Gesture Classification of Surface Electromyography Signals Using Machine Learning Algorithms for Hand Prosthetics

The actions of humans executed by their hands play a remarkable part in controlling and handling variety of objects in their daily life activities. The effect of losing or degradation in the functioning of one hand has a greater influence in bringing down the regular activity. Hence the design of prosthetic hands which assists the individuals to enhance their regular activity seems a better remedy in this new era. This paper puts forward a classification framework using machine learning algorithms for classifying hand gesture signals. The surface electromyography (sEMG) dataset acquired for 9 wrist movements of publicly available database are utilized to identify the potential biomarkers for classification and in evaluating the efficacy of the proposed algorithm. The statistical and time domain features of the sEMG signals from 27 intact subjects and 11 trans-radial amputated subjects are extracted and the optimal features are determined implementing the feature selection approach based on correlation factor. The classifiers performance of machine learning algorithms namely support vector machine (SVM), Naïve bayes (NB) and Ensemble classifier are evaluated. The experimental results highlight that the SVM classifier can yield the maximum accuracy movement classification of 99.6% for intact and 97.56% for trans-amputee subjects. The proposed approach offers better accuracy and sensitivity compared to other approaches that have used the sEMG dataset for movement classification.

A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.

Dimensionality Reduction of Human Gait for Prosthetic Control

We seek to use dimensionality reduction to simplify the difficult task of controlling a lower limb prosthesis. Though many techniques for dimensionality reduction have been described, it is not clear which is the most appropriate for human gait data. In this study, we first compare how Principal Component Analysis (PCA) and an autoencoder on poses (Pose-AE) transform human kinematics data during flat ground and stair walking. Second, we compare the performance of PCA, Pose-AE and a new autoencoder trained on full human movement trajectories (Move-AE) in order to capture the time varying properties of gait. We compare these methods for both movement classification and identifying the individual. These are key capabilities for identifying useful data representations for prosthetic control. We first find that Pose-AE outperforms PCA on dimensionality reduction by achieving a higher Variance Accounted For (VAF) across flat ground walking data, stairs data, and undirected natural movements. We then find in our second task that Move-AE significantly outperforms both PCA and Pose-AE on movement classification and individual identification tasks. This suggests the autoencoder is more suitable than PCA for dimensionality reduction of human gait, and can be used to encode useful representations of entire movements to facilitate prosthetic control tasks.

Wearable Multimodal - Serious Game System For Hand and Cognitive Rehabilitation After Stroke

Objective: Stroke often leads to both motor control and cognitive dysfunction, and effective rehabilitation requires keeping patients engaged and motivated. We introduce a wearable multimodal system based on force myography, electromyography, and inertial sensing with two associated serious games for stroke rehabilitation of twelve hand movements related to activities of daily living and the Fugl Meyer Assessment.Methods: In the ‘Find the Sheep’ serious game, patients performed corresponding hand movements to select the correct sheep card, and in the ‘Best Salesman’ serious game, patients performed corresponding hand movements to grab specific food and drink items in a store. A multi-sensor fusion model was developed for movement classification via linear discriminant analysis. Ten stroke patients with mild to moderate motor impairments (Brunnstrom Stage for Hand II-VI) performed validation testing, and effectiveness was evaluated by movement classification accuracy and qualitative patient questionnaires.Results: Classification accuracy for twelve movements using combined force myography, electromyography, and inertial sensing was 81.0%, and accuracies for using electromyography, force myography, or inertial sensing alone were 69.6%, 63.2%, and 47.8%, respectively. All patients reported that they were more enthusiastic about rehabilitation while playing serious games than conventional rehabilitation, and a majority reported the wearable multimodal-based system was easier to wear than a sensorized data glove. Significance: Results showed that multi-sensor fusion could improve hand gesture classification accuracy for stroke patients and demonstrated that the proposed wearable multimodal-serious game system could potentially facilitate upper extremity rehabilitation and cognitive training after stroke.