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Electromyography (EMG) based interfaces are the most common solutions for the control of robotic, orthotic, prosthetic, assistive, and rehabilitation devices, translating myoelectric activations into meaningful actions. Over the last years, a lot of emphasis has been put into the EMG based decoding of human intention, but very few studies have been carried out focusing on the continuous decoding of human motion. In this work, we present a learning scheme for the EMG based decoding of object motions in dexterous, in-hand manipulation tasks. We also study the contribution of different muscles while performing these tasks and the effect of the gender and hand size in the overall decoding accuracy. To do that, we use EMG signals derived from 16 muscle sites (8 on the hand and 8 on the forearm) from 11 different subjects and an optical motion capture system that records the object motion. The object motion decoding is formulated as a regression problem using the Random Forests methodology. Regarding feature selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 10-fold cross validation procedure is used for model assessment purposes and the feature variable importance values are calculated for each feature. This study shows that subject specific, hand specific, and object specific decoding models offer better decoding accuracy that the generic models.

The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g., Electromyography based control of a prosthetic hand). One of the biggest challenges in developing a surface Electromyography (sEMG) based interface is the selection of the right muscles for the execution of a desired task. In this work, we investigate optimal muscle selections for sEMG based decoding of dexterous in-hand manipulation motions. To do that, we use EMG signals derived from 14 muscle sites of interest (7 on the hand and 7 on the forearm) and an optical motion capture system that records the object motion. The regression problem is formulated using the Random Forests methodology that is based on decision trees. Regarding features selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 5-fold cross validation procedure is used for model assessment purposes and the importance values are calculated for each feature. This pilot study shows that the muscles of the hand contribute more than the muscles of the forearm to the execution of inhand manipulation tasks and that the myoelectric activations of the hand muscles provide better estimation accuracies for the decoding of manipulation motions. These outcomes suggest that the loss of the hand muscles in certain amputations limits the amputees' ability to perform a dexterous, EMG based control of a prosthesis in manipulation tasks. The results discussed can also be used for improving the efficiency and intuitiveness of EMG based interfaces for healthy subjects.

A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object to be grasped. The discrimination between the different reach to grasp strategies is accomplished with machine learning techniques for classification. The classification decision is then used in order to trigger an EMG-based task-specific motion decoding model. Task specific models manage to outperform "general" models providing better estimation accuracy. Thus, the proposed scheme takes advantage of a framework incorporating both a classifier and a regressor that cooperate advantageously in order to split the task space. The proposed learning scheme can be easily used to a series of EMG-based interfaces that must operate in real time, providing data-driven capabilities for multiclass problems, that occur in everyday life complex environments.

Electromyography (EMG) signals have been used in designing muscle-machine interfaces (MuMIs) for various applications, ranging from entertainment (EMG controlled games) to human assistance and human augmentation (EMG controlled prostheses and exoskeletons). For this, classical machine learning methods such as Random Forest (RF) models have been used to decode EMG signals. However, these methods depend on several stages of signal pre-processing and extraction of hand-crafted features so as to obtain the desired output. In this work, we propose EMG based frameworks for the decoding of object motions in the execution of dexterous, in-hand manipulation tasks using raw EMG signals input and two novel deep learning (DL) techniques called Temporal Multi-Channel Transformers and Vision Transformers. The results obtained are compared, in terms of accuracy and speed of decoding the motion, with RF-based models and Convolutional Neural Networks as a benchmark. The models are trained for 11 subjects in a motion-object specific and motion-object generic way, using the 10-fold cross-validation procedure. This study shows that the performance of MuMIs can be improved by employing DL-based models with raw myoelectric activations instead of developing DL or classic machine learning models with hand-crafted features. [ABSTRACT FROM AUTHOR]