Muscle fibers act as a source of force in the human body. The joint actions occur due to contraction of these muscle fibers controlled by central nervous system (CNS). Surface electromyography (EMG) signals produced during any muscular activity can play a vital role in measuring the muscular force under non-fatigued and fatigued conditions. Due to nonlinear, irregular, and continuous characteristics, sEMG signals need to be preprocessed to estimate muscle force correctly. The upper limb Biceps and Triceps muscles controls the movement of the elbow joints. Any disorder in this muscle can lead to difficulty in bending the elbow, turning palms in and out, and movement of arms. This research paper proposes different deep learning-based sEMG-to-force estimation models. The proposed models estimate the force from the sEMG signals acquired from the Biceps and Triceps muscles during maximal and submaximal contractions. The proposed deep learning models are based on hybrid 1D Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) and 1D Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) approaches along with self-attention mechanism. The dataset contains raw sEMG signals acquired from the human arm’s Biceps and Triceps muscles. These signals are first preprocessed to remove DC offset and high frequency noises. The models are then trained with the pre-processed signals. Results of the proposed deep learning models are compared with the conventional machine learning algorithms including Support Vector regression (SVR) and Gaussian process regression (GPR) algorithms. Different performance metrics including Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R ² ) are utilized to measure the performance of the models based on the predicted and actual force. Results shows that the proposed CNN-LSTM and CNN-GRU models achieved better performance (MSE: 0.001, RMSE: 0.023, MAE: 0.017, and R ² : 0.987) and (MSE: 0.001, RMSE: 0.025, MAE: 0.019, and R ² : 0.985), respectively, as compared to GPR (MSE: 0.080, RMSE: 0.282, MAE: 0.162, and R ² : 0.856) and SVR (MSE: 0.008, RMSE: 0.082, MAE: 0.067, and R ² : 0.482). The proposed research study is helpful in developing a rehabilitation control system beneficial for people suffering with muscular disorder.

Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches. These approaches recognize different levels of stress based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring good quality features from these modalities using feature engineering is often a difficult job. Recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. This paper proposes different CNN and CNN-LSTSM-based fusion models using physiological signals (SRAD dataset) and multimodal data (AffectiveROAD dataset) for the driver’s two and three stress levels. The fuzzy EDAS (evaluation based on distance from average solution) approach is used to evaluate the performance of the proposed models based on different classification metrics (accuracy, recall, precision, F-score, and specificity). Fuzzy EDAS performance estimation shows that the proposed CNN and hybrid CNN-LSTM models achieved the first ranks based on the fusion of BH, E4-Left (E4-L), and E4-Right (E4-R). Results showed the significance of multimodal data for designing an accurate and trustworthy stress recognition diagnosing model for real-world driving conditions. The proposed model can also be used for the diagnosis of the stress level of a subject during other daily life activities.

Surface electromyography (sEMG) signals acquired with linear electrode array are useful in analyzing muscle anatomy and physiology. Most algorithms for signal processing, detection, and estimation require adequate quality of the input signals, however, multi-channel sEMG signals are commonly contaminated due to several noise sources. The sEMG signal needs to be enhanced prior to the digital signal and image processing to achieve the best results. This study is using spatio-temporal images to represent surface EMG signals. The motor unit action potential (MUAP) in these images looks like a linear structure, making certain angles with the x-axis, depending on the conduction velocity of the MU. A multi-scale Hessian-based filter is used to enhance the linear structure, i.e., the MUAP region, and to suppress the background noise. The proposed framework is compared with some of the existing algorithms using synthetic, simulated, and experimental sEMG signals. Results show improved detection accuracy of the motor unit action potential after the proposed enhancement as a preprocessing step.