The human motor system is a complex neuro-musculo sensory system that needs further investigations of neuro-muscular commands and sensory-motor coupling to decode movement execution. Some researchers suggest that the central nervous system (CNS) activates a small set of modules termed muscle synergies to simplify motor control. Further, these modules form functional building blocks of movement as they can explain the neurophysiological characteristics of movements. We can identify and extract these muscle synergies from electromyographic signals (EMG) recorded in the laboratory by using linear decomposition algorithms, such as principal component analysis (PCA) and non-Negative Matrix Factorization Algorithm (NNMF). For the past three decades, the hypothesis of muscle synergies has received considerable attention as we attempt to understand and apply the concept of muscle synergies in clinical settings and rehabilitation. In this article, we first explore the concept of muscle synergies. We then present different strategies of adaptation in these synergies that the CNS employs to accomplish a movement goal.

The study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in supine positions on force plates. We segmented their data into no-movement (NM) phases, i.e., the easy phase (EP) and IBM phase (comprising several events or sub-phases of IBM). Acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five–six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggles and were labeled as classes for classification using support vector machine (SVM), naive Bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had low computational costs and high robustness of 96–99% accuracy.

Free divers are known to experience a physiological response during extreme breath holding, causing involuntary breathing movements (IBMs). To investigate these movements, a low-cost multi-core ESP32-Pico microcontroller prototype was developed to measure IBMs during a static breath hold. This novel device, called the bioSense, uses a differential measurement between two accelerometers placed on the sternum and the xiphoid process to acquire breathing-related movements. Sensor placement allowed for data acquisition that was posture- and body-shape-agnostic. Sensor placement was also designed to be as non-intrusive as possible and precisely capture breathing movements at configurable sampling rates. Measurements from the device were sent over WiFi to be accessed on a password-protected webserver and backed up to a micro-secure digital (microSD) card. This device was used in a pilot study, where it captured the various phases of breathing experienced by recreational free divers alongside a force plate measurement system for comparison.

Muscle synergies are defined as the specific co-activation of muscle groups to accomplish function level targets. The hypothesis of muscle synergies helps simplify the motor coordination problem by decreasing the numbers of central nervous system signals. The purpose of this research was to discuss how muscle coordination works across different individuals during the performance of a cyclic movement at specific force levels. We acquired Electromyography (EMG) signals from seven right upper-limb muscles of four participants as they operated the hand-cycle ergometer. Non-negative matrix factorization (NNMF) was used to extract muscle synergies from the EMG signals and compared across the participants at a specific power level. We found that five synergies were shared across participants. Also, the recruitment profile for those synergies appeared to be similar with only slight deviations. Therefore, we conclude that during a constrained rotational motion at constant speed (50 r.p.m) and constant power level (20 watts), the muscle synergies were preserved across participants. The results of this study would be useful in designing assistive devices for rehabilitation purposes.

Estimation of Elbow Joint Movement Using ANN-Based Softmax Classifier by Abdullah Y. Al-Maliki 1,* , Kamran Iqbal 1 and Gannon White 2 1 Department of Electrical and Computer Engineering, University of Arkansas at Little Rock, Little Rock, 72204, USA 2 Department of Kinesiology, Colorado Mesa University, Grand Junction, 81501, USA * Author to whom correspondence … Continue reading "Estimation of Elbow Joint Movement Using ANN-Based Softmax Classifier"

The study presents a novel scheme that recognizes and classifies different sub-phases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in a supine position on force plates. We segmented their data into the no-movement (NM) phase aka easy phase and IBM phase (comprising several events or sub-phases of IBM). The acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. The K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five-six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggle and were labeled as classes for classification using support vector machine (SVM), naive bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had a low computational cost and high robustness of 96–99% accuracy.