Artificial neural network EMG classifier for functional hand grasp movements prediction.

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-freedom hand grasp movements (i.e. pinching, grasp an object, grasping) were predicted by means of surface EMG signals, recorded from 10 bipolar EMG electrodes arranged in a circular configuration around the forearm 2-3 cm from the elbow. Two cascaded artificial neural networks were then exploited to detect the patient's motion intention from the EMG signal window starting from the electrical activity onset to movement onset (i.e. electromechanical delay). Results The proposed approach was tested on eight healthy control subjects (4 females; age range 25-26 years) and it demonstrated a mean ± SD testing performance of 76% ± 14% for correctly predicting healthy users' motion intention. Two post-stroke patients tested the controller and obtained 79% and 100% of correctly classified movements under testing conditions. Conclusion A task-selection controller was developed to estimate the intended movement from the EMG measured during the electromechanical delay.

An affordable, adaptable, and hybrid assistive device for upper-limb neurorehabilitation.

The paper presents a multisensory and multimodal device for neuromuscular rehabilitation of the upper limb, designed to enable enriched rehabilitation treatment in both clinical and home environments. Originating from an existing low-cost, variable-stiffness rehabilitation device, it expands its functionalities by integrating additional modules in order to augment application scenarios and applicable clinical techniques. The newly developed system focuses on the integration of a wearable neuromuscular electrical stimulation system, a virtual rehabilitation scenario, a low-cost unobtrusive sensory system and a patient model for adapting training task parameters. It also monitors the user behavior during each single session and its evolution throughout the entire training period. The result is a modular, integrated and affordable rehabilitation device, enabling a biomechanical, neurological, and physiological-based training of patients, including innovative features currently unavailable within off-the-shelf rehabilitation devices.