Weight classification during actively assisted elbow flexion and extension

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Severe muscle weakness is a symptom appearing in certain neuromuscular diseases (NMDs), such as Duchenne Muscular Dystrophy (DMD), affecting people's daily lives by reducing functionality, decreasing independence, and reducing the ability to perform essential daily activities. This patient group might benefit from using active-assistive devices by having the potential to provide precise support torque counterbalancing the passive forces acting on the arm, the movement intention of the user, and external forces exerted by lifted objects. However, the determination of support to counteract the weight of lifted objects is an ongoing challenge. This research aims to improve the understanding of external forces by using data classification algorithms to distinguish between different lifted weights in a human experiment. Fourteen healthy individuals participated in this experiment, lifting weights ranging from 0 - 1000 grams while an active-assistive device compensated for the passive torques acting on the arm. Data was collected using various sensors: a force sensor, an Inertial Measurement Unit (IMU), a joint encoder, and surface Electromyography (sEMG) electrodes. Subsequently, this data was processed and fed into a K Nearest Neighbour (KNN) classifier and a Support Vector Machine (SVM) classifier to determine the lifted weights during human elbow flexion and extension. The classifier showing the highest performance achieved an accuracy of 39.70% on the test dataset, indicating several misclassifications. However, a recall percentage of 76.95% for the 1000-gram class within the multi-class classification demonstrates the capability to distinguish larger weights. While demonstrating potential in weight discrimination, especially for larger weights, improvements in the compensation strategy, arm support alignment, and experimental design are crucial. Future research on the impact of picking and placing objects, the influence of muscle weakness, and the application of alternative data classification algorithms are essential to further enhance understanding of the interaction with objects and result in more accurate predictions.