Search Results (40)

Electromyography (EMG)-based pattern recognition is a key enabling technology for intuitive upper-limb prosthetic control; however, amputee EMG signals show substantial intersubject variability and nonstationary characteristics, which can limit the effectiveness of conventional fixed feature representations. This study proposes an Adaptive Multi-Representation Fusion Framework that combines an enhanced Adaptive Linear Series Decomposition Learner (Adaptive LSDL) with Time-Domain Power Spectral Descriptors (TDPSD) for amputee movement classification. The proposed Adaptive LSDL extends conventional decomposition by using adaptive threshold-region optimization and multitransform signal analysis, enabling subject-specific identification of the most discriminative decomposition characteristics while preserving low computational complexity. The complementary properties of TDPSD and Adaptive LSDL are then exploited through feature-level fusion, producing a unified representation that captures both local temporal-spectral complexity and adaptive structural signal information. Experimental evaluation was conducted using the Ninapro DB3 dataset, comprising 11 transradial amputees, and a MYO armband amputee dataset, comprising 6 transradial amputees. Subject-specific five-fold cross-validation was performed using a Linear Discriminant Analysis (LDA) classifier. Adaptive LSDL consistently improved performance relative to the original fixed-threshold LSDL representation, while fusion improved classification accuracy for 7 of 11 DB3 amputees and 5 of 6 MYO amputees. The largest fusion gains reached 4.39 and 5.60 percentage points for the DB3 and MYO cohorts, respectively. Furthermore, the proposed framework demonstrated competitive performance relative to a lightweight real-time convolutional neural network benchmark while requiring substantially lower computational complexity. These findings indicate that adaptive decomposition and representation-level information fusion provide an effective approach for exploiting subject-specific neuromuscular information while remaining suitable for computationally efficient prosthetic control applications. Full article

Surface electromyography (sEMG) is increasingly used for gesture recognition in prosthetics, rehabilitation, and human–computer interaction. Existing architectures typically force heterogeneous sEMG features into a shared latent representation, limiting their ability to capture complementary temporal, frequency-domain, and inter-electrode spatial dependencies. To better exploit these features, this paper proposes a three-branch fusion network. Unlike many existing multi-branch methods, the proposed network explicitly models the ring arrangement of armband electrodes, capturing the adjacency information in the sensor topology that linear channel representations ignore. The temporal and spectral branches use a compact multi-scale residual structure, so this topology branch is added while maintaining modest model complexity. A reliability-aware routing mechanism then adaptively assigns fusion weights to the three branches for each sample. On NinaPro DB5 Exercise B (eight-channel lower armband), the method reaches 83.99% under subject-dependent training and 85.66% under transfer learning, exceeding prior transfer learning approaches under matched conditions. Ablation experiments confirm that the three branches contribute non-redundant information and that adaptive fusion outperforms fixed combinations. The architecture also generalizes to MyoArmbandDataset under a subject-adaptive transfer learning protocol without dataset-specific hyperparameter retuning, indicating potential for wearable gesture interfaces, rehabilitation, and prosthetic control. Full article

In collaborative robotics, efficiency and user experience play a central role. This study looks at how perceived performance differs from measured performance when comparing two ways of controlling industrial robots: traditional teaching pendants and wearable EMG-based gesture control. A Myo Armband was used as an accessible 8-channel EMG platform, and three experiments were carried out on a Universal Robots UR10e to test pick-and-place tasks and precision positioning. Time and accuracy data were gathered together with blind feedback from 13 participants through a multi-criteria analysis framework. Even though the teaching pendant turned out to be more accurate in every scenario, 85% of participants still rated gesture control higher in overall satisfaction. These results point to a notable gap between what users perceive and how they actually perform and suggest that user experience deserves more weight in the design of future robot control interfaces. Full article

This study explores the use of machine learning models to assess the severity of Parkinson’s disease symptoms based on data from wearable and smartphone sensors. It presents models to predict the severities of individual symptoms—tremor, bradykinesia, stiffness, and dyskinesia—as well as the overall state of patients, using both clinician and patient self-assessments as labels. The dataset, although limited and imbalanced, enabled the identification of key trends. The best performance was achieved when combining data from both the MYO armband and smartphone, and when using patient self-assessments as targets. Tremor was the most predictable symptom, while others proved more challenging—especially at higher severity levels, which were poorly represented in the dataset. These results highlight the value of multimodal data and the importance of patient input in symptom monitoring. However, they also point to the need for more balanced and extensive datasets to improve prediction accuracy across all severity levels and symptoms. Full article

Hand gesture recognition is a prominent topic in the recent literature, with surface ElectroMyoGraphy (sEMG) recognized as a key method for wearable Human–Machine Interfaces (HMIs). However, sensor placement still significantly impacts systems performance. This study addresses sensor displacement by introducing a fast and low-impact orientation correction algorithm for sEMG-based HMI armbands. The algorithm includes a calibration phase to estimate armband orientation and real-time data correction, requiring only two distinct hand gestures in terms of sEMG activation. This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. The algorithm was implemented in a hand gesture HMI system featuring a custom seven-channel sEMG armband with an Artificial Neural Network (ANN) capable of recognizing nine gestures. Validation demonstrated its effectiveness, achieving $93.36$% average prediction accuracy with arbitrary armband wearing orientation. The algorithm also has minimal impact on power consumption and latency, requiring just an additional 500 μW and introducing a latency increase of 408 μs. These results highlight the algorithm’s efficacy, general applicability, and efficiency, presenting it as a promising solution to the electrode-shift issue in sEMG-based HMI applications. Full article

Every year, thousands of people undergo amputations due to trauma or medical conditions. The loss of an upper limb, in particular, has profound physical and psychological consequences for patients. One potential solution is the use of externally powered prostheses equipped with motorized artificial hands. However, these commercially available prosthetic hands are prohibitively expensive for most users. In recent years, advancements in 3D printing and sensor technologies have enabled the design and production of low-cost, externally powered prostheses. This paper presents a pattern-recognition-based human–prosthesis interface that utilizes surface electromyography (sEMG) signals, captured by an affordable device, the Myo armband. A Support Vector Machine (SVM) algorithm, optimized using Bayesian techniques, is trained to classify the user’s intended grasp from among nine common grasping postures essential for daily life activities and functional prosthetic performance. The proposal is viable for real-time implementations on low-cost platforms with 85% accuracy in grasping posture recognition. Full article

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human–computer interaction technologies. Full article

This study explores the development and pilot testing of an exergame designed for the rehabilitation of individuals with upper limb deficits. While traditional physiotherapy is effective, it often fails to fully engage patients due to its repetitive nature. This research integrates a novel exergame into physiotherapy regimens, aiming to enhance patient motivation through a gaming experience that complements conventional sessions. The exergame is structured around a narrative-driven adventure, with exercises embedded in gameplay that mirror adjustable physiotherapy routines. It utilizes the Myo armband, a wearable electromyography device, to capture muscle activity and movement. The system, part of a web-based platform, is easily deployable in various settings, including home environments. Comprehensive evaluations with health professionals and neurological patients indicate that the exergame significantly improves patient engagement. This study not only demonstrates the potential of exergames in enhancing traditional therapy but also underscores the importance of patient-centered therapeutic tools. Full article

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human–robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots. Full article

The rapidly evolving field of Virtual Reality (VR)-based Human–Computer Interaction (HCI) presents a significant demand for robust and accurate hand tracking solutions. Current technologies, predominantly based on single-sensing modalities, fall short in providing comprehensive information capture due to susceptibility to occlusions and environmental factors. In this paper, we introduce a novel sensor fusion approach combined with a Long Short-Term Memory (LSTM)-based algorithm for enhanced hand tracking in VR-based HCI. Our system employs six Leap Motion controllers, two RealSense depth cameras, and two Myo armbands to yield a multi-modal data capture. This rich data set is then processed using LSTM, ensuring the accurate real-time tracking of complex hand movements. The proposed system provides a powerful tool for intuitive and immersive interactions in VR environments. Full article

In recent years, the number of people with disabilities has increased hugely, especially in low- and middle-income countries. At the same time, robotics has made significant advances in the medical field, and many research groups have begun to develop low-cost wearable solutions. The Mechatronics and Dynamic Modelling Lab of the Department of Industrial Engineering at the University of Florence has recently developed a new version of a wearable hand exoskeleton for assistive purposes. In this paper, we will present a new regression method to predict the finger angle position of the first joint from the value of the sEMG of the forearm and the previous position of the finger itself. To acquire the dataset necessary to train the regressor a specific graphical user interface was developed which was able to acquire sEMG data from a Myo armband and the finger position from a Leap Motion Controller. Two long short-term memory (LSTM) models were compared, one in its standard configuration and the other with a convolutional layer, yielding significantly better performance for the second one, with an increase in $R^2$ coefficient from an average value of $0.746$ to $0.825$, leading to the conclusion that a convolutional layer could increase performance when few sensors are available. Full article

Surgical Instrument Signaling (SIS) is compounded by specific hand gestures used by the communication between the surgeon and surgical instrumentator. With SIS, the surgeon executes signals representing determined instruments in order to avoid error and communication failures. This work presented the feasibility of an SIS gesture recognition system using surface electromyographic (sEMG) signals acquired from the Myo armband, aiming to build a processing routine that aids telesurgery or robotic surgery applications. Unlike other works that use up to 10 gestures to represent and classify SIS gestures, a database with 14 selected gestures for SIS was recorded from 10 volunteers, with 30 repetitions per user. Segmentation, feature extraction, feature selection, and classification were performed, and several parameters were evaluated. These steps were performed by taking into account a wearable application, for which the complexity of pattern recognition algorithms is crucial. The system was tested offline and verified as to its contribution for all databases and each volunteer individually. An automatic segmentation algorithm was applied to identify the muscle activation; thus, 13 feature sets and 6 classifiers were tested. Moreover, 2 ensemble techniques aided in separating the sEMG signals into the 14 SIS gestures. Accuracy of 76% was obtained for the Support Vector Machine classifier for all databases and 88% for analyzing the volunteers individually. The system was demonstrated to be suitable for SIS gesture recognition using sEMG signals for wearable applications. Full article

sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method. Full article

Hand gesture recognition systems (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) have been studied for different applications in recent years. Most commonly, cutting-edge HGR methods are based on supervised machine learning methods. However, the potential benefits of reinforcement learning (RL) techniques have shown that these techniques could be a viable option for classifying EMGs. Methods based on RL have several advantages such as promising classification performance and online learning from experience. In this work, we developed an HGR system made up of the following stages: pre-processing, feature extraction, classification, and post-processing. For the classification stage, we built an RL-based agent capable of learning to classify and recognize eleven hand gestures—five static and six dynamic—using a deep Q-network (DQN) algorithm based on EMG and IMU information. The proposed system uses a feed-forward artificial neural network (ANN) for the representation of the agent policy. We carried out the same experiments with two different types of sensors to compare their performance, which are the Myo armband sensor and the G-force sensor. We performed experiments using training, validation, and test set distributions, and the results were evaluated for user-specific HGR models. The final accuracy results demonstrated that the best model was able to reach up to $97.50\%\pm 1.13\%$ and $88.15\%\pm 2.84\%$ for the classification and recognition, respectively, with regard to static gestures, and $98.95\%\pm 0.62\%$ and $90.47\%\pm 4.57\%$ for the classification and recognition, respectively, with regard to dynamic gestures with the Myo armband sensor. The results obtained in this work demonstrated that RL methods such as the DQN are capable of learning a policy from online experience to classify and recognize static and dynamic gestures using EMG and IMU signals. Full article

Virtual reality (VR) and augmented reality (AR) are engaging interfaces that can be of benefit for rehabilitation therapy. However, they are still not widely used, and the use of surface electromyography (sEMG) signals is not established for them. Our goal is to explore whether there is a standardized protocol towards therapeutic applications since there are not many methodological reviews that focus on sEMG control/feedback. A systematic literature review using the PRISMA (preferred reporting items for systematic reviews and meta-analyses) methodology is conducted. A Boolean search in databases was performed applying inclusion/exclusion criteria; articles older than 5 years and repeated were excluded. A total of 393 articles were selected for screening, of which 66.15% were excluded, 131 records were eligible, 69.46% use neither VR/AR interfaces nor sEMG control; 40 articles remained. Categories are, application: neurological motor rehabilitation (70%), prosthesis training (30%); processing algorithm: artificial intelligence (40%), direct control (20%); hardware: Myo Armband (22.5%), Delsys (10%), proprietary (17.5%); VR/AR interface: training scene model (25%), videogame (47.5%), first-person (20%). Finally, applications are focused on motor neurorehabilitation after stroke/amputation; however, there is no consensus regarding signal processing or classification criteria. Future work should deal with proposing guidelines to standardize these technologies for their adoption in clinical practice. Full article