Electromyogram Signals Research Articles

Characteristics of Human Postactivation Effect of Skeletal Muscles Using Spectral and Non-Linear Parameters of the Surface Electromyogram

Postactivation effect (PAE, postactivation phenomenon) is a specific type of involuntary muscle tone (tonic automatism) which is generated in the “tonogenic” structures of the brain, presumably without the “sensory copy” and “motor command” mechanisms. In this regard, the electromyogram (EMG) signal of PAE may have a simpler temporal signal structure compared to PAE-inducing voluntary activity. The purpose of this work is to characterize the temporal structure and complexity of surface EMG (sEMG) of the human deltoid and biceps brachii muscles using fractal (D) and correlation dimensions (Dc). It was found that in deltoid muscles the value of D was 1.78–1.81 both during PAE and voluntary effort (p 0.05). Dc (approximately 4.0–4.2) also did not differ between PAE and voluntary effort, although the average frequency of the sEMG spectrum during PAE was 15–16 Hz (p 0.05) higher compared to voluntary effort. In biceps brachii muscles, the D value was 1.8 during PAE and 1.62 during voluntary effort (p 0.05). Dc values did not differ between PAE and voluntary contraction (4–4.8). Thus, despite the supposed difference in the central organization of PAE and voluntary effort, the temporal structure of their sEMG did not differ, indicating that isometric voluntary effort and involuntary tone in the form of PAE share a common principle of sEMG signal generation. At the same time, the differences in the frequency of the sEMG spectrum indicate that the organization of sEMG signal during PAE is specific on the level of the motoneuron pool.

Feasibility of Transformer Model for User Authentication Using Electromyogram Signals

Transformer models are widely used in natural language processing (NLP) and time-series data analysis. Applications of these models include prediction systems and hand gesture recognition using electromyogram (EMG) signals. However, in the case of time-series analysis, the models perform similarly to traditional networks, contrary to expectations. This study aimed to compare the performance of the transformer model and its various modified versions in terms of accuracy through a user authentication system using EMG signals, which exhibit significant variability and pose challenges in feature extraction. A Siamese network was employed to distinguish subtle differences in the EMG signals between users, using Euclidean distance. Data from 100 individuals were used to create a challenging scenario while ensuring accuracy. Three scenarios were considered: data preprocessing, integration with existing models, and the modification of the internal structure of the transformer model. The method that achieved the highest accuracy was the bidirectional long short-term memory (BiLSTM)–transformer approach. Based on this, a network was further constructed and optimized, resulting in a user authentication accuracy of 99.7% using EMG data from 100 individuals.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience.

Many studies have investigated the manipulation of a virtual upper arm using electromyogram (EMG); however, these studies primarily used a machine learning model or trigger control for this purpose. Furthermore, most of them could only display the constant motion of the virtual arm because the motion to be displayed was selected by pattern recognition or trigger control. In addition, these studies did not examine changes in the electromyographic signals after experiencing the virtual arm. By contrast, we propose a real-time, continuous, learning-free avatar that manipulates the virtual arm with electromyogram signals or physio-avatar EMG biofeedback (EB). The goal of the physio-avatar EB system is to induce physiological changes through experiential interactions. We explored the possibility of changing motor control strategies by applying the system to healthy individuals as a case study. An intervention method that provided an experience of a body different from one's own was conducted on seven participants using a time-invariant calculation algorithm to determine the joint angles of the avatar. Control strategies for an indicator of the equilibrium point in the baseline and adaptation phases were determined to evaluate the physio-avatar EB intervention effect. The similarity of these BL and adaptation control strategies compared to those used during the washout period was assessed using the coefficient of determination. The accuracy and reliability of the virtual reality (VR) system were evaluated by comparison with existing studies and the required specs. Changes in motor control strategies due to the physio-avatar EB system were observed in four experiments, where the participants gradually returned to their pre-intervention control strategies. This result can be attributed to the aftereffects caused by error learning. This implies that the developed system influenced their motor control strategies. The number of EMG acquisition bits was 16 bits, and the sampling rate was 1,000 Hz. The refresh rate of the head-mounted display was 90 Hz, and its resolution was for a single eye. Additionally, the simulation frame rate was 30 FPS. These values were adequate compared to existing studies and required specs. The essential contribution of this study is the development of an avatar that is controlled by a different method than has been used in previous studies and the demonstration of changes in a subject's muscle activity after they experience an avatar. In the future, the clinical efficacy of the proposed system will be evaluated with actual patients.

EMG and SSVEP-based bimodal estimation of elbow angle trajectory

Detecting intentions and estimating movement trajectories in a human–machine interface (HMI) using electromyogram (EMG) signals is particularly challenging, especially for individuals with movement impairments. Therefore, incorporating additional information from other biological sources, potential discrete information in the movement, and the EMG signal can be practical. This study combined EMG and target information to enhance estimation performance during reaching movements. EMG activity of the shoulder and arm muscles, elbow angle, and the electroencephalogram signals of ten healthy subjects were recorded while they reached blinking targets. The reaching target was recognized by steady-state visual evoked potential (SSVEP). The selected target’s final angle and EMG were then mapped to the elbow angle trajectory. The proposed bimodal structure, which integrates EMG and final elbow angle information, outperformed the EMG-based decoder. Even under conditions of higher fatigue, the proposed structure provided better performance than the EMG decoder. Including additional information about the recognized reaching target in the trajectory model improved the estimation of the reaching profile. Consequently, this study’s findings suggest that bimodal decoders are highly beneficial for enhancing assistive robotic devices and prostheses, especially for real-time upper limb rehabilitation.

Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition.

Myoelectric control, the use of electromyogram (EMG) signals generated during muscle contractions to control a system or device, is a promising input, enabling always-available control for emerging ubiquitous computing applications. However, its widespread use has historically been limited by the need for user-specific machine learning models because of behavioural and physiological differences between users. Leveraging the publicly available 612-user EMG-EPN612 dataset, this work dispels this notion, showing that true zero-shot cross-user myoelectric control is achievable without user-specific training. By taking a discrete approach to classification (i.e., recognizing the entire dynamic gesture as a single event), a classification accuracy of 93.0% for six gestures was achieved on a set of 306 unseen users, showing that big data approaches can enable robust cross-user myoelectric control. By organizing the results into a series of mini-studies, this work provides an in-depth analysis of discrete cross-user models to answer unknown questions and uncover new research directions. In particular, this work explores the number of participants required to build cross-user models, the impact of transfer learning for fine-tuning these models, and the effects of under-represented end-user demographics in the training data, among other issues. Additionally, in order to further evaluate the performance of the developed cross-user models, a completely new dataset was created (using the same recording device) that includes known covariate factors such as cross-day use and limb-position variability. The results show that the large data models can effectively generalize to new datasets and mitigate the impact of common confounding factors that have historically limited the adoption of EMG-based inputs.

Force estimation for human–robot interaction using electromyogram signals from varied arm postures

In this paper, a system for force estimation based on surface electromyography signals measured from the eight channels of the Myo armband is presented. We evaluated nineteen regression models to continuously estimate force in three scenario cases to cover the natural movement in two degrees of freedom planer rehabilitation mobile robots. The best estimation model that could overcome the challenge in a variety of different scenarios was determined. Based on the experimental results, the Gaussian process regression model performed best, giving a root mean square error in the overall range of 1.18–1.77 N. Additionally, the results showed that the exponential algorithm outperformed other solutions, significantly reducing the force estimation error.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects.

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

The Effects of the StartReact on Reaction Time, Rate of Force Development, and Muscle Activity in Biceps Brachii

ABSTRACTThe StartReact test, increasingly popular for assessing cortico‐reticular functioning, is a valid method to influence the firing of reticulospinal tract neurons noninvasively. However, there remains limited evidence on how different stimuli employed in the StartReact test impact motor output in humans. The present study tested elbow flexor responses of 33 adults (aged 26–48 years) to visual stimuli only (LED light), audio‐visual (80 dB) stimuli, and startle‐inducing audio‐visual (120 dB) stimuli sitting with the arm supinated in an electromechanical dynamometer. Surface electromyogram (EMG) recorded muscle activity from the right biceps brachii muscle. Participants were presented with 20 stimuli for each of the three conditions in pseudorandom order with interstimulus intervals of ~8 s. Reaction times were calculated from the stimulus trigger to the initial rise in the EMG signal above 7 × SD from baseline. Rate of torque development (RTD) and EMG signals were recorded throughout and analyzed over their initial 50 ms and 100 ms time‐windows. Reaction times were reduced from visual (169 ± 23) to audio‐visual (140 ± 23) and further reduced to startle‐inducing audio‐visual stimuli (108 ± 19, p &lt; 0.001). While RTD and EMG were consistently greatest following startle‐inducing stimuli (p &lt; 0.001), they were also enhanced following all audio‐visual stimuli over 100 ms (p &lt; 0.05). It appears that startle‐inducing audio‐visual stimuli result in shorter reaction times, increased RTD, and enhanced muscle activity within the initial 50 ms, likely from subcortical upregulation. However, the 100 ms time‐window suggests cortical upregulation following all audio‐visual stimuli considering the longer transmission times.

Real-time identification of noise type contaminated in surface electromyogram signals using efficient statistical features

Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.

Comparing online wrist and forearm EMG-based control using a rhythm game-inspired evaluation environment

Objective.The use of electromyogram (EMG) signals recorded from the wrist is emerging as a desirable input modality for human-machine interaction (HMI). Although forearm-based EMG has been used for decades in prosthetics, there has been comparatively little prior work evaluating the performance of wrist-based control, especially in online, user-in-the-loop studies. Furthermore, despite different motivating use cases for wrist-based control, research has mostly adopted legacy prosthesis control evaluation frameworks.Approach.Gaining inspiration from rhythm games and the Schmidt's law speed-accuracy tradeoff, this work proposes a new temporally constrained evaluation environment with a linearly increasing difficulty to compare the online usability of wrist and forearm EMG. Compared to the more commonly used Fitts' Law-style testing, the proposed environment may offer different insights for emerging use cases of EMG as it decouples the machine learning algorithm's performance from proportional control, is easily generalizable to different gesture sets, and enables the extraction of a wide set of usability metrics that describe a users ability to successfully accomplish a task at a certain time with different levels of induced stress.Main results.The results suggest that wrist EMG-based control is comparable to that of forearm EMG when using traditional prosthesis control gestures and can even be better when using fine finger gestures. Additionally, the results suggest that as the difficulty of the environment increased, the online metrics and their correlation to the offline metrics decreased, highlighting the importance of evaluating myoelectric control in real-time evaluations over a range of difficulties.Significance.This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.

Isometric Plantarflexion Moment Prediction Based on a Compartment-Specific HD-sEMG-Driven Musculoskeletal Model.

electromyogram (EMG)-driven musculoskeletal models have been widely used to investigate human movements while existing EMG-driven models commonly neglect regional heterogeneity in anatomy and activation within a skeletal muscle. To consider neuromuscular compartment anatomy and activation, a subject- and compartment-specific EMG-driven model was developed for isometric plantarflexion moment prediction. the model was hill-type consisting of gastrocnemius medialis, gastrocnemius lateralis, and soleus around the ankle joint, and each muscle was discretised into four compartments. The moment arms of each compartment were determined using magnetic resonance imaging and the compartment activation was calculated based on high-density surface EMG signals. And the hill-type compartment parameters were tuned in a calibration process. The developed compartment-specific model and a generic EMG-driven model were examined by comparing their predicted net ankle moments with measurements obtained while subjects performed isometric plantarflexion tasks at different contraction levels. compared to the generic EMG-driven model, the isometric plantarflexion moment prediction using the compartment-specific model was more accurate at all contraction levels, with the average prediction error decreasing from average 13.81% to 10.11%. The contraction of each compartment was found to be generally non-uniform at all contraction levels. the developed compartment-specific model enabled accurate prediction of isometric plantarflexion moment and the simulation of non-uniform muscular contraction, which is more physiologically appropriate than the existing EMG-driven models. the proposed compartment-specific formulation opens new perspectives for subject-specific musculoskeletal modelling, which has great potential in understanding regional characteristics of the neuromuscular activities.

A Novel Design of an Oral Appliance for Monitoring Electromyograms of the Genioglossus Muscle in Obstructive Sleep Apnea Syndrome.

Obstructive sleep apnea (OSA) is a prevalent source of sleep-disordered breathing. OSA is most commonly associated with dysfunctions in the genioglossus (GG) muscle. In this study, we present the first version of a medical device that produces an electromyogram (EMG) of the GG. The prototype is composed of a (custom-made) 3D-printed mouthpiece. Impressions were taken for the lower arch and scanned with a lab scanner to be converted into digital impressions. ExoCad software was used to design the appliance. Fusion 360 software was then used to modify the design and create tubes to house the electrodes in a bilateral configuration to secure excellent and continuous contact with the GG muscle. Silver-silver chloride electrodes were incorporated within the appliance through the created tubes to produce a muscle EMG. In this preliminary prototype, an EMG amplifier was placed outside the mouth, and isolated electric wires were connected to the amplifier input. To test the design, we ran experiments to acquire EMG signals from a group of OSA patients and a control group in wakefulness. The GG EMGs were acquired from the participants for 60 s in a resting state whereby they rested their tongues without performing any movement. Then, the subjects pushed their tongues against the fontal teeth with steady force while keeping the mouth closed (active state). Several features were extracted from the acquired EMGs, and statistical tests were applied to evaluate the significant differences in these features between the two groups. The results showed that the mean power and standard deviation were higher in the control group than in the OSA group (p < 0.01). Regarding the wavelength during the active state, the control group had a significantly longer wavelength than the OSA group (p < 0.01). Meanwhile, the mean frequency was higher in the OSA group (p < 0.01) at rest. These findings support research that showed that impairment in GG activity continues in the daytime and does not only occur during sleep. Future research should focus on developing the device to be more user-friendly and easily used at home during wakefulness and sleep.

High stretchable and self-adhesive multifunctional hydrogel for wearable and flexible sensors

Ionic conductive hydrogel has recently garnered significant research attention due to its potential applications in the field of wearable and flexible electronics. Nonetheless, the integration of multifunctional and synergistic advantages, including reliable electronic properties, high swelling capacity, exceptional mechanical characteristics, and self-adhesive properties, presents an ongoing challenge. In this study, we have developed an ionic conductive hydrogel through the co-polymerization of 4-Acryloylmorpholine (ACMO) and sodium acrylate using UV curing technology. The hydrogel exhibits excellent mechanical properties, high conductivity, superior swelling capacity, and remarkable self-adhesive attributes. The hydrogel serves as a highly sensitive strain sensor, enabling precise monitoring of both substantial and subtle human motions. Furthermore, the hydrogel demonstrates the capability to adhere to human skin, functioning as a human-machine interface for the detection of physiological signals, including electromyogram (EMG) signals, with low interfacial impedance. This work is anticipated to yield a new class of stretchable and conductive materials with diverse potential applications, ranging from flexible sensors and wearable bio-electronics to contributions in the field of artificial intelligence.

A Human-Computer Interaction Strategy for An FPGA Platform Boosted Integrated "Perception-Memory" System Based on Electronic Tattoos and Memristors.

The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted system that enables the sensing, recognition, and memory for human-computer interaction is reported by the combination of ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)-based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception-memory" integrated system not only serves the disabled, enhancing human-computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.

A 62.2dB SNDR Event-Driven Level-Crossing ADC with SAR-Assisted Delay Compensation Loop for Time-Sparse Biomedical Signal Acquisition.

This paper proposed an event-driven clockless level-crossing ADC (LC-ADC) suitable for biomedical applications. Thanks to the LC loop, the sampling rate of the converter automatically adapts to the input activities. Activity-dependent power consumption and data compression can thus be realized, saving system power, especially during time-sparse signal acquisition. Meanwhile, a SAR-assisted loop is exploited to resolve the loop-delay-induced distortion in conventional LC-ADC. Therefore, the resolution and power efficiency of the LC-ADC are improved effectively while maintaining the event-driven feature. Implemented in a 55nm process, the proposed LC-ADC achieves a scalable power consumption and a peak SNDR of 62.2dB for a 20kHz input. It also achieves a Walden FoM of 29.7fJ/conv.-step and a Schreier FoM of 158.6dB, which is best in class, without using off-chip calibration. Sub μW power is realized when the input frequency is below 1.5kHz. The proposed LC-ADC is also verified by simulated electrocardiogram (ECG), neural spike, and electromyogram (EMG) signals. It provides a ~7X data compression for ECG input, providing an attractive solution for time-sparse signal acquisition in biomedical applications.

Simple Siamese Model with Long Short-Term Memory for User Authentication with Field-Programmable Gate Arrays

Recent studies have focused on user authentication methods that use biometric signals such as electrocardiogram (ECG) and photo-plethysmography (PPG). These authentication technologies have advantages such as ease of acquisition, strong security, and the capability for non-aware authentication. This study addresses user authentication using electromyogram (EMG) signals, which are particularly easy to acquire, can be fabricated in a wearable form such as a wristwatch, and are readily expandable with technologies such as human–machine interface. However, despite their potential, they often exhibit lower accuracy (approximately 90%) than traditional methods such as fingerprint recognition. Accuracy can be improved using complex algorithms and multiple biometric authentication technologies; however, complex algorithms use substantial hardware resources, making their application to wearable devices difficult. In this study, a simple Siamese model with long short-term memory (LSTM) (SSiamese-LSTM) is proposed to achieve a high accuracy of over 99% with limited resources suitable for wearable devices. The hardware implementation was accomplished using field-programmable gate arrays (FPGAs). In terms of accuracy, EMG measurement results from Chosun University were used, and data from 100 individuals were employed for verification. The proposed digital logic will be integrated with an EMG sensor in the form of a watch that can be used for user authentication.

Relation Between Abnormal Spontaneous Brain Activity and Altered Neuromuscular Activation of Lumbar Paraspinal Muscles in Chronic Low Back Pain

ObjectiveTo investigate the neural mechanism underlying functional reorganization and motor coordination strategies in patients with chronic low back pain (cLBP). DesignA case-control study based on data collected during routine clinical practice. SettingThis study was conducted at a university hospital. ParticipantsFifteen patients with cLBP and 15 healthy controls. InterventionsNot applicable. Main Outcome MeasuresWhole brain blood oxygen level-dependent signals were measured using functional magnetic resonance imaging and amplitude of low-frequency fluctuation (ALFF) method to identify pain-induced changes in regional spontaneous brain activity. A novel approach based on the surface electromyogram (EMG) system and fine-wire electrodes was used to record EMG signals in the deep multifidus, superficial multifidus, and erector spinae. ResultsIn cLBP, compared with healthy groups, ALFF was higher in the medial prefrontal, primary somatosensory, primary motor, and inferior temporal cortices, whereas it was lower in the cerebellum and anterior cingulate and posterior cingulate cortices. Furthermore, the decrease in the average EMG activity of the 3 lumbar muscles in the cLBP group was positively correlated with the ALFF values of the primary somatosensory cortex, motor cortex, precuneus, and middle temporal cortex but significantly negatively correlated with the ALFF values of the medial prefrontal and inferior temporal cortices. Interestingly, the correlation between the functional activity in the cerebellum and the EMG activity varied in the lumbar muscles. ConclusionsThese findings suggest a functional association between changes in spontaneous brain activity and altered voluntary neuromuscular activation patterns of the lumbar paraspinal muscles, providing new insights into the mechanisms underlying pain chronicity as well as important implications for developing novel therapeutic targets of cLBP.

Biomechanical Parameters for Rigidity Assessment in Parkinson's Disease: A Scoping Review

Context: Rigidity is a cardinal motor symptom in Parkinson's disease (PD), and evaluating these symptoms remains a significant challenge for researchers and neurologists. This scoping review utilizes a methodology to extract, categorize, and compare biomechanical parameters (MPs) for rigidity assessment from studies spanning the years 1980 to 2023. Evidence Acquisition: Electronic databases, including Google Scholar, ScienceDirect, PubMed, Springer, and Civilica, were systematically searched to identify research on rigidity assessment in PD utilizing various biomechanical parameters. Additionally, reference lists of identified papers were reviewed to identify additional relevant studies. A total of 53 articles met the inclusion criteria and were evaluated. Findings: Based on the results of 53 studies, viscoelastic parameters, electromyogram (EMG) signal measurements, impedance, work, and torque-angle slopes (stiffness) were identified as essential and commonly recommended parameters for rigidity evaluation in Parkinsonian patients. Furthermore, correlations between these parameters and clinical scores have been reported. Introducing biomechanical parameters capable of identifying rigidity and its severity, along with their classification, could contribute to a better understanding, modeling, and evaluation of rigidity. Conclusions: This review could be of interest to both biomechanists and neurologists, facilitating a common language for discussing aspects of rigidity in diseases such as Parkinson's.

An Embedded Electromyogram Signal Acquisition Device.

In this study, we design an embedded surface EMG acquisition device to conveniently collect human surface EMG signals, pursue more intelligent human-computer interactions in exoskeleton robots, and enable exoskeleton robots to synchronize with or even respond to user actions in advance. The device has the characteristics of low cost, miniaturization, and strong compatibility, and it can acquire eight-channel surface EMG signals in real time while retaining the possibility of expanding the channel. This paper introduces the design and function of the embedded EMG acquisition device in detail, which includes the use of wired transmission to adapt to complex electromagnetic environments, light signals to indicate signal strength, and an embedded processing chip to reduce signal noise and perform filtering. The test results show that the device can effectively collect the original EMG signal, which provides a scheme for improving the level of human-computer interactions and enhancing the robustness and intelligence of exoskeleton equipment. The development of this device provides a new possibility for the intellectualization of exoskeleton systems and reductions in their cost.