Pain State Classification of Stiff Knee Joint Using Electromyogram for Robot-Based Post-Fracture Rehabilitation Training

Osteoarthritis is a degenerative joint disease that is caused by many factors, among others; allergic reactions, infections, genetics, and because of the aging process of a person. The disease that is often experienced by the elderly is a joint disease or osteoarthritis. Osteoarthritis ranks first chronic diseases suffered by older people (44%), followed by hypertension (39%), reduced hearing or deafness (28%) and heart disease (27%). This is common in the joints of the body weight retention (knee and hip), the joints in the fingers and spine. This study aimed to identify the effect of ginger compress to decrease the level of pain in osteoarthritis patients at health center UPT Guluk-guluk. This research is pre-experiment is one group prepost test design, with sampling purposive technique. The population in this study were 58 people with a total sample of 41 people. Data analysis in this study using the Wilcoxon test. The results of this study previously conducted before the pain level in patients given ginger compress majority of respondents experienced a level of moderate pain (73.2%) and the level of pain in patients after given a ginger compress majority of respondents experienced a level of moderate pain (58.5%). Analysis test the effect of ginger compress to decrease the level of pain in osteoarthritis patients was measured using the Wilcoxon test showed p = 0.000 (<0.05). The conclusions of this study showed that there is an effect of ginger compress on the level of pain in osteoarthritis patients at health center UPT Guluk -guluk. It is advisable in future studies in order to conduct research using other variables that influence the reduction of pain in osteoarthritis patients.

Wearable e-health system, are frequently used for monitoring biomedical signals. These devices need to have advanced and applicable methods of feature selection and classifications for real time applications. Electromyogram (EMG) signal records the movement of the human muscle. EMG signal processing techniques aim to achieve the actual signal and among others, detect the state of signals related to positive and negative emotional expression. In our study, the data collected is from the facial muscle activity that is produced by the emotion of the facial expressions. The key challenge is in finding an accurate classification method of the measured signals. This paper investigates the promising techniques for the detection and classification of EMG signal using machine-learning theory. Here, we demonstrated Support Vector Machine (SVM) is an optimal method for classification of facial surface Electromyogram (sEMG) signal associated to pain dataset. The test results and the methods are able to analyze the patterns recognition of facial EMG signal classification. The result and the findings 99% accuracy with SVM method adds value on the classification algorithms of our EMG signal acquisitions platform.

FRI0532 Relationship between patient-reported outcomes and proprioceptive acuity in patients with total knee arthroplasty

Introduction: In the majority of cases, anterior cruciate ligament (ACL) injuries require specialist surgical and physiotherapeutic treatment. The fact that the patient regains a full range of flexion and extension in the knee joint as well as the reduction or elimination of pain is a significant determinant of successful physiotherapy. Material and methods: The study included 72 randomly selected individuals with a complete ACL tear who were qualified for its surgical reconstruction. The research group included 37 patients aged 18-60 (mean age 37±10.3 years). All the participants from this group underwent physiotherapy based on recognised schemes of rehabilitation and performed for 4 weeks before the reconstruction surgery. The control group included 35 patients aged 18-60 (mean age 34±10.0 years) who did not undergo any presurgical physiotherapy programme. Results: Both in the research group and in the control group, subsequent measurements revealed a statistically significant improvement in the range of flexion and extension (p&lt;0.05). Statistical analysis revealed a significantly better extension 1 week and 6 weeks after the ACL reconstruction (p&lt;0.05) in the research group. Statistically significant differences in the level of pain in patients from both groups were noted in subsequent measurements (p&lt;0.05). Also, a statistically significant difference concerning pain assessment 12 weeks after the reconstruction was noted in the research group (p&lt;0.05). Conclusions: A quicker recovery of the range of extension in the operated knee joint and lower intensity of pain in the final measurement noted in the research group may indicate a certain therapeutic value of presurgical physiotherapy.

ObjectiveTo determine the effect of live environmental music therapy (EMT), prerecorded music (same music as EMT), and a control condition on state anxiety, stress, pain, and well-being levels in patients and caregivers in the emergency department (ED) waiting areas of 2 general hospitals in Colombia, South America.MethodsThis was a pragmatic, multicenter randomized clinical trial with 3 arms: standard care + EMT, standard care + prerecorded music, and standard care only. Participants included 256 adult patients and 174 caregivers. Primary outcome was the six-item State-Trait Anxiety Inventory. Secondary outcomes were stress and pain levels, assessed with Visual Analog Scales (Visual Analog Stress Scale and Visual Analog Pain Scale), and well-being, assessed with the Well-Being Numerical Rating Scales.ResultsResults indicated that both music therapy interventions were associated with statistically significant reductions in anxiety, stress, and pain levels in patients and caregivers. Improvements in psychologic well-being were observed primarily in the group receiving live music therapy. Overall, live EMT showed better improvements across the emotional and physical outcomes.ConclusionThe results of this study indicate that live EMT and prerecorded music implemented by trained music therapists can be effective strategies for improving mental health and reducing pain in patients and caregivers in the ED waiting area.

Stiffness has often been considered as a regulated property of the neuromuscular system. The purpose of this study was to examine the ankle and knee joint stiffness regulation during sprint running. Ten male sprinters ran at the constant relative speeds of 70, 80, 90, and 100% over a force platform, and ground reaction forces, kinematic, and EMG parameters were collected. The results indicated that with increasing running speed the average joint stiffness (change in joint moment divided by change in joint angle) was constant (7 N x m x deg(-1)) in the ankle joint and increased from 17 to 24 N x m x deg(-1) (P < 0.01) in the knee joint. The observed constant ankle joint stiffness may depend on (constant) tendon stiffness because of its dominating role in triceps surae muscle-tendon unit. Thus, we conclude that in sprint running the spring-like behavior of the leg might be adjusted by changing the stiffness of the knee joint. However, in complicated motor task, such as sprint running, ankle and knee joint stiffness might be controlled by the individual mechanical and neural properties.

Continuous Passive Motion (CPM) devices are used during the early post-rehabilitation stages of Total Knee Arthroplasty (TKA) to enhance joint flexibility and reduce stiffness, enabling the restoration of mobility and function. However, commercially available CPM devices are manually operated and are incapable of autonomous response to patient-specific pain feedback. This study presents a closed-loop CPM system that utilizes machine learning (ML) to relate electromyography (EMG) signals with the patient's pain levels, enabling dynamic adjustments to its motion parameters. For precise and robust control, we implement sliding mode control (SMC) with an exponential rate-reaching law for reliable tracking of a time-varying reference trajectory based on EMG feedback, along with Proportional Integral Derivative (PID) control for comparative analysis. The SMC controller is tested for various disturbances to assess its responsiveness and robustness. A single-class classifier, Support Vector Machine (SVM), the learning ML model, is trained using key time domain features such as Mean Absolute Value (MAV), Root Mean Square (RMS), First Derivative, and Wavelength, extracted from processed EMG signals of patients to characterize muscle activity and estimate pain intensity. By capturing the nonlinear relationship between these features and perceived pain, the SVM model can be used to adapt the control system in real time. By integrating signal-driven feedback with robust control strategies, the proposed closed-loop system aims to enhance rehabilitation outcomes through personalized responsive therapy.

Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

Characterization of Electromyogram (EMG) signals is important for identifying neuromuscular diseases. Although various techniques were implemented for classification, none of them were implemented in the complexity domain. In this study, characterization of EMG signals recorded from healthy, neuropathic and myopathic subjects has been performed in complexity domain based on Multiscale Entropy (MSE) method. To do this, the multiscale entropy method has been applied to an EMG database publicly available in PhysioNet. The complexity profile curves obtained with the MSE approach have shown promising classification among healthy subject, neuropathic and myopathic patients in terms of complexity. One way ANOVA test has shown statistically significant differences (p<0.01) among these three classes. Moreover, Support Vector Machine (SVM) has demonstrated a classification accuracy of 86.1% for characterizing EMG signals of neuromuscular disorders. Furthermore, myopathic and neuropathic patients have been recognized with 66.7% sensitivity at 95.8% specificity and 100% sensitivity at 100% specificity respectively.

Using electromyogram (EMG) signals as a human-machine interface to control the myoelectric prostheses is consummately advanced. Nevertheless, reported studies present high classification performance from the EMG signal captured in laboratory conditions. There is a very challenging suffix regarding untrained condition. Limb position change causes high variation in EMG signals resulting in high misclassification rate in the untrained position. The goal of this work is to indicate the most relevance EMG channel, which is robust to the effect of a limb position change. The EMG signals from four different object shapes with five different object placement positions were examined using linear discriminant analysis. To determine the best EMG channel subset of all possible combination of selective channels, we reduced the number of EMG channels from 12 to 3. The results have shown that the minimum classification error rate (2.09%) across five subjects can be achieved by use of eight optimal EMG channels. Additionally, our results suggest the EMG channel number 3 (Triceps brachii), 7 (Extensor digitorum communis), and 9 (Brachioradialis) as the most three emphasized channels that contain the rich information of hand grasping and generalize limb position change.

Electromyogram (EMG) signals are biopotential signals, which occur as the result of electro-chemical circumstances dependent on contraction of muscles. In this study, a new method is presented to determine the motor unit action potentials of EMG signals recorded with surface electrodes. Compared to needle EMG signals, surface EMG signals have more active noise component. Results, which are gained by the process train including wavelet de-noising method for noise anlysis at first step and then ICA algorithms to provide independent components, form four different datas to determine the motor unit action potentials. Performed study, which is aimed to make ineffective the problems that make the analysis of EMG signals difficult and then to reach the desired results, provides simultaneous comparison possibility submitting four results. After the determination of the results, with the feedback between input and output, examination of accuracy ratio of results are possible and this examination eliminates the questions about the accuracy of the study.

BackgroundLumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms.MethodsWe utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed.ResultsIn the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy.ConclusionsThe RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

The aim of this study is to analyze Electromyogram (EMG) signals in Rapid Eye Movement (REM) sleep using different techniques to detect the level of normality and abnormality of normal and abnormal (patients with a lack of REM sleep atonia) subjects and predict the development of Parkinson’s disease in abnormal subjects. Quantitative elctromyogram (EMG) signal analysis in the frequency domain using classical power spectrum analysis techniques have been well documented over the past decade. Yet none of these [sic] work have been done on EMG during Rapid Eye Movement (REM) Stage of sleep. In this work three techniques for classifying chin movement via EMG signals during sleep is presented. Three methods (Autoregressive modeling, Cepstrum Analysis and Wavelet Analysis) for extracting features from EMG signal during sleep and a classification algorithm (Linear Discriminant Analysis (LDA)) were analyzed and compared. EMG data are used to detect and describe different disease processes affecting sleep. Rapid Eye Movement Behavior Disorder (RBD) is an example of EMG abnormality in which patients lose their muscle control while in REM stage of sleep resulting in physically acting out their dreams. An adaptive segmentation based on Recursive Least Square (RLS) algorithm was analyzed. This algorithm was used to segment the non-stationary EMG signal into locally stationary components, which were then autoregressive modeled using the Burg-Lattice method. The cepstral measurements described was used and applied to modify the coefficients computed from the autoregressive (AR) model. Yet due to the nature of the EMG, frequency analysis cannot be used to approximate a signal whose properties change over time. To address this problem a time varying feature representation is necessary for analysis to extract useful infomration from the signal. As a consequence Wavelet coefficients were computed using discrete and continuous wavelet transforms. Furthermore, the classification performance of the above three feature sets were investigated for the two classes (Normal and Abnormal). Results showed wavelet analysis compared to AR modeling and cepstrum analysis is a better assessment in finding EMG abnormalities during sleep. However, these methods may be useful in distinguishing EMG patterns that predict the emergence of Parkinson disease in humans.

to the editor: The electromyogram (EMG) as an indirect measure of motor unit activity has proved invaluable in motor control studies. Increasingly, the application of frequency domain techniques to EMG signals has provided important insight into the structure and function of the human motor system.

The electromyogram (EMG) signal has been used as the command input to myoelectric prostheses. A common control scheme is based on classifying the EMG signals from multiple electrodes into one of several distinct classes of user intent/function. In this work, we investigated the use of EMG whitening as a preprocessing step to EMG pattern recognition. Whitening is known to decorrelate the EMG signal, with improved performance shown in the related applications of EMG amplitude estimation and EMG-torque processing. We reanalyzed the EMG signals recorded from 10 electrodes placed circumferentially around the forearm of 10 intact subjects and 5 amputees. The coefficient of variation of two time-domain features--mean absolute value and signal length--was significantly reduced after whitening. Pre-whitened classification models using these features, along with autoregressive power spectrum coefficients, added approximately five percentage points to their classification accuracy. Improvement was best using smaller window durations (<100 ms).