considering the EMG responses as ground truth. The acceleration-based calibration procedure achieved a mean accuracy of up to 87% relative to the classical EMG approach as ground truth on a combined cohort of 11 healthy subjects and 11 patients. Based on this classification, the identified current amplitude for the tSCS therapy was in 85%, comparable to the EMG-based ground truth. In healthy subjects, where both therapy current and position have been identified, 91% of the outcome matched well with the EMG approach. We conclude that MMG has the potential to make the tuning of tSCS feasible in clinical practice and even in home use.

Quantifying muscle contraction with a conductive electroactive polymer sensor: introduction to a novel surface mechanomyography device

Clinicians seek an accurate method to assess muscle contractility during activities to better guide treatment. We investigated application of a conductive electroactive polymer sensor as a novel wearable surface mechanomyography (sMMG) sensor for quantifying muscle contractility. The radial displacement of a muscle during a contraction is detected by the physically stretched dielectric elastomer component of the sMMG sensor which quantifies the changes in capacitance. The duration of muscle activation times for quadriceps, hamstrings, and gastrocnemius muscles demonstrated strong correlation between sMMG and EMG during a parallel squat activity and isometric contractions. A moderate to strong correlation was demonstrated between the sMMG isometric muscle activation times and force output times from a dynamometer. The potential wearable application of an electroactive polymer sensor to measure muscle contraction time is supported.

3D muscle networks based on vibrational mechanomyography

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Effect of Forearm Postures and Elbow Joint Angles on Elbow Flexion Torque and Mechanomyography in Neuromuscular Electrical Stimulation of the Biceps Brachii

Neuromuscular electrical stimulation plays a pivotal role in rehabilitating muscle function among individuals with neurological impairment. However, there remains uncertainty regarding whether the muscle's response to electrical excitation is affected by forearm posture, joint angle, or a combination of both factors. This study aimed to investigate the effects of forearm postures and elbow joint angles on the muscle torque and MMG signals. Measurements of the torque around the elbow and MMG of the biceps brachii (BB) muscle were conducted in 36 healthy subjects (age, 22.24 ± 2.94 years; height, 172 ± 0.5 cm; and weight, 67.01 ± 7.22 kg) using an in-house elbow flexion testbed and neuromuscular electrical stimulation (NMES) of the BB muscle. The BB muscle was stimulated while the forearm was positioned in the neutral, pronation, or supination positions. The elbow was flexed at angles of 10°, 30°, 60°, and 90°. The study analyzed the impact of the forearm posture(s) and elbow joint angle(s) on the root-mean-square value of the torque (TQRMS). Subsequently, various MMG parameters, such as the root-mean-square value (MMGRMS), the mean power frequency (MMGMPF), and the median frequency (MMGMDF), were analyzed along the longitudinal, lateral, and transverse axes of the BB muscle fibers. The test-retest interclass correlation coefficient (ICC21) for the torque and MMG ranged from 0.522 to 0.828. Repeated-measure ANOVAs showed that the forearm posture and elbow flexion angle significantly influenced the TQRMS (p < 0.05). Similarly, the MMGRMS, MMGMPF, and MMGMDF showed significant differences among all the postures and angles (p < 0.05). However, the combined main effect of the forearm posture and elbow joint angle was insignificant along the longitudinal axis (p > 0.05). The study also found that the MMGRMS and TQRMS increased with increases in the joint angle from 10° to 60° and decreased at greater angles. However, during this investigation, the MMGMPF and MMGMDF exhibited a consistent decrease in response to increases in the joint angle for the lateral and transverse axes of the BB muscle. These findings suggest that the muscle contraction evoked by NMES may be influenced by the interplay between actin and myosin filaments, which are responsible for muscle contraction and are, in turn, influenced by the muscle length. Because restoring the function of limbs is a common goal in rehabilitation services, the use of MMG in the development of methods that may enable the real-time tracking of exact muscle dimensional changes and activation levels is imperative.

Mechanomyographic Analysis for Muscle Activity Assessment during a Load-Lifting Task

The purpose of this study was to compare electromyographic (EMG) with mechanomyographic (MMG) recordings during isometric conditions, and during a simulated load-lifting task. Twenty-two males (age: 25.5 ± 5.3 years) first performed maximal voluntary contractions (MVC) and submaximal isometric contractions of upper limb muscles at 25%, 50% and 75% MVC. Participants then executed repetitions of a functional activity simulating a load-lifting task above shoulder level, at 25%, 50% and 75% of their maximum activity (based on MVC). The low-frequency part of the accelerometer signal (<5 Hz) was used to segment the six phases of the motion. EMG and MMG were both recorded during the entire experimental procedure. Root mean square (RMS) and mean power frequency (MPF) were selected as signal extraction features. During isometric contractions, EMG and MMG exhibited similar repeatability scores. They also shared similar RMS vs. force relationship, with RMS increasing to 75% MVC and plateauing to 100%. MPF decreased with increasing force to 75% MVC. In dynamic condition, RMSMMG exhibited higher sensitivity to changes in load than RMSEMG. These results confirm the feasibility of MMG measurements to be used during functional activities outside the laboratory. It opens new perspectives for future applications in sports science, ergonomics and human-machine interface conception.

Mechanomyography as a Surgical Adjunct for Treatment of Chronic Entrapment Neuropathy: A Case Series

BACKGROUND

Chronic entrapment neuropathy results in a clinical syndrome ranging from mild pain to debilitating atrophy. There remains a lack of objective metrics that quantify nerve dysfunction and guide surgical decision-making. Mechanomyography (MMG) reflects mechanical motor activity after stimulation of neuromuscular tissue and may indicate underlying nerve dysfunction.

OBJECTIVE

To evaluate the role of MMG as a surgical adjunct in treating chronic entrapment neuropathies.

METHODS

Patients 18 years or older with cubital tunnel syndrome (n = 8) and common peroneal neuropathy (n = 15) were enrolled. Surgical decompression of entrapped nerves was performed with intraoperative MMG of the hypothenar and tibialis anterior muscles. MMG stimulus thresholds (MMG-st) were correlated with compound muscle action potential (CMAP), motor nerve conduction velocity, baseline functional status, and clinical outcomes.

RESULTS

After nerve decompression, MMG-st significantly reduced, the mean reduction of 0.5 mA (95% CI: 0.3-0.7, P < .001). On bivariate analysis, MMG-st exhibited significant negative correlation with common peroneal nerve CMAP ( P < .05), but no association with ulnar nerve CMAP and motor nerve conduction velocity. On preoperative electrodiagnosis, 60% of nerves had axonal loss and 40% had conduction block. The MMG-st was higher in the nerves with axonal loss as compared with the nerves with conduction block. MMG-st was negatively correlated with preoperative hand strength (grip/pinch) and foot-dorsiflexion/toe-extension strength ( P < .05). At the final visit, MMG-st significantly correlated with pain, PROMIS-10 physical function, and Oswestry Disability Index ( P < .05).

CONCLUSION

MMG-st may serve as a surgical adjunct indicating axonal integrity in chronic entrapment neuropathies which may aid in clinical decision-making and prognostication of functional outcomes.

Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration, and regeneration: part 1, neurophysiology

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Incorporating Intraoperative Mechanomyography to Peripheral Nerve Decompression Surgery

BACKGROUND

Mechanomyography (MMG) is a novel intraoperative tool to detect and quantify nerve activity with high sensitivity as compared with traditional electromyographic recordings. MMG reflects the mechanical vibrations of single motor units detected through accelerometer sensors after direct motor neuron stimulation.

OBJECTIVE

To determine the feasibility of applying intraoperative MMG during peripheral nerve surgery.

METHODS

A total of 20 consecutive patients undergoing surgical decompression of the ulnar nerve at the cubital tunnel or common peroneal nerve at the fibular head were included in this study. Intraoperatively, the common peroneal and ulnar nerves were directly stimulated through the MMG electrode probe starting at 0.1 mA threshold and increasing by 0.1 mA increments until target muscle activity was noted. The lowest threshold current required to elicit a muscle response was recorded before decompression and after proximal and distal nerve decompression.

RESULTS

Of the patients, 80% (16/20) had MMG signals detected and recorded. Four patients were unable to have MMG signal detected despite direct nerve visualization and complete neurolysis. The mean predecompression stimulus threshold was 1.59 ± 0.19 mA. After surgical decompression, improvement in the mean MMG stimulus threshold was noted (0.47 ± 0.03 mA, P = .0002). Postoperatively, all patients endorsed symptomatic improvement with no complications.

CONCLUSION

MMG may provide objective guidance for the intraoperative determination of the extent of nerve decompression. Lower stimulus thresholds may represent increased sparing of axonal tissue. Future work should focus on validating normative values of MMG stimulus thresholds in various nerves and establishing clinical associations with functional outcomes.

An optoelectronic muscle contraction sensor for prosthetic hand application

Surface electromyography (sEMG) is considered an established means for controlling prosthetic devices. sEMG suffers from serious issues such as electrical noise, motion artifact, complex acquisition circuitry, and high measuring costs because of which other techniques have gained attention. This work presents a new optoelectronic muscle (OM) sensor setup as an alternative to the EMG sensor for precise measurement of muscle activity. The sensor integrates a near-infrared light-emitting diode and phototransistor pair along with the suitable driver circuitry. The sensor measures skin surface displacement (that occurs during muscle contraction) by detecting backscattered infrared light from skeletal muscle tissue. With an appropriate signal processing scheme, the sensor was able to produce a 0-5 V output proportional to the muscular contraction. The developed sensor depicted decent static and dynamic features. In detecting muscle contractions from the forearm muscles of subjects, the sensor showed good similarity with the EMG sensor. In addition, the sensor displayed higher signal-to-noise ratio values and better signal stability than the EMG sensor. Furthermore, the OM sensor setup was utilized to control the rotation of the servomotor using an appropriate control scheme. Hence, the developed sensing system can measure muscle contraction information for controlling assistive devices.

Toward a robust swallowing detection for an implantable active artificial larynx: a survey

Total laryngectomy consists in the removal of the larynx and is intended as a curative treatment for laryngeal cancer, but it leaves the patient with no possibility to breathe, talk, and swallow normally anymore. A tracheostomy is created to restore breathing through the throat, but the aero-digestive tracts are permanently separated and the air no longer passes through the nasal tracts, which allowed filtration, warming, humidification, olfaction, and acceleration of the air for better tissue oxygenation. As for phonation restoration, various techniques allow the patient to talk again. The main one consists of a tracheo-esophageal valve prosthesis that makes the air passes from the esophagus to the pharynx, and makes the air vibrate to allow speech through articulation. Finally, swallowing is possible through the original tract as it is now isolated from the trachea. Yet, many methods exist to detect and assess a swallowing, but none is intended as a definitive restoration technique of the natural airway, which would permanently close the tracheostomy and avoid its adverse effects. In addition, these methods are non-invasive and lack detection accuracy. The feasibility of an effective early detection of swallowing would allow to further develop an implantable active artificial larynx and therefore restore the aero-digestive tracts. A previous attempt has been made on an artificial larynx implanted in 2012, but no active detection was included and the system was completely mechanic. This led to residues in the airway because of the imperfect sealing of the mechanism. An active swallowing detection coupled with indwelling measurements would thus likely add a significant reliability on such a system as it would allow to actively close an artificial larynx. So, after a brief explanation of the swallowing mechanism, this survey intends to first provide a detailed consideration of the anatomical region involved in swallowing, with a detection perspective. Second, the swallowing mechanism following total laryngectomy surgery is detailed. Third, the current non-invasive swallowing detection technique and their limitations are discussed. Finally, the previous points are explored with regard to the inherent requirements for the feasibility of an effective swallowing detection for an artificial larynx. Graphical Abstract.

Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation

Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.

A Systematic Review of Sensor Fusion Methods Using Peripheral Bio-Signals for Human Intention Decoding

Humans learn about the environment by interacting with it. With an increasing use of computer and virtual applications as well as robotic and prosthetic devices, there is a need for intuitive interfaces that allow the user to have an embodied interaction with the devices they are controlling. Muscle-machine interfaces can provide an intuitive solution by decoding human intentions utilizing myoelectric activations. There are several different methods that can be utilized to develop MuMIs, such as electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy. In this paper, we analyze the advantages and disadvantages of different myography methods by reviewing myography fusion methods. In a systematic review following the PRISMA guidelines, we identify and analyze studies that employ the fusion of different sensors and myography techniques, while also considering interface wearability. We also explore the properties of different fusion techniques in decoding user intentions. The fusion of electromyography, ultrasonography, mechanomyography, and near-infrared spectroscopy as well as other sensing such as inertial measurement units and optical sensing methods has been of continuous interest over the last decade with the main focus decoding the user intention for the upper limb. From the systematic review, it can be concluded that the fusion of two or more myography methods leads to a better performance for the decoding of a user's intention. Furthermore, promising sensor fusion techniques for different applications were also identified based on the existing literature.

Toward Soft Wearable Strain Sensors for Muscle Activity Monitoring

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03).

Tensiomyography Allows to Discriminate between Injured and Non-Injured Biceps Femoris Muscle

The hamstring muscle group is the most frequently injured muscle group in non-contact muscle injuries in sports involving high-speed running. A total of 84% of hamstring injuries affect the biceps femoris (BF) muscle. Clinical assessments and magnetic resonance imaging (MRI) are routinely used for diagnosis and plan management. MRI-negative scans for clinically diagnosed hamstring injuries range from 14% to 45%. We tested the hypothesis that the functional differences between injured and non-injured BF assessed by tensiomyography can be used for diagnostic and classification purposes. We compared an injured group of 53 international-level soccer players and sprinters with 53 non-injured international-level soccer players and sprinters of both sexes. Comparing the injured vs. non-injured athletes and the left vs. right side in all of the athletes, we used the percentage of absolute differences in the BF contraction time (Tc) to classify non-injured and injured BF muscles. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) and the precision−recall curve (PRC) were used to measure the classification accuracy and to identify cut-off limits using the Tc differences. There was a very high ROC AUC value of 0.981 (SE = 0.009, p < 0.000), with 98.11% of the injured muscles being correctly classified (cut-off point 12.50% on Tc differences), and an AUPRC value of 0.981, with association classification criteria at >9.87. Tensiomyography has a high predictive ability to discriminate between injured and non-injured BF non-invasively and functionally.

Wearable Iontronic FMG for Classification of Muscular Locomotion

Human motion recognition with high accuracy, fast response speed has long been considered an essential component in human-machine interactive activities such as assistive robotics, medical prosthesis, and wearable electronics. This study proposed a novel human lower limb locomotion classification strategy based on flexible supercapacitive iontronic sensors. Benefiting from the ultrahigh sensitivity (up to 1 nF/mmHg) and low activation pressure (less than 3 mmHg) of the supercapacitive iontronic pressure sensor, force myography (FMG) signal was acquired more accurately from 5 iontronic sensors strapped to the thigh (5 percentage point improvement compared with force sensitive resistor (FSR) in low window length). In the experiment with 12 subjects, the real-time classification strategy based on sliding window and SVM model gave an average locomotion classification accuracy of 99% for seven categories, including sitting, standing, walking on level ground, ramp ascent, ramp descent, stair ascent, stair descent. Compared with traditional FSR sensors, the result showed that iontronic sensors improved the classification accuracy by 5 percentage points in the case of short time window. The implementation of the high sensitivity flexible iontronic sensors in the wearable system brings a valuable tool for detecting small human body pressure signals and has great potential to improve the performance of the human-machine interface in rehabilitation and medical applications.

Phonomyography on Perioperative Neuromuscular Monitoring: An Overview

Complications related to neuromuscular blockade (NMB) could occur during anesthesia induction, maintenance, and emergency. It is recommended that neuromuscular monitoring techniques be utilized perioperatively to avoid adverse outcomes. However, current neuromuscular monitoring methods possess different shortcomings. They are cumbersome to use, susceptible to disturbances, and have limited alternative monitoring sites. Phonomyography (PMG) monitoring based on the acoustic signals yielded by skeletal muscle contraction is emerging as an interesting and innovative method. This technique is characterized by its convenience, stable signal quality, and multimuscle recording ability and shows great potential in the application field. This review summarizes the progression of PMG on perioperative neuromuscular monitoring chronologically and presents the merits, demerits, and challenges of PMG-based equipment, aiming at underscoring the potential of PMG-based apparatuses for neuromuscular monitoring.

Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation

Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.

Synergistic Upper-limb Functional Muscle Connectivity using Acoustic Mechanomyography

Functional connectivity is a critical concept in describing synergistic muscle synchronization for the execution of complex motor tasks. Muscle synchronization is typically derived from the decomposition of intermuscular coherence (IMC) at different frequency bands through electromyography (EMG) signal analysis with limited out-of-clinic applications. In this investigation, we introduce muscle network analysis to assess the coordination and functional connectivity of muscles based on mechanomyography (MMG), focused on a targeted group of muscles that are typically active in the conduction of activities of daily living using the upper limb. In this regard, functional muscle networks are evaluated in this paper for ten able-bodied participants and three amputees. MMG activity was acquired from a custom-made wearable MMG armband placed over four superficial muscles around the forearm (i.e., flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. The results of connectivity analysis at multiple frequency bands showed significant topographical differences across gestures for low (< 5Hz) and high (> 12 Hz) frequencies and observable differences between able-bodied and amputee subjects. These findings show evidence that MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic synchronization of upper-limb muscles in complex upper-limb tasks. The new physiological modality further provides key insights into the neural circuitry of motor coordination and offers the concomitant outcomes of demonstrating the feasibility of MMG to map muscle coherence from a neurophysiological perspective as well as providing the mechanistic basis for its translation into human-robot interfaces.

Basic characteristics between mechanomyogram and muscle force during twitch and tetanic contractions in rat skeletal muscles

The mechanomyogram (MMG) is a signal measured by various vibration sensors for slight vibrations induced by muscle contraction, and it reflects the muscle force during electrically induced-contraction or until 60%-70% maximum voluntary contraction, so the MMG is considered an alternative and novel measurement tool for muscle strength. We simultaneously measured the MMG and muscle force in the gastrocnemius (GC), vastus intermedius (VI), and soleus (SOL) muscles of rats. The muscle force was measured by attaching a hook to the tendon using a load cell, and the MMG was measured using a charged-coupled device-type displacement sensor at the middle of the target muscle. The MMG-twitch waveform was very similar to that of the muscle force; however, the half relaxation time and relaxation time (10%), which are relaxation parameters, were prolonged compared to those of the muscle force. The MMG amplitude correlated with the muscle force. Since stimulation frequencies that are necessary to evoke tetanic progression have a significant correlation with the twitch parameter, there is a close relationship between twitch and tetanus in the MMG signal. Therefore, we suggest that the MMG, which is electrically induced and detected by a laser displacement sensor, may be an alternative tool for measuring muscle strength.

Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation

Functional electrical stimulation (FES) is a potential neurorehabilitative intervention to enable functional movements in persons with neurological conditions that cause mobility impairments. However, the quick onset of muscle fatigue during FES is a significant challenge for sustaining the desired functional movements for more extended periods. Therefore, a considerable interest still exists in the development of sensing techniques that reliably measure FES-induced muscle fatigue. This study proposes to use ultrasound (US) imaging-derived echogenicity signal as an indicator of FES-induced muscle fatigue. We hypothesized that the US-derived echogenicity signal is sensitive to FES-induced muscle fatigue under isometric and dynamic muscle contraction conditions. Eight non-disabled participants participated in the experiments, where FES electrodes were applied on their tibialis anterior (TA) muscles. During a fatigue protocol under either isometric and dynamic ankle dorsiflexion conditions, we synchronously collected the isometric dorsiflexion torque or dynamic dorsiflexion angle on the ankle joint, US echogenicity signals from TA muscle, and the applied stimulation intensity. The experimental results showed an exponential reduction in the US echogenicity relative change (ERC) as the fatigue progressed under the isometric (R2=0.891±0.081) and dynamic (R2=0.858±0.065) conditions. The experimental results also implied a strong linear relationship between US ERC and TA muscle fatigue benchmark (dorsiflexion torque or angle amplitude), with R2 values of 0.840±0.054 and 0.794±0.065 under isometric and dynamic conditions, respectively. The findings in this study indicate that the US echogenicity signal is a computationally efficient signal that strongly represents FES-induced muscle fatigue. Its potential real-time implementation to detect fatigue can facilitate an FES closed-loop controller design that considers the FES-induced muscle fatigue.

Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation

The rapid onset of muscle fatigue during functional electrical stimulation (FES) is a major challenge when attempting to perform long-term periodic tasks such as walking. Surface electromyography (sEMG) is frequently used to detect muscle fatigue for both volitional and FES-evoked muscle contraction. However, sEMG contamination from both FES stimulation artifacts and residual M-wave signals requires sophisticated processing to get clean signals and evaluate the muscle fatigue level. The objective of this paper is to investigate the feasibility of computationally efficient ultrasound (US) echogenicity as a candidate indicator of FES-induced muscle fatigue. We conducted isometric and dynamic ankle dorsiflexion experiments with electrically stimulated tibialis anterior (TA) muscle on three human participants. During a fatigue protocol, we synchronously recorded isometric dorsiflexion force, dynamic dorsiflexion angle, US images, and stimulation intensity. The temporal US echogenicity from US images was calculated based on a gray-scaled analysis to assess the decrease in dorsiflexion force or motion range due to FES-induced TA muscle fatigue. The results showed a monotonic reduction in US echogenicity change along with the fatigue progression for both isometric (R² =0.870±0.026) and dynamic (R² =0.803±0.048) ankle dorsiflexion. These results implied a strong linear relationship between US echogenicity and TA muscle fatigue level. The findings indicate that US echogenicity may be a promising computationally efficient indicator for assessing FES-induced muscle fatigue and may aid in the design of muscle-in-the-loop FES controllers that consider the onset of muscle fatigue.

Muscle Oxygenation Level Might Trigger the Regulation of Capillary Venous Blood Filling during Fatiguing Isometric Muscle Actions

The regulation of oxygen and blood supply during isometric muscle actions is still unclear. Recently, two behavioral types of oxygen saturation (SvO2) and relative hemoglobin amount (rHb) in venous microvessels were described during a fatiguing holding isometric muscle action (HIMA) (type I: nearly parallel behavior of SvO2 and rHb; type II: partly inverse behavior). The study aimed to ascertain an explanation of these two regulative behaviors. Twelve subjects performed one fatiguing HIMA trial with each arm by weight holding at 60% of the maximal voluntary isometric contraction (MVIC) in a 90° elbow flexion. Six subjects additionally executed one fatiguing PIMA trial by pulling on an immovable resistance with 60% of the MVIC with each side and same position. Both regulative types mentioned were found during HIMA (I: n = 7, II: n = 17) and PIMA (I: n = 3, II: n = 9). During the fatiguing measurements, rHb decreased initially and started to increase in type II at an average SvO2-level of 58.75 ± 2.14%. In type I, SvO2 never reached that specific value during loading. This might indicate the existence of a threshold around 59% which seems to trigger the increase in rHb and could explain the two behavioral types. An approach is discussed to meet the apparent incompatibility of an increased capillary blood filling (rHb) despite high intramuscular pressures which were found by other research groups during isometric muscle actions.

Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

Detecting subtle yet fast skeletal muscle contractions with ultrasoft and durable graphene-based cellular materials

Human bodily movements are primarily controlled by the contractions of skeletal muscles. Unlike joint or skeletal movements that are generally performed in the large displacement range, the contractions of the skeletal muscles that underpin these movements are subtle in intensity yet high in frequency. This subtlety of movement makes it a formidable challenge to develop wearable and durable soft materials to electrically monitor such motions with high fidelity for the purpose of, for example, muscle/neuromuscular disease diagnosis. Here we report that an intrinsically fragile ultralow-density graphene-based cellular monolith sandwiched between silicone rubbers can exhibit a highly effective stress and strain transfer mechanism at its interface with the rubber, with a remarkable improvement in stretchability (>100%). In particular, this hybrid also exhibits a highly sensitive, broadband-frequency electrical response (up to 180 Hz) for a wide range of strains. By correlating the mechanical signal of muscle movements obtained from this hybrid material with electromyography, we demonstrate that the strain sensor based on this hybrid material may provide a new, soft and wearable mechanomyography approach for real-time monitoring of complex neuromuscular–skeletal interactions in a broad range of healthcare and human–machine interface applications. This work also provides a new architecture-enabled functional soft material platform for wearable electronics.

Stretchable, Self-Healing, and Skin-Mounted Active Sensor for Multipoint Muscle Function Assessment

Assessment of muscle function is an essential indicator for estimating elderly health, evaluating motor function, and instructing rehabilitation training, which also sets urgent requirements for mechanical sensors with superior quantification, accuracy, and reliability. To overcome the rigidity and vulnerability of traditional metallic electrodes, we synthesize an ionic hydrogel with large deformation tolerance and fast self-healing ability. And we propose a stretchable, self-healing, and skin-mounted (Triple S) active sensor (TSAS) based on the principles of electrostatic induction and electrostatic coupling. The skin modulus-matched TSAS provides outstanding sensing properties: maximum output voltage of 78.44 V, minimal detection limit of 0.2 mN, fast response time of 1.03 ms, high signal-to-noise ratio and excellent long-term service stability. In training of arm muscle, the functional signals of biceps and triceps brachii muscles as well as the joint dexterity of bending angle can be acquired simultaneously through TSAS. The signal can also be sent wirelessly to a terminal for analysis. With the characteristics of high sensitivity, reliability, convenience, and low-cost, TSAS shows its potential to be the next-generation procedure for real-time assessment of muscle function and rehabilitation training.

Influence of sensor mass and adipose tissue on the mechanomyography signal of elbow flexor muscles

Mechanomyography (MMG) is a non-invasive technique that records muscle contraction using sensors positioned on the skin's surface. Therefore, it can have its signal attenuated due to the adipose tissue, directly influencing the results. This study evaluates the influence of different mass added to a sensor's assembly and the adipose tissue on MMG signals of elbow flexor muscles. Test protocol consisted of skinfold thickness measurement of 22 volunteers, followed by applying 2-3 s electrical stimulation for muscle contraction during the acquisition of MMG signals. MMG signals were processed in the time domain, using the average of the absolute amplitude, and expressed in gravity values (G), termed here as MMG(G). Tests occurred four times with different sensor masses. MMG data were processed and analyzed statistically using Friedman and Kruskal-Wallis tests to determine the differences between the MMG signals measured with different sensor masses. The Mann-Whitney analysis indicated differences in the MMG signals between groups with different skinfold thickness. MMG(G) signals suffered attenuation with increasing sensor mass (0.4416 G to 0.94 g; 0.3902 G to 2.64 g; 0.3762 G to 5.44 g; 0.3762 G to 7.14 g) and adipose tissue.

Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing

In this paper, we introduce a new mode of mechanomyography (MMG) signal capture for enhancing the performance of human-machine interfaces (HMIs) through modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the sensor-skin contact area. We detail the mechatronic design, experimental validation, and user study of an armband with embedded acoustic sensors demonstrating this capacity. The design is motivated by the nonlinear viscoelasticity of the tissue, which increases with the normal surface pressure. This, in theory, results in higher conductivity of mechanical waves and hypothetically allows to interface with deeper muscle; thus, enhancing the discriminative information context of the signal space. Ten subjects (seven able-bodied and three trans-radial amputees) participated in a study consisting of the classification of hand gestures through MMG while increasing levels of contact force were administered. Four MMG channels were positioned around the forearm and placed over the flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris muscles. A total of 852 spectrotemporal features were extracted (213 features per each channel) and passed through a Neighborhood Component Analysis (NCA) technique to select the most informative neurophysiological subspace of the features for classification. A linear support vector machine (SVM) then classified the intended motion of the user. The results indicate that increasing the normal force level between the MMG sensor and the skin can improve the discriminative power of the classifier, and the corresponding pattern can be user-specific. These results have significant implications enabling embedding MMG sensors in sockets for prosthetic limb control and HMI.

A Multimodal Intention Detection Sensor Suite for Shared Autonomy of Upper-Limb Robotic Prostheses

Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.

Sonomechanomyography (SMMG): Mapping of Skeletal Muscle Motion Onset during Contraction Using Ultrafast Ultrasound Imaging and Multiple Motion Sensors

BACKGROUND

Available methods for studying muscle dynamics, including electromyography (EMG), mechanomyography (MMG) and M-mode ultrasound, have limitations in terms of spatial resolution.

METHODS

This study developed a novel method/protocol of two-dimensional mapping of muscle motion onset using ultrafast ultrasound imaging, i.e., sono-mechano-myo-graphy (SMMG). The developed method was compared with the EMG, MMG and force outputs of tibialis anterior (TA) muscle during ankle dorsiflexion at different percentages of maximum voluntary contraction (MVC) force in healthy young adults.

RESULTS

Significant differences between all pairwise comparisons of onsets were identified, except between SMMG and MMG. The EMG onset significantly led SMMG, MMG and force onsets by 40.0 ± 1.7 ms (p < 0.001), 43.1 ± 5.2 ms (p < 0.005) and 73.0 ± 4.5 ms (p < 0.001), respectively. Muscle motion also started earlier at the middle aponeurosis than skin surface and deeper regions when viewed longitudinally (p < 0.001). No significant effect of force level on onset delay was found.

CONCLUSIONS

This study introduced and evaluated a new method/protocol, SMMG, for studying muscle dynamics and demonstrated its feasibility for muscle contraction onset research. This novel technology can potentially provide new insights for future studies of neuromuscular diseases, such as multiple sclerosis and muscular dystrophy.

Ultrasound Measurement of Skeletal Muscle Contractile Parameters Using Flexible and Wearable Single-Element Ultrasonic Sensor

Skeletal muscle is considered as a near-constant volume system, and the contractions of the muscle are related to the changes in tissue thickness. Assessment of the skeletal muscle contractile parameters such as maximum contraction thickness (Th), contraction time (Tc), contraction velocity (Vc), sustain time (Ts), and half-relaxation (Tr) provides valuable information for various medical applications. This paper presents a single-element wearable ultrasonic sensor (WUS) and a method to measure the skeletal muscle contractile parameters in A-mode ultrasonic data acquisition. The developed WUS was made of double-layer polyvinylidene fluoride (PVDF) piezoelectric polymer films with a simple and low-cost fabrication process. A flexible, lightweight, thin, and small size WUS would provide a secure attachment to the skin surface without affecting the muscle contraction dynamics of interest. The developed WUS was employed to monitor the contractions of gastrocnemius (GC) muscle of a human subject. The GC muscle contractions were evoked by the electrical muscle stimulation (EMS) at varying EMS frequencies from 2 Hz up to 30 Hz. The tissue thickness changes due to the muscle contractions were measured by utilizing a time-of-flight method in the ultrasonic through-transmission mode. The developed WUS demonstrated the capability to monitor the tissue thickness changes during the unfused and fused tetanic contractions. The tetanic progression level was quantitatively assessed using the parameter of the fusion index (FI) obtained. In addition, the contractile parameters (Th, Tc, Vc, Ts, and Tr) were successfully extracted from the measured tissue thickness changes. In addition, the unfused and fused tetanus frequencies were estimated from the obtained FI-EMS frequency curve. The WUS and ultrasonic method proposed in this study could be a valuable tool for inexpensive, non-invasive, and continuous monitoring of the skeletal muscle contractile properties.

Ultra-High-Frame-Rate Ultrasound Monitoring of Muscle Contractility Changes Due to Neuromuscular Electrical Stimulation

The quick onset of muscle fatigue is a critical issue when applying neuromuscular electrical stimulation (NMES) to generate muscle contractions for functional limb movements, which were lost/impaired due to a neurological disorder or an injury. For in situ assessment of the effect of NMES-induced muscle fatigue, a novel noninvasive sensor modality that can quantify the degraded contractility of a targeted muscle is required. In this study, instantaneous strain maps of a contracting muscle were derived from ultra-high-frame-rate (2 kHz) ultrasound images to quantify the contractility. A correlation between strain maps and isometric contraction force values was investigated. When the muscle reached its maximum contraction, the maximum and the mean values of the strain map were correlated with the force values and were further used to stage the contractility change. During the muscle activation period, a novel methodology based on the principal component regression (PCR) was proposed to explore the strain-force correlation. The quadriceps muscle of 3 able-bodied human participants was investigated during NMES-elicited isometric knee extension experiments. Strong to very strong correlation results were obtained and indicate that the proposed measurements from ultrasound images are promising to quantify the muscle contractility changes during NMES.

Central and Peripheral Shoulder Fatigue Pre-screening Using the Sigma-Lognormal Model: A Proof of Concept

Background

Clinical tests for detecting central and peripheral shoulder fatigue are limited. The discrimination of these two types of fatigue is necessary to better adapt recovery intervention. The Kinematic Theory of Rapid Human Movements describes the neuromotor impulse response using lognormal functions and has many applications in pathology detection. The ideal motor control is modeled and a change in the neuromuscular system is reflected in parameters extracted according to this theory.

Objective

The objective of this study was to assess whether a shoulder neuromuscular fatigue could be detected through parameters describing the theory, if there is the possibility to discriminate central from peripheral fatigue, and which handwriting test gives the most relevant information on fatigue.

Methods

Twenty healthy participants performed two sessions of fast stroke handwriting on a tablet, before and after a shoulder fatigue. The fatigue was in internal rotation for one session and in external rotation during the other session. The drawings consisted of simple strokes, triangles, horizontal, and vertical oscillations. Parameters of these strokes were extracted according to the Sigma–Lognormal model of the Kinematic Theory. The evolution of each participant was analyzed through a U-Mann–Whitney test for individual comparisons. A Hotelling’s T²-test and a U-Mann–Whitney test were also performed on all participants to assess the group evolution after fatigue. Moreover, a correlation among parameters was calculated through Spearman coefficients to assess intrinsic parameters properties of each handwriting test.

Results

Central and peripheral parameters were statistically different before and after fatigue with a possibility to discriminate them. Participants had various responses to fatigue. However, when considering the group, parameters related to the motor program execution showed significant increase in the handwriting tests after shoulder fatigue. The test of simple strokes permits to know more specifically where the fatigue comes from, whereas the oscillations tests were the most sensitive to fatigue.

Conclusion

The results of this study suggest that the Sigma–Lognormal model of the Kinematic Theory is an innovative approach for fatigue detection with discrimination between the central and peripheral systems. Overall, there is a possibility to implement the setting for clinics and sports personalized follow-up.

Mechanomyographic Measures of Muscle Contractile Properties are Influenced by Electrode Size and Stimulation Pulse Duration

The aim was to determine the effects of changing pulse duration and electrode size on muscle contractile properties. Thirty-six healthy young male participated in the study (age 24.8 ± 5.8 years; height 178.2 ± 0.6 cm; body mass 71.8 ± 7.3 kg; self-reported weekly moderate intensity activity 3.5 ± 1.2 h·week⁻¹). Tensiomyography was used to assess rectus femoris (RF) and vastus medialis (VM) muscles neuromuscular properties of the dominant leg according to the electrode size (3.2–5 cm) and the stimulus length (0.2, 0.5, and 1 ms). Maximal radial displacement (Dm); Contraction time (Tc); Delay time (Td); Sustained time (Ts) and Half relaxation time (Tr) were measured. Relative and absolute reliability was quantified. To analyze the effects of the electrode and the stimulus length, a repeated-measures analysis of variance was used. Dm and Tc parameters showed for both muscles an excellent relative (0.95–0.99) and absolute reliability (1.6–4.2%). However, Ts and Tr showed low values of absolute reliability (4.4–40.9%). The duration of the stimulus length applied to the RF and VM and electrode size significantly influences muscle’s contractile properties (p < 0.05; η²_p = 0.09–0.60). The Dm increases substantially as the duration of the stimulus increases and with the use of the larger electrode in both muscles. However, Tc and Td are less affected by both conditions and not entirely clear. Practically, our study suggests that a stimulus pulse duration of 1 ms together with a 5 × 5 cm electrode is necessary to reach a reliable and reproducible assessment of both RF and VM muscles contractile properties.

Quadriceps mechanomyography reflects muscle fatigue during electrical stimulus-sustained standing in adults with spinal cord injury - a proof of concept

This study investigates whether mechanomyography (MMG) produced from contracting muscles as a measure of their performance could be a proxy of muscle fatigue during a sustained functional electrical stimulation (FES)-supported standing-to-failure task. Bilateral FES-evoked contractions of quadriceps and glutei muscles, of four adults with motor-complete spinal cord injury (SCI), were used to maintain upright stance using two different FES frequencies: high frequency (HF - 35 Hz) and low frequency (LF - 20 Hz). The time at 30° knee angle reduction was taken as the point of critical "fatigue failure", while the generated MMG characteristics were used to track the pattern of force development during stance. Quadriceps fatigue, which was primarily responsible for the knee buckle, was characterized using MMG-root mean square (RMS) amplitude. A double exponential decay model fitted the MMG fatigue data with good accuracy [R2 = 0.85-0.99; root mean square error (RMSE) = 2.12-8.10] implying changes in the mechanical activity performance of the muscle's motor units. Although the standing duration was generally longer for the LF strategy (31-246 s), except in one participant, when compared to the HF strategy, such differences were not significant (p > 0.05) but suggested a faster muscle fatigue onset during HF stimulation. As MMG could discriminate between different stimulation frequencies, we speculate that this signal can quantify muscle fatigue characteristics during prolonged FES applications.

New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle

Introduction

The Mechanical Muscle Activity with Real-time Kinematics project aims to develop a device incorporating wearable sensors for arm rehabilitation following stroke. These will record kinematic activity using inertial measurement units and mechanical muscle activity. The gold standard for measuring muscle activity is electromyography; however, mechanomyography offers an appropriate alterative for our home-based rehabilitation device. We have patent filed a new laboratory-tested device that combines an inertial measurement unit with mechanomyography. We report on the validity and reliability of the mechanomyography against electromyography sensors.

Methods

In 18 healthy adults (27–82 years), mechanomyography and electromyography recordings were taken from the forearm flexor and extensor muscles during voluntary contractions. Isometric contractions were performed at different percentages of maximal force to examine the validity of mechanomyography. Root-mean-square of mechanomyography and electromyography was measured during 1 s epocs of isometric flexion and extension. Dynamic contractions were recorded during a tracking task on two days, one week apart, to examine reliability of muscle onset timing.

Results

Reliability of mechanomyography onset was high (intraclass correlation coefficient = 0.78) and was comparable with electromyography (intraclass correlation coefficient = 0.79). The correlation between force and mechanomyography was high (R² = 0.94).

Conclusion

The mechanomyography device records valid and reliable signals of mechanical muscle activity on different days.

SVR modelling of mechanomyographic signals predicts neuromuscular stimulation-evoked knee torque in paralyzed quadriceps muscles undergoing knee extension exercise

BACKGROUND AND OBJECTIVE

Using traditional regression modelling, we have previously demonstrated a positive and strong relationship between paralyzed knee extensors' mechanomyographic (MMG) signals and neuromuscular electrical stimulation (NMES)-assisted knee torque in persons with spinal cord injuries. In the present study, a method of estimating NMES-evoked knee torque from the knee extensors' MMG signals using support vector regression (SVR) modelling is introduced and performed in eight persons with chronic and motor complete spinal lesions.

METHODS

The model was developed to estimate knee torque from experimentally derived MMG signals and other parameters related to torque production, including the knee angle and stimulation intensity, during NMES-assisted knee extension.

RESULTS

When the relationship between the actual and predicted torques was quantified using the coefficient of determination (R²), with a Gaussian support vector kernel, the R² value indicated an estimation accuracy of 95% for the training subset and 94% for the testing subset while the polynomial support vector kernel indicated an accuracy of 92% for the training subset and 91% for the testing subset. For the Gaussian kernel, the root mean square error of the model was 6.28 for the training set and 8.19 for testing set, while the polynomial kernels for the training and testing sets were 7.99 and 9.82, respectively.

CONCLUSIONS

These results showed good predictive accuracy for SVR modelling, which can be generalized, and suggested that the MMG signals from paralyzed knee extensors are a suitable proxy for the NMES-assisted torque produced during repeated bouts of isometric knee extension tasks. This finding has potential implications for using MMG signals as torque sensors in NMES closed-loop systems and provides valuable information for implementing this method in research and clinical settings.

Association of anthropometric parameters with amplitude and crosstalk of mechanomyographic signals during forearm flexion, pronation and supination torque tasks

This study aimed to quantify the association of four anthropometric parameters of the human arm, namely, the arm circumference (CA), arm length (LA), skinfold thickness (ST) and inter-sensor distance (ISD), with amplitude (RMS) and crosstalk (CT) of mechanomyography (MMG) signals. Twenty-five young, healthy, male participants were recruited to perform forearm flexion, pronation and supination torque tasks. Three accelerometers were employed to record the MMG signals from the biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) at 80% maximal voluntary contraction (MVC). Signal RMS was used to quantify the amplitude of the MMG signals from a muscle, and cross-correlation coefficients were used to quantify the magnitude of the CT among muscle pairs (BB & BRA, BRA & BRD, and BB & BRD). For all investigated muscles and pairs, RMS and CT showed negligible to low negative correlations with CA, LA and ISD (r = −0.0001–−0.4611), and negligible to moderate positive correlations with ST (r = 0.004–0.511). However, almost all of these correlations were statistically insignificant (p > 0.05). These findings suggest that RMS and CT values for the elbow flexor muscles recorded and quantified using accelerometers appear invariant to anthropometric parameters.

Quantitative Assessment of Changes in Muscle Contractility Due to Fatigue During NMES: An Ultrasound Imaging Approach

OBJECTIVE

This paper investigates an ultrasound imaging-based non-invasive methodology to quantitatively assess changes in muscle contractility due to the fatigue induced by neuromuscular electrical stimulation (NMES).

METHODS

Knee extension experiments on human participants were conducted to record synchronized isometric knee force data and ultrasound images of the electrically stimulated quadriceps muscle. The data were first collected in a pre-fatigue stage and then in a post-fatigue stage. Ultrasound images were processed using a contraction rate adaptive speckle tracking algorithm. A two-dimensional strain measure field was constructed based on the muscle displacement tracking results to quantify muscle contractility.

RESULTS

Analysis of the strain images showed that, between the pre-fatigue and post-fatigue stages, there was a reduction in the strain peaks, a change in the strain peak distribution, and a decrease in an area occupied by the large positive strain.

CONCLUSION

The results indicate changes in muscle contractility due to the NMES-induced muscle fatigue.

SIGNIFICANCE

Ultrasound imaging with the proposed methodology is a promising tool for a direct NMES-induced fatigue assessment and facilitates new strategies to alleviate the effects of the NMES-induced fatigue.

Segmenting Mechanomyography Measures of Muscle Activity Phases Using Inertial Data

Electromyography (EMG) is the standard technology for monitoring muscle activity in laboratory environments, either using surface electrodes or fine wire electrodes inserted into the muscle. Due to limitations such as cost, complexity, and technical factors, including skin impedance with surface EMG and the invasive nature of fine wire electrodes, EMG is impractical for use outside of a laboratory environment. Mechanomyography (MMG) is an alternative to EMG, which shows promise in pervasive applications. The present study used an exerting squat-based task to induce muscle fatigue. MMG and EMG amplitude and frequency were compared before, during, and after the squatting task. Combining MMG with inertial measurement unit (IMU) data enabled segmentation of muscle activity at specific points: entering, holding, and exiting the squat. Results show MMG measures of muscle activity were similar to EMG in timing, duration, and magnitude during the fatigue task. The size, cost, unobtrusive nature, and usability of the MMG/IMU technology used, paired with the similar results compared to EMG, suggest that such a system could be suitable in uncontrolled natural environments such as within the home.

Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing

This paper presents a new approach to wearable hand gesture recognition and finger angle estimation based on the modified barometric pressure sensing. Barometric pressure sensors were encased and injected with VytaFlex rubber such that the rubber directly contacted the sensing element allowing pressure change detection when the encasing rubber was pressed. A wearable prototype consisting of an array of ten modified barometric pressure sensors around the wrist was developed and validated with experimental testing for three different hand gesture sets and finger flexion/extension trials for each of the five fingers. The overall hand gesture recognition classification accuracy was 94%. Further analysis revealed that the most important sensor location was the underside of the wrist and that when reducing the sensor number to only five optimally placed sensors, classification accuracy was still 90%. For continuous finger angle estimation, aggregate R² values between actual and predicted angles were thumb: 0.81 ± 0.10, index finger: 0.85±0.06, middle finger: 0.77±0.08, ring finger: 0.77 ± 0.12, and pinkie finger: 0.75 ± 0.10, and the overall average was 0.79 ± 0.05. These results demonstrate that a modified barometric pressure wristband can be used to classify hand gestures and to estimate individual finger joint angles. This approach could serve to improve the clinical treatment for upper extremity deficiencies, such as for stroke rehabilitation, by providing objective patient motor control metrics to inform and aid physicians and therapists throughout the rehabilitation process.

Assessment of Skeletal Muscle Contractile Properties by Radial Displacement: The Case for Tensiomyography

Skeletal muscle operates as a near-constant volume system; as such muscle shortening during contraction is transversely linked to radial deformation. Therefore, to assess contractile properties of skeletal muscle, radial displacement can be evoked and measured. Mechanomyography measures muscle radial displacement and during the last 20 years, tensiomyography has become the most commonly used and widely reported technique among the various methodologies of mechanomyography. Tensiomyography has been demonstrated to reliably measure peak radial displacement during evoked muscle twitch, as well as muscle twitch speed. A number of parameters can be extracted from the tensiomyography displacement/time curve and the most commonly used and reliable appear to be peak radial displacement and contraction time. The latter has been described as a valid non-invasive means of characterising skeletal muscle, based on fibre-type composition. Over recent years, applications of tensiomyography measurement within sport and exercise have appeared, with applications relating to injury, recovery and performance. Within the present review, we evaluate the perceived strengths and weaknesses of tensiomyography with regard to its efficacy within applied sports medicine settings. We also highlight future tensiomyography areas that require further investigation. Therefore, the purpose of this review is to critically examine the existing evidence surrounding tensiomyography as a tool within the field of sports medicine.

Facet Sparing Foraminal Decompression Using the Flexible Shaver Foraminotomy System: Nerve Safety, Pain Relief, and Patient Satisfaction

Background

A number of surgical options exist for decompressing lumbar foraminal stenosis. Flexible shaver foraminotomy is a recent addition to this armamentarium. While the foraminotomy device has been incorporated into clinical practice, the literature on its safety and efficacy remain limited. We aimed to evaluate nerve safety, pain relief, and patient satisfaction in a series of patients treated with the iO-Flex shaver system (Amendia, Inc., Marietta, Georgia).

Methods

Thirty-one consecutive patients with lumbar foraminal stenosis underwent foraminal decompression using the flexible microblade shaver system at 62 neuroforamina. The shavers were inserted into each foramen using an open hemilaminotomy and fluoroscopic guidance. Nerve mapping via mechanomyography (MMG) was used to ensure nerve safety. Perioperative charts were reviewed to find the incidence of neurologic complications and to quantify pain relief. Average office-based follow-up was 5.3 months. A 3-item questionnaire was administered to assess patient satisfaction during late follow-up, which occurred at an average of 21 months.

Results

No planned iO-Flex foraminotomies were aborted. Neurologic complications included transient dysesthetic pain in 1 patient (3.2%, n = 31), and transient numbness in 3 patients (9.7%, n = 31). There were no motor deficits. The composite nerve complication rate was 12.7%. Preoperative visual analog scale scores decreased from a mean of 7.1 (n = 31, standard deviation [SD] 2.0) to a mean of 3.5 (n = 30, SD 2.5). If asked to repeat their decision to do surgery, 81% of patients would redo the procedure. The rate of patient dissatisfaction was 19%.

Conclusions

Decompression of lumbar foramina using the flexible shaver system and MMG nerve mapping is safe and effective, although the short-term sensory complication with this technique may be higher than previously reported. Patient satisfaction with iO-Flex foraminotomy is comparable to reported satisfaction outcomes for traditional lumbar decompression.

Level of Evidence

4.