Abstract
Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.
Highlights
Hand gesture recognition (HGR) systems are human–machine interfaces that are responsible for determining which gesture was performed and when it was performed [1]
The accuracy value, which is considered our main metric of evaluation, is useful to analyze the proportion of correct predictions over a set of measures, as can be observed in true positives (TP) + true negatives (TN) + FP + FN
The accuracy results for recognition are 94.2% and 80.3% for the user-specific and user-general models, respectively
Summary
Hand gesture recognition (HGR) systems are human–machine interfaces that are responsible for determining which gesture was performed and when it was performed [1]. Several applications of HGRs have been proven useful These models have been applied in sign language recognition (English, Arabic, Italian) [3,4,5], in prosthesis control [6,7,8,9], in robotics [10,11], in biometric technology [12], and in gesture recognition of activities of daily living [13], among others. There are many fields of application, HGR models have not reached their full potential, nor have they been widely adopted. This is caused mainly by three factors. This is partly because they are not easy or intuitive to use (i.e., an HGR implementation is expected to be real-time, non-invasive, and wireless), or because they require some training or strict procedure before usage
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