Feasibility of building robust surface electromyography-based hand gesture interfaces

Can surgically assisted rapid palatal expansion (SARPE) be recommended over orthodontic rapid palatal expansion (ORPE) for girls above the age of 14? : A cone-beam CT study on midpalatal suture maturation

BACKGROUND

For patients with a maxillary transversal deficiency (MTD), various treatment options are available, partly based on the practitioner's experience. This study aimed to determine a cut-off age for decision making between surgically assisted rapid palatal expansion (SARPE) over orthodontic rapid palatal expansion (ORPE) based on skeletal maturation in a female population.

METHODS

A total of 100 cone beam computed tomography (CBCT) images of young females were analyzed on maturation of the pterygomaxillary (PMS), zygomaticomaxillary (ZMS), transpalatal (TPS), and midpalatal (MPS) sutures. Based on the maturation of these four junctions, four independent observers had to determine whether they would prefer ORPE or SARPE to widen the maxilla.

RESULTS

For the PMS, the results show a closure of 83-100% from 13 to 17 years. As for the TPS, a closure of 78-85% was observed from 15 years of age. For the 15- to 17-year-old females, a closed ZMS was present in 32-47%. Regarding MPS, closed sutures presented in 61% (stages D and E) of the 15-year-old females. The cut-off age at which SARPE was recommended was 15.1 years for the orthodontist observers and 14.8 years for the maxillofacial surgeon observers.

CONCLUSIONS

Significant maturation of MPS was reached at the age of 15 in a female population. The PMS, TPS, MPS, and ZMS closed sequentially. A comprehensive diagnostic approach is necessary for choosing the appropriate treatment. When in doubt, age could assist decision making in a female population, with a cut-off age of 15 years in favor of SARPE based on this study.

Deep Cross-User Models Reduce the Training Burden in Myoelectric Control

The effort, focus, and time to collect data and train EMG pattern recognition systems is one of the largest barriers to their widespread adoption in commercial applications. In addition to multiple repetitions of motions, including exemplars of confounding factors during the training protocol has been shown to be critical for robust machine learning models. This added training burden is prohibitive for most regular use cases, so cross-user models have been proposed that could leverage inter-repetition variability supplied by other users. Existing cross-user models have not yet achieved performance levels sufficient for commercialization and require users to closely adhere to a training protocol that is impractical without expert guidance. In this work, we extend a previously reported adaptive domain adversarial neural network (ADANN) to a cross-subject framework that requires very little training data from the end-user. We compare its performance to single-repetition within-user training and the previous state-of-the-art cross-subject technique, canonical correlation analysis (CCA). ADANN significantly outperformed CCA for both intact-limb (86.8–96.2%) and amputee (64.1–84.2%) populations. Moreover, the ADANN adaptation computation time was substantially lower than the time otherwise devoted to conducting a full within-subject training protocol. This study shows that cross-user models, enabled by deep-learned adaptations, may be a viable option for improved generalized pattern recognition-based myoelectric control.

Towards identification of finger flexions using single channel surface electromyography--able bodied and amputee subjects

Background

This research has established a method for using single channel surface electromyogram (sEMG) recorded from the forearm to identify individual finger flexion. The technique uses the volume conduction properties of the tissues and uses the magnitude and density of the singularities in the signal as a measure of strength of the muscle activity.

Methods

SEMG was recorded from the flexor digitorum superficialis muscle during four different finger flexions. Based on the volume conduction properties of the tissues, sEMG was decomposed into wavelet maxima and grouped into four groups based on their magnitude. The mean magnitude and the density of each group were the inputs to the twin support vector machines (TSVM). The algorithm was tested on 11 able-bodied and one trans-radial amputated volunteer to determine the accuracy, sensitivity and specificity. The system was also tested to determine inter-experimental variations and variations due to difference in the electrode location.

Results

Accuracy and sensitivity of identification of finger actions from single channel sEMG signal was 93% and 94% for able-bodied and 81% and 84% for trans-radial amputated respectively, and there was only a small inter-experimental variation.

Conclusions

Volume conduction properties based sEMG analysis provides a suitable basis for identifying finger flexions from single channel sEMG. The reported system requires supervised training and automatic classification.

A sign-component-based framework for Chinese sign language recognition using accelerometer and sEMG data

Identification of constituent components of each sign gesture can be beneficial to the improved performance of sign language recognition (SLR), especially for large-vocabulary SLR systems. Aiming at developing such a system using portable accelerometer (ACC) and surface electromyographic (sEMG) sensors, we propose a framework for automatic Chinese SLR at the component level. In the proposed framework, data segmentation, as an important preprocessing operation, is performed to divide a continuous sign language sentence into subword segments. Based on the features extracted from ACC and sEMG data, three basic components of sign subwords, namely the hand shape, orientation, and movement, are further modeled and the corresponding component classifiers are learned. At the decision level, a sequence of subwords can be recognized by fusing the likelihoods at the component level. The overall classification accuracy of 96.5% for a vocabulary of 120 signs and 86.7% for 200 sentences demonstrate the feasibility of interpreting sign components from ACC and sEMG data and clearly show the superior recognition performance of the proposed method when compared with the previous SLR method at the subword level. The proposed method seems promising for implementing large-vocabulary portable SLR systems.