The effect of perspiration on the sEMG amplitude and power spectrum

Electromyography and range-of-motion measurements in German soldiers wearing different types of body armour while marching

INTRODUCTION

In this study, we used surface electromyography (EMG) electrodes in order to measure and compare activity in the neck, back and thigh muscles of soldiers wearing two different types of body armour. A secondary objective was to analyse shoulder and hip ranges of motion using inertial motion sensors.

METHODS

Fourteen male soldiers were instructed to march 6 km on a treadmill while wearing different types of body armour. All participants wore shorts and a T-shirt and the same size vest regardless of their body size. We measured back and thigh muscle activity as well as shoulder and hip ranges of motion at regular intervals during the march.

RESULTS

Over the course of a 6 km march, muscle activity was already increased to 1.3 to 2.0 times after putting on the vest and increased by up to 13 times during the march with equipment. The new vest with hip belt required higher levels of muscle activity.

CONCLUSIONS

Body armour with hip belt placed higher levels of stress on back and neck muscles during a 6 km march than without. There was no major difference between the two types of body armour in terms of thigh muscle activity.

TRIAL REGISTRATION NUMBER

DRKS00016005.

AI-Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

Motion recognition (MR)-based somatosensory interaction technology, which interprets user movements as input instructions, presents a natural approach for promoting human-computer interaction, a critical element for advancing metaverse applications. Herein, this work introduces a non-intrusive muscle-sensing wearable device, that in conjunction with machine learning, enables motion-control-based somatosensory interaction with metaverse avatars. To facilitate MR, the proposed device simultaneously detects muscle mechanical activities, including dynamic muscle shape changes and vibrational mechanomyogram signals, utilizing a flexible 16-channel pressure sensor array (weighing ≈0.38 g). Leveraging the rich information from multiple channels, a recognition accuracy of ≈96.06% is achieved by classifying ten lower-limb motions executed by ten human subjects. In addition, this work demonstrates the practical application of muscle-sensing-based somatosensory interaction, using the proposed wearable device, for enabling the real-time control of avatars in a virtual space. This study provides an alternative approach to traditional rigid inertial measurement units and electromyography-based methods for achieving accurate human motion capture, which can further broaden the applications of motion-interactive wearable devices for the coming metaverse age.

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.

Enhancing the Longevity and Functionality of Ti-Ag Dry Electrodes for Remote Biomedical Applications: A Comprehensive Study

This study aims to evaluate the lifespan of Ti-Ag dry electrodes prepared using flexible polytetrafluoroethylene (PTFE) substrates. Following previous studies, the electrodes were designed to be integrated into wearables for remote electromyography (EMG) monitoring and electrical stimulation (FES) therapy. Four types of Ti-Ag electrodes were prepared by DC magnetron sputtering, using a pure-Ti target doped with a growing number of Ag pellets. After extensive characterization of their chemical composition and (micro)structural evolution, the Ti-Ag electrodes were immersed in an artificial sweat solution (standard ISO-3160-2) at 37 °C with constant stirring. Results revealed that all the Ti-Ag electrodes maintained their integrity and functionality for 24 h. Although there was a notable increase in electrical resistivity beyond this timeframe, the acquisition and transmission of (bio)signals remained viable for electrodes with Ag/Ti ratios below 0.23. However, electrodes with higher Ag content (Ag/Ti = 0.31) became insulators after 7 days of immersion due to excessive Ag release into the sweat solution. This study concludes that higher Ag/Ti atomic ratios result in heightened corrosion processes on the electrode's surface, consequently diminishing their lifespan despite the advantages of incorporating Ag into their composition. This research highlights the critical importance of evaluating electrode longevity, especially in remote biomedical applications like smart wearables, where electrode performance over time is crucial for reliable and sustained monitoring and stimulation.

Multi-dimensional task recognition for human-robot teaming: literature review

Human-robot teams collaborating to achieve tasks under various conditions, especially in unstructured, dynamic environments will require robots to adapt autonomously to a human teammate's state. An important element of such adaptation is the robot's ability to infer the human teammate's tasks. Environmentally embedded sensors (e.g., motion capture and cameras) are infeasible in such environments for task recognition, but wearable sensors are a viable task recognition alternative. Human-robot teams will perform a wide variety of composite and atomic tasks, involving multiple activity components (i.e., gross motor, fine-grained motor, tactile, visual, cognitive, speech and auditory) that may occur concurrently. A robot's ability to recognize the human's composite, concurrent tasks is a key requirement for realizing successful teaming. Over a hundred task recognition algorithms across multiple activity components are evaluated based on six criteria: sensitivity, suitability, generalizability, composite factor, concurrency and anomaly awareness. The majority of the reviewed task recognition algorithms are not viable for human-robot teams in unstructured, dynamic environments, as they only detect tasks from a subset of activity components, incorporate non-wearable sensors, and rarely detect composite, concurrent tasks across multiple activity components.

Lower limb muscle fatigue after uphill walking in children with unilateral spastic cerebral palsy

Fatigue during walking is a common complaint in cerebral palsy (CP). The primary purpose of this study is to investigate muscle fatigue from surface electromyography (sEMG) measurements after a treadmill-based fatigue protocol with increasing incline and speed in children with CP with drop foot. The secondary purpose is to investigate whether changes in sagittal kinematics of hip, knee and ankle occur after fatigue. Eighteen subjects with unilateral spastic CP performed the protocol while wearing their ankle-foot orthosis and scored their fatigue on the OMNI scale of perceived exertion. The median frequency (MF) and root mean square (RMS) were used as sEMG measures for fatigue and linear mixed effects model were applied. The MF was significantly decreased in fatigued condition, especially in the affected leg and in the tibialis anterior and peroneus longus muscle. The RMS did not change significantly in fatigued condition, while the OMNI fatigue score indicated patients felt really fatigued. No changes in sagittal kinematics of hip, knee and ankle were found using statistical non-parametric mapping. In conclusion, the current fatigue protocol seems promising in inducing fatigue in a population with CP with drop foot and it could be used to expand knowledge on muscle fatigue during walking in CP.

Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics

Human-in-the-loop (HITL) optimization with metabolic cost feedback has been proposed to reduce walking effort with wearable robotics. This study investigates if lower limb surface electromyography (EMG) could be an alternative feedback variable to overcome time-intensive metabolic cost based exploration. For application, it should be possible to distinguish conditions with different walking efforts based on the EMG. To obtain such EMG data, a laboratory experiment was designed to elicit changes in the effort by loading and unloading pairs of weights (in total 2, 4, and 8 kg) in three randomized weight sessions for 13 subjects during treadmill walking. EMG of seven lower limb muscles was recorded for both limbs. Mean absolute values of each stride prior to and following weight loading and unloading were used to determine the detection rate (100% if every loading and unloading is detected accordingly) for changing between loaded and unloaded conditions. We assessed the use of multiple consecutive strides and the combination of muscles to improve the detection rate and estimated the related acquisition times of diminishing returns. To conclude on possible limitations of EMG for HITL optimization, EMG drift was evaluated during the Warmup and the experiment. Detection rates highly increased for the combination of multiple consecutive strides and the combination of multiple muscles. EMG drift was largest during Warmup and at the beginning of each weight session. The results suggest using EMG feedback of multiple involved muscles and from at least 10 consecutive strides (5.5 s) to benefit from the increases in detection rate in HITL optimization. In combination with up to 20 excluded acclimatization strides, after changing the assistance condition, we advise exploring about 16.5 s of walking to obtain reliable EMG-based feedback. To minimize the negative impact of EMG drift on the detection rate, at least 6 min of Warmup should be performed and breaks during the optimization should be avoided. Future studies should investigate additional feedback variables based on EMG, methods to reduce their variability and drift, and should apply the outcomes in HITL optimization with lower limb wearable robots.

Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces

A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm²) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).

Designing an efficient platform that enables verbal communication without vocalization remains a challenge. Here, the authors propose a silent speech interface by utilizing a deep learning algorithm combined with strain sensors attached near the subject’s mouth, able to collect 100 words and classify at a high accuracy rate.

sEMG-Triggered Fast Assistance Strategy for a Pneumatic Back Support Exoskeleton

To prevent lower back pain (LBP) in the industrial workplace, various powered back support exoskeletons (BSEs) have been developed. However, conventional kinematics-triggered assistance (KA) strategies induce latency, degrading assistance efficiency. Therefore, we proposed and experimentally evaluated a surface electromyography (sEMG)-triggered assistance (EA) strategy. Nine healthy subjects participated in the lifting experiments: 1) external loads test, 2) extra latency test, and 3) repetitive lifting test. In the external loads test, subject performed lifting with four different external loads (0 kg, 7.5 kg, 15 kg, and 22.5 kg). The assistance was triggered earlier by EA compared to KA from 114 ms to 202 ms, 163 ms to 269 ms for squat and stoop lifting respectively, as external loads increased from 0 kg to 22.5 kg. In the extra latency test, the effects of extra latency (manual switch, 0 ms, 100 ms and 200 ms) in EA on muscle activities were investigated. Muscle activities were minimized in the fast assistance (0 ms and 100 ms) condition and increased with extra latency. In the repetitive lifting test, the EA strategy significantly reduced L1 muscle fatigue by 70.4% in stoop lifting, compared to KA strategy. Based on the experimental results, we concluded that fast assistance triggered by sEMG improved assistance efficiency in BSE and was particularly beneficial in heavy external loads situations. The proposed assistive strategy can be used to prevent LBP by reducing back muscle fatigue and is easily applicable to various industrial exoskeleton applications.

SeNic: An Open Source Dataset for sEMG-Based Gesture Recognition in Non-ideal Conditions

In order to reduce the gap between the laboratory environment and actual use in daily life of human-machine interaction based on surface electromyogram (sEMG) intent recognition, this paper presents a benchmark dataset of sEMG in non-ideal conditions (SeNic). The dataset mainly consists of 8-channel sEMG signals, and electrode shifts from an 3D-printed annular ruler. A total of 36 subjects participate in our data acquisition experiments of 7 gestures in non-ideal conditions, where non-ideal factors of 1) electrode shifts, 2) individual difference, 3) muscle fatigue, 4) inter-day difference, and 5) arm postures are elaborately involved. Signals of sEMG are validated first in temporal and frequency domains. Results of recognizing gestures in ideal conditions indicate the high quality of the dataset. Adverse impacts in non-ideal conditions are further revealed in the amplitudes of these data and recognition accuracies. To be concluded, SeNic is a benchmark dataset that introduces several non-ideal factors which often degrade the robustness of sEMG-based systems. It could be used as a freely available dataset and a common platform for researchers in the sEMG-based recognition community. The benchmark dataset SeNic are available online via the website3.

Systematic reduction of leg muscle activity throughout a standard assessment of running footwear

PURPOSE

This study aimed to investigate whether there is a systematic change of leg muscle activity, as quantified by surface electromyography (EMG), throughout a standard running footwear assessment protocol at a predetermined running speed.

METHODS

Thirty-one physically active adults (15 females and 16 males) completed 5 testing rounds consisting of overground running trials at a speed of 3.5 m/s. The level of muscle activity from 6 major leg muscles was recorded using surface EMG. The variables assessed were the EMG total intensity as a function of time and the cumulative EMG overall intensity. Systematic effects of the chronological testing round (independent variable) on the normalized EMG overall intensity (dependent variable) were examined using Friedman analysis of variates and post hoc pairwise Wilcoxon signed-rank tests (α = 0.05).

RESULTS

There was a systematic reduction in overall EMG intensity for all 6 muscles over the time course of the running protocol (p < 0.001) until the fourth testing round when EMG intensities reached a steady state. The one exception was the biceps femoris muscle, which showed a significant reduction of EMG intensity during the stance phase (p < 0.001) but not the swing phase (p = 0.16).

CONCLUSION

While running at a predetermined speed, the neuromuscular system undergoes an adaptation process characterized by a progressive reduction in the activity level of major leg muscles. This process may represent an optimization strategy of the neuromuscular system towards a more energetically efficient running style. Future running protocols should include a familiarization period of at least 7 min or 600 strides of running at the predetermined speed.

The effect of prolonged walking on muscle fatigue and neuromuscular control in children with cerebral palsy

BACKGROUND

Muscle fatigue of the lower limbs is considered a main contributor to the perceived fatigue in children with cerebral palsy (CP) and is expected to occur during prolonged walking. In adults without disabilities, muscle fatigue has been proposed to be associated with adaptations in complexity of neuromuscular control.

RESEARCH QUESTION

What are the effects of prolonged walking on signs of muscle fatigue and complexity of neuromuscular control in children with CP?

METHODS

Ten children with CP and fifteen typically developing (TD) children performed a standardised protocol on an instrumented treadmill consisting of three stages: six-minutes walking at preferred speed (6 MW), moderate-intensity walking (MIW, with two minutes at heart rate > 70% of predicted maximal heart rate) and four-minutes walking at preferred speed (post-MIW). Electromyography (EMG) data were analysed for eight muscles of one leg during three time periods: 6 MW-start, 6 MW-end and post-MIW. Signs of muscle fatigue were quantified as changes in EMG median frequency and EMG root mean square (RMS). Complexity of neuromuscular control was quantified by total variance accounted for by one synergy (tVAF1). Muscle coactivation was assessed for antagonistic muscle pairs.

RESULTS

EMG median frequency was decreased at 6 MW-end and post-MIW compared to 6 MW-start in children with CP (p < 0.05), but not in TD children. In both groups, EMG-RMS (p < 0.01) and muscle coactivation (p < 0.01) were decreased at 6 MW-end and post-MIW compared to 6 MW-start. tVAF1 decreased slightly at 6 MW-end and post-MIW compared to 6 MW-start in both groups (p < 0.05). Changes were most pronounced from 6 MW-start to 6 MW-end.

SIGNIFICANCE

Children with CP presented signs of muscle fatigue after prolonged walking, while no effects were found for TD. Both groups showed minimal changes in tVAF1, suggesting signs of muscle fatigue are not associated with changes in complexity of neuromuscular control.

Changes in the distribution of muscle activity when using a passive trunk exoskeleton depend on the type of working task: A high-density surface EMG study

Exoskeleton effectiveness in reducing muscle efforts has been usually assessed from surface electromyograms (EMGs) collected locally. It has been demonstrated, however, muscle activity redistributes within the low back muscles during static and dynamic contractions, suggesting the need of detecting surface EMGs from a large muscle region to reliably investigate changes in global muscle activation. This study used high-density surface EMG to assess the effects of a passive trunk exoskeleton on the distribution of low back muscles' activity during different working tasks. Ten, male volunteers performed a static and a dynamic task with and without the exoskeleton. Multiple EMGs were sampled bilaterally from the lumbar erector spinae muscles while the hip and knee angles were measured unilaterally. Key results revealed for the static task exoskeleton led to a decrease in the average root mean square (RMS) amplitude (∼10%) concomitantly with a stable mean frequency and a redistribution of muscle activity (∼0.5 cm) in the caudal direction toward the end of the task. For the dynamic task, the exoskeleton reduced the RMS amplitude (∼5%) at the beginning of the task and the variability in the muscle activity distribution during the task. Moreover, a reduced range of motion in the lower limb was observed when using the exoskeleton during the dynamic task. Current results support the notion the passive exoskeleton has the potential to alleviate muscular loading at low back level especially for the static task.

Wearable sensor-driven responsive deep brain stimulation for essential tremor

BACKGROUND

Deep brain stimulation (DBS) is an effective surgical therapy for individuals with essential tremor (ET). However, DBS operates continuously, resulting in adverse effects such as postural instability or dysarthria. Continuous DBS (cDBS) also presents important practical issues including limited battery life of the implantable neurostimulator (INS). Collectively, these shortcomings impact optimal therapeutic benefit in ET.

OBJECTIVE

The goal of the study was to establish a physiology-driven responsive DBS (rDBS) system to provide targeted and personalized therapy based on electromyography (EMG) signals.

METHODS

Ten participants with ET underwent rDBS using Nexus-D, a Medtronic telemetry wand that acts as a direct conduit to the INS by modulating stimulation voltage. Two different rDBS paradigms were tested: one driven by one EMG (single-sensor) and another driven by two or more EMGs (multi-sensor). The feature(s) used in the rDBS algorithms was the pow2er in the participant's tremor frequency band derived from the sensors controlling stimulation. Both algorithms were trained on kinetic and postural data collected during DBS off and cDBS states.

RESULTS

Using established clinical scales and objective measurements of tremor severity, we confirm that both rDBS paradigms deliver equivalent clinical benefit as cDBS. Moreover, both EMG-driven rDBS paradigms delivered less total electrical energy translating to an increase in the battery life of the INS.

CONCLUSIONS

The results of this study verify that EMG-driven rDBS provides clinically equivalent tremor suppression compared to cDBS, while delivering less total electrical energy. Controlling stimulation using a dynamic rDBS paradigm can mitigate limitations of traditional cDBS systems.

Transcranial Magnetic Stimulation to Assess Exercise-Induced Neuroplasticity

Aerobic exercise facilitates neuroplasticity and has been linked to improvements in cognitive and motor function. Transcranial magnetic stimulation (TMS) is a non-invasive technique that can be used to quantify changes in neurophysiology induced by exercise. The present review summarizes the single- and paired-pulse TMS paradigms that can be used to probe exercise-induced neuroplasticity, the optimal stimulation parameters and the current understanding of the neurophysiology underlying each paradigm. Further, this review amalgamates previous research exploring the modulation of these paradigms with exercise-induced neuroplasticity in healthy and clinical populations and highlights important considerations for future TMS-exercise research.

A Single Bout of High-intensity Interval Exercise Increases Corticospinal Excitability, Brain-derived Neurotrophic Factor, and Uncarboxylated Osteolcalcin in Sedentary, Healthy Males

Exercise induces neuroplasticity in descending motor pathways facilitating motor learning, and as such it could be utilized as an intervention in neurorehabilitation, for example when re-learning motor skills after stroke. To date, however, the neurophysiological and molecular mechanisms underlying exercise-induced neuroplasticity remain largely unknown impeding the potential utilization of exercise protocols as 'motor learning boosters' in clinical and non-clinical settings. Here, we assessed corticospinal excitability, intracortical facilitation (ICF) and short-interval intracortical inhibition (SICI) using transcranial magnetic stimulation (TMS) and serum biochemical markers including brain-derived neurotrophic factor (BDNF), total and precursor cathepsin B (tCTSB, proCTSB), uncarboxylated and carboxylated osteocalcin (unOCN, cOCN) and irisin using ELISA. Measurements were carried out in sedentary, healthy males before and after a single session of high-intensity interval exercise (HIIE) or in individuals who rested and did not perform exercise (No Exercise). We found that HIIE increased corticospinal excitability, BDNF and unOCN, and decreased cOCN. We also determined that greater increases in BDNF were associated with increases in unOCN and irisin and decreases in cOCN only in participants who underwent HIIE, suggesting that unOCN and irisin may contribute to exercise-induced BDNF increases. Conversely, no changes other than a decrease in serum unOCN/tOCN were found in No Exercise participants. The present findings show that a single session of HIIE is sufficient to modulate corticospinal excitability and to increase BDNF and unOCN in sedentary, healthy males.

A New Force Myography-Based Approach for Continuous Estimation of Knee Joint Angle in Lower Limb Amputees and Able-Bodied Subjects

In this paper, we present a new method for estimating knee joint angle using force myography. The technique utilized force myogram signals from thigh muscles while subjects walked on a treadmill at different speeds, i.e., slow, medium, fast, and run. An eight-channel in-house force myography (FMG) data acquisition system was developed to collect the data wirelessly from seven healthy subjects and a transfemoral amputee. An artificial neural network was employed to estimate the knee joint angle from force myogram signals. The root-mean-square error across the healthy subjects was 6.9±1.5° at slow (1.5 km/hr), 6.5±1.3° at medium (4 km/hr), 7.4±2.2° at fast (6 km/hr) speeds, and 8.1±2.2° while running (8 km/hr). The root-mean-square error, across the trials, for the transfemoral amputee was 4.0±1.2° at slow (1 km/hr), 3.2±0.6° at medium (2 km/hr) and 3.8±0.9° at fast (3 km/hr) speeds. The proposed approach is useful in real-time gait analysis. The system is easily wearable, convenient in out-door use, portable, and commercially viable.

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.

Ultra-conformal drawn-on-skin electronics for multifunctional motion artifact-free sensing and point-of-care treatment

An accurate extraction of physiological and physical signals from human skin is crucial for health monitoring, disease prevention, and treatment. Recent advances in wearable bioelectronics directly embedded to the epidermal surface are a promising solution for future epidermal sensing. However, the existing wearable bioelectronics are susceptible to motion artifacts as they lack proper adhesion and conformal interfacing with the skin during motion. Here, we present ultra-conformal, customizable, and deformable drawn-on-skin electronics, which is robust to motion due to strong adhesion and ultra-conformality of the electronic inks drawn directly on skin. Electronic inks, including conductors, semiconductors, and dielectrics, are drawn on-demand in a freeform manner to develop devices, such as transistors, strain sensors, temperature sensors, heaters, skin hydration sensors, and electrophysiological sensors. Electrophysiological signal monitoring during motion shows drawn-on-skin electronics' immunity to motion artifacts. Additionally, electrical stimulation based on drawn-on-skin electronics demonstrates accelerated healing of skin wounds.

Domingues R, Lopes C, Borges J, Haueisen J, Vaz F. Dry Electrodes for Surface Electromyography Based on Architectured Titanium Thin Films

Electrodes of silver/silver chloride (Ag/AgCl) are dominant in clinical settings for surface electromyography (sEMG) recordings. These electrodes need a conductive electrolyte gel to ensure proper performance, which dries during long-term measurements inhibiting the immediate electrode’s reuse and is often linked to skin irritation episodes. To overcome these drawbacks, a new type of dry electrodes based on architectured titanium (Ti) thin films were proposed in this work. The architectured microstructures were zigzags, obtained with different sputtering incidence angles (α), which have been shown to directly influence the films’ porosity and electrical conductivity. The electrodes were prepared using thermoplastic polyurethane (TPU) and stainless-steel (SS) substrates, and their performance was tested in male volunteers (athletes) by recording electromyography (EMG) signals, preceded by electrode-skin impedance measurements. In general, the results showed that both SS and TPU dry electrodes can be used for sEMG recordings. While SS electrodes almost match the signal quality parameters of reference electrodes of Ag/AgCl, the performance of electrodes based on TPU functionalized with a Ti thin film still requires further improvements. Noteworthy was the clear increase of the signal to noise ratios when the thin films’ microstructure evolved from normal growth towards zigzag microstructures, meaning that further tailoring of the thin film microstructure is a possible route to achieve optimized performances. Finally, the developed dry electrodes are reusable and allow for multiple EMG recordings without being replaced.

Acute high-intensity and moderate-intensity interval exercise do not change corticospinal excitability in low fit, young adults

Previous research has demonstrated a lack of neuroplasticity induced by acute exercise in low fit individuals, but the influence of exercise intensity is unclear. In the present study, we assessed the effect of acute high-intensity (HI) or moderate-intensity (MOD) interval exercise on neuroplasticity in individuals with low fitness, as determined by a peak oxygen uptake (VO2peak) test (n = 19). Transcranial magnetic stimulation (TMS) was used to assess corticospinal excitability via area under the motor evoked potential (MEP) recruitment curve before and following training. Corticospinal excitability was unchanged after HI and MOD, suggesting no effect of acute exercise on neuroplasticity as measured via TMS in sedentary, young individuals. Repeated bouts of exercise, i.e., physical training, may be required to induce short-term changes in corticospinal excitability in previously sedentary individuals.

Techniques for Improving the Reliability of Prosthesis Wearer Muscle Signals Using Pressure and EMG Sensors

In this study, we propose a technique to increase the reliability of muscle signals in case of weak muscle signal measurements using pressure sensors in a prosthesis wearer for measuring the degree of contraction of the muscle. This technique is applied when determining the intention of the wearer of an intelligent prosthesis. Through this technique, it is possible to determine the intention of a prosthesis wearer, which was previously not possible.

Donut-Shaped Stretchable Kirigami: Enabling Electronics to Integrate with the Deformable Muscle

Electronic devices used to record biological signals are important in neuroscience, brain-machine interfaces, and medical applications. Placing electronic devices below the skin surface and recording the muscle offers accurate and robust electromyography (EMG) recordings. The device stretchability and flexibility must be similar to the tissues to achieve an intimate integration of the electronic device with the biological tissues. However, conventional elastomer-based EMG electrodes have a Young's modulus that is ≈20 times higher than that of muscle. In addition, these stretchable devices also have an issue of displacement on the tissue surface, thereby causing some challenges during accurate and robust EMG signal recordings. In general, devices with kirigami design solve the issue of the high Young's modulus of conventional EMG devices. In this study, donut-shaped kirigami bioprobes are proposed to reduce the device displacement on the muscle surface. The fabricated devices are tested on an expanding balloon and they show no significant device (microelectrode) displacement. As the package, the fabricated device is embedded in a dissolvable material-based scaffold for easy-to-use stretchable kirigami device in an animal experiment. Finally, the EMG signal recording capability and stability using the fabricated kirigami device is confirmed in in vivo experiments without significant device displacements.

Effects of wearable power assist device on low back fatigue during repetitive lifting tasks

BACKGROUND

A wearable power assist device was developed to reduce the stress on the lower back by using pneumatic muscles. The purpose of this study was to explore whether the assist device could reduce the activity or fatigue of lower back muscles during a repetitive lifting task.

METHODS

Twelve male subjects participated in the study. Electromyography of the thoracic erector spinae at the T9 level and lumbar erector spinae at the L3 level was recorded during 90 lifts in 15 min. Subjects' heart rate and Borg's Rate of Perceived Exertion Scale score were recorded during lifting sessions.

FINDINGS

The electromyography amplitude of thoracic erector spinae and lumbar erector spinae was only increased by 32.45% and 40.17%, respectively, when the wearable power assist device was used when comparing the pre- and post-lifting task. Whereas it was increased by 125.78% and 85.90%, respectively, when the wearable power assist device was not used. The decrease in electromyography median frequency from the start until the end of the lifting session was significantly lower when wearing the assist device for the thoracic erector spinae (2.72% vs 7.45%) and the lumbar erector spinae (3.91% vs 13.70%). Use of the assist device also significantly reduced the percentage change in heart rate and Borg Scale (p < 0.05).

INTERPRETATION

The use of the wearable power assist device showed less back muscle contraction compared to the no-use, which can potentially minimize the level of back muscle fatigue across the lifting session.

Directional Forgetting for Stable Co-Adaptation in Myoelectric Control

Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.

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.

Arrhythmia Evaluation in Wearable ECG Devices

This study evaluates four databases from PhysioNet: The American Heart Association database (AHADB), Creighton University Ventricular Tachyarrhythmia database (CUDB), MIT-BIH Arrhythmia database (MITDB), and MIT-BIH Noise Stress Test database (NSTDB). The ANSI/AAMI EC57:2012 is used for the evaluation of the algorithms for the supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), atrial fibrillation (AF), and ventricular fibrillation (VF) via the evaluation of the sensitivity, positive predictivity and false positive rate. Sample entropy, fast Fourier transform (FFT), and multilayer perceptron neural network with backpropagation training algorithm are selected for the integrated detection algorithms. For this study, the result for SVEB has some improvements compared to a previous study that also utilized ANSI/AAMI EC57. In further, VEB sensitivity and positive predictivity gross evaluations have greater than 80%, except for the positive predictivity of the NSTDB database. For AF gross evaluation of MITDB database, the results show very good classification, excluding the episode sensitivity. In advanced, for VF gross evaluation, the episode sensitivity and positive predictivity for the AHADB, MITDB, and CUDB, have greater than 80%, except for MITDB episode positive predictivity, which is 75%. The achieved results show that the proposed integrated SVEB, VEB, AF, and VF detection algorithm has an accurate classification according to ANSI/AAMI EC57:2012. In conclusion, the proposed integrated detection algorithm can achieve good accuracy in comparison with other previous studies. Furthermore, more advanced algorithms and hardware devices should be performed in future for arrhythmia detection and evaluation.

Comparison of six electromyography acquisition setups on hand movement classification tasks

Hand prostheses controlled by surface electromyography are promising due to the non-invasive approach and the control capabilities offered by machine learning. Nevertheless, dexterous prostheses are still scarcely spread due to control difficulties, low robustness and often prohibitive costs. Several sEMG acquisition setups are now available, ranging in terms of costs between a few hundred and several thousand dollars. The objective of this paper is the relative comparison of six acquisition setups on an identical hand movement classification task, in order to help the researchers to choose the proper acquisition setup for their requirements. The acquisition setups are based on four different sEMG electrodes (including Otto Bock, Delsys Trigno, Cometa Wave + Dormo ECG and two Thalmic Myo armbands) and they were used to record more than 50 hand movements from intact subjects with a standardized acquisition protocol. The relative performance of the six sEMG acquisition setups is compared on 41 identical hand movements with a standardized feature extraction and data analysis pipeline aimed at performing hand movement classification. Comparable classification results are obtained with three acquisition setups including the Delsys Trigno, the Cometa Wave and the affordable setup composed of two Myo armbands. The results suggest that practical sEMG tests can be performed even when costs are relevant (e.g. in small laboratories, developing countries or use by children). All the presented datasets can be used for offline tests and their quality can easily be compared as the data sets are publicly available.

Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue

Objective

Surface electromyography has been a long-standing source of signals for control of powered prosthetic devices. By contrast, force myography is a more recent alternative to surface electromyography that has the potential to enhance reliability and avoid operational challenges of surface electromyography during use. In this paper, we report on experiments conducted to assess improvements in classification of surface electromyography signals through the addition of collocated force myography consisting of piezo-resistive sensors.

Methods

Force sensors detect intrasocket pressure changes upon muscle activation due to changes in muscle volume during activities of daily living. A heterogeneous sensor configuration with four surface electromyography-force myography pairs was investigated as a control input for a powered upper limb prosthetic. Training of two different multilevel neural perceptron networks was employed during classification and trained on data gathered during experiments simulating socket shift and muscle fatigue.

Results

Results indicate that intrasocket pressure data used in conjunction with surface EMG data can improve classification of human intent and control of a powered prosthetic device compared to traditional, surface electromyography only systems.

Significance

Additional sensors lead to significantly better signal classification during times of user fatigue, poor socket fit, as well as radial and ulnar wrist deviation. Results from experimentally obtained training data sets are presented.

Translational Motion Tracking of Leg Joints for Enhanced Prediction of Walking Tasks

OBJECTIVE

Walking task prediction in powered leg prostheses is an important problem in the development of biomimetic prosthesis controllers. This paper proposes a novel method to predict upcoming walking tasks by estimating the translational motion of leg joints using an integrated inertial measurement unit.

METHODS

We asked six subjects with unilateral transtibial amputations to traverse flat ground, ramps, and stairs using a powered prosthesis while inertial signals were collected. We then performed an offline analysis in which we simulated a real-time motion tracking algorithm on the inertial signals to estimate knee and ankle joint translations, and then used pattern recognition separately on the inertial and translational signal sets to predict the target walking tasks of individual strides.

RESULTS

Our analysis showed that using inertial signals to derive translational signals enabled a prediction error reduction of 6.8% compared to that attained using the original inertial signals. This result was similar to that seen by addition of surface electromyography sensors to integrated sensors in previous work, but was effected without adding any extra sensors. Finally, we reduced the size of the translational set to that of the inertial set and showed that the former still enabled a composite error reduction of 5.8%.

CONCLUSION AND SIGNIFICANCE

These results indicate that translational motion tracking can be used to substantially enhance walking task prediction in leg prostheses without adding external sensing modalities. Our proposed algorithm can thus be used as a part of a task-adaptive and fully integrated prosthesis controller.

Motor unit firing frequency of lower limb muscles during an incremental slide board skating test

This study investigated how the combination of workload and fatigue affected the frequency components of muscle activation and possible recruitment priority of motor units during skating to exhaustion. Ten male competitive speed skaters performed an incremental maximal test on a slide board. Activation of six muscles from the right leg was recorded throughout the test. A time-frequency analysis was performed to compute overall, high, and low frequency bands from the whole signal at 10, 40, 70, and 90% of total test time. Overall activation increased for all muscles throughout the test (p < 0.05 and ES > 0.80). There was an increase in low frequency (90 vs. 10%, p = 0.035, ES = 1.06) and a decrease in high frequency (90 vs. 10%, p = 0.009, ES = 1.38, and 90 vs. 40%, p = 0.025, ES = 1.12) components of gluteus maximus. Strong correlations were found between the maximal cadence and vastus lateralis, gluteus maximus and gluteus medius activation at the end of the test. In conclusion, the incremental skating test lead to an increase in activation of lower limb muscles, but only gluteus maximus was sensitive to changes in frequency components, probably caused by a pronounced fatigue.

Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis

Restoring locomotion functionality of transfemoral amputees is essential for early rehabilitation treatment and for preserving mobility and independence in daily life. Research in wearable robotics fostered the development of innovative active mechatronic lower-limb prostheses designed with the goal to reduce the cognitive and physical effort of lower-limb amputees in rehabilitation and daily life activities. To ensure benefits to the users, active mechatronic prostheses are expected to be aware of the user intention and properly interact in a closed human-in-the-loop paradigm. In the state of the art various cognitive interfaces have been proposed to online decode the user's intention. Electromyography in combination with mechanical sensing such as inertial or pressure sensors is a widely adopted solution for driving active mechatronic prostheses. In this framework, researchers also explored targeted muscles re-innervation for an objective-oriented surgical amputation promoting wider usability of active prostheses. However, information kept by the neural component of the cognitive interface deteriorates in a prolonged use scenario due to electrodes-related issues, thereby undermining the correct functionality of the active prosthesis. The objective of this work is to present a novel controller for an active transfemoral prosthesis based on whole body awareness relying on a wireless distributed non-invasive sensory apparatus acting as cognitive interface. A finite-state machine controller based on signals monitored from the wearable interface performs subject-independent intention detection of functional tasks such as ground level walking, stair ascent, and sit-to-stand maneuvres and their main sub-phases. Experimental activities carried out with four transfemoral amputees (among them one dysvascular) demonstrated high reliability of the controller capable of providing 100% accuracy rate in treadmill walking even for weak subjects and low walking speeds. The minimum success rate was of 94.8% in performing sit-to-stand tasks. All the participants showed high confidence in using the transfemoral active prosthesis even without training period thanks to intuitiveness of the whole body awareness controller.

Curved Microneedle Array-Based sEMG Electrode for Robust Long-Term Measurements and High Selectivity

Surface electromyography is widely used in many fields to infer human intention. However, conventional electrodes are not appropriate for long-term measurements and are easily influenced by the environment, so the range of applications of sEMG is limited. In this paper, we propose a flexible band-integrated, curved microneedle array electrode for robust long-term measurements, high selectivity, and easy applicability. Signal quality, in terms of long-term usability and sensitivity to perspiration, was investigated. Its motion-discriminating performance was also evaluated. The results show that the proposed electrode is robust to perspiration and can maintain a high-quality measuring ability for over 8 h. The proposed electrode also has high selectivity for motion compared with a commercial wet electrode and dry electrode.