(1) Background: This study presents a new method for the motion quantitative analysis of Duchenne muscular dystrophy patients (DMD) performing functional tasks in clinical conditions. (2) Methods: An experimental study was designed to define how different levels of external mass (light and heavy) influence the performance of the upper limbs of a tested DMD and reference subject (RS) during horizontal movements (level of the waist) under natural conditions (NC) and passive manipulator conditions (PMC); the kinematic data and muscle activity of four chosen superficial muscles (EMG) were recorded. (3) Results: A piecewise linear multi-regression analysis revealed high statistically significant results (R² ≥ 0.75) between the tested muscle activities and kinematic data in the tested DMD patient and RS. These results were used to define whether the neural control of the four tested muscles (processed EMG data) was correlated more with the displacement of the wrist joint or the center of mass of the tested upper limb while performing functional tests with a given external weight in a horizontal plane under NC or PMC. (4) Conclusions: The proposed approach can be used to estimate the contributions of the tested muscles to patients’ motion performance and define whether this contribution is correlated with the kinematics or dynamics of the tested arm.

Objective: The aim of this preliminary study was to recognize motion patterns by classifying time series features extracted from electromyography (EMG) data of the upper limb muscles. Methods: In this study, we tested six machine learning (ML) classification models (decision trees, support vector machines, linear discriminant, quadratic discriminant, k-nearest neighbors, and efficient logistic regression) to classify time series features segmented from processed EMG data that were acquired from eight superficial muscles of two upper limbs over performing given physical activities in two main stages (supination and neutral forearm configuration) in initial and target (isometric) positions. Results: Findings indicate that in aiming to classify stages of the upper limb with the highest performance, the following ML models should be used: (1) K-NN cityblock (F1 equals 0.973/0.992) and K-NN minkowski (0.966/0.992) for the left limb in initial or target position; (2) K-NN seuclidean (0.959/0.985) and K-NN minkowski (0.957/0.986) for the right limb in initial position; (3) K-NN cityblock (0.966/0.986), K-NN seuclidean (0.959/0.985), and K-NN minkowski (0.957/0.986) for the right limb in target position. Conclusions: Upper limb positions tested in this study can be recognized based on classification of surface EMG data by using the k-nearest neighbors models (K-NN cityblock, K-NN seuclidean or K-NN minkowski) that have to be trained separately for the right and left upper limbs.

Objective: The aim of this preliminary study was to recognize motion patterns by classifying time series features obtained from electromyography (EMG) data of the upper limb muscles. Methods: In this study we tested six models Machine Learning (ML) classification models (Decision Trees, Support Vector Machines, Linear Discriminant, Quadratic Discriminant, K-Nearest Neighbors, and Efficient Logistic Regression) to classify time series features extracted from processed EMG data that were acquired from eight superficial muscles of two upper limbs over performing given physical activities in two main stages (supination and neutral forearm configuration) in initial and target (isometric) positions. Results: Findings indicate that aiming to classify stages of the upper limb with the highest performance following ML models should be used: 1) K-NN cityblock (F1 equals 0.973/0.992), K-NN seuclidean (0.971/0.996), K-NN minkowski (0.966 /0.992), K-NN co-sine (0.962/0.969) for the left limb; 2) K-NN euclidean (0.970 /0.989), K-NN cityblock (0.966 /0.986), K-NN seuclidean (0.959/0.985), K-NN minkowski (0.957/0.986) for the right limb. Conclusion: Motion patterns tested in this study can be recognized with the highest performance by applying following ML models to classify EMG data: K-NN city-block, K-NN seuclidean, and K-NN minkowski models.