FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography.

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through the processing of surface electromyogram (sEMG) signals. Objective: Although DNNs have shown superior accuracy compared to conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. The main objective of this work is to design a modern DNN-based gesture detection model that relies on minimal training data while providing high accuracy. Methods: We propose the novel Few-Shot learning- Hand Gesture Recognition (FS-HGR) architecture. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training observations. The proposed FS-HGR generalizes after seeing very few observations from each class by combining temporal convolutions with attention mechanisms. This allows the meta-learner to aggregate contextual information from experience and to pinpoint specific pieces of information within its available set of inputs. Data Source & Summary of Results: The performance of FS-HGR was tested on the second and fifth Ninapro databases, referred to as the DB2 and DB5, respectively. The DB2 consists of 50 gestures (rest included) from 40 healthy subjects. The Ninapro DB5 contains data from 10 healthy participants performing a total of 53 different gestures (rest included). The proposed approach for the Ninapro DB2 led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot). Moreover, the proposed approach for the Ninapro DB5 led to 64.65% classification accuracy on new subjects with few-shot observation (5-way 5-shot).

Deep Learning for Robust Decomposition of High-Density Surface EMG Signals.

Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trains. Once the separation matrix is blindly estimated on a signal interval, it is also possible to apply the same matrix to subsequent signal segments. Nonetheless, the trained separation matrices are sub-optimal in noisy conditions and require that incoming data undergo computationally expensive whitening. One unexplored alternative is to instead use the paired HD-sEMG signal and BSS output to train a model to predict MU activations within a supervised learning framework. A gated recurrent unit (GRU) network was trained to decompose both simulated and experimental unwhitened HD-sEMG signal using the output of the gCKC algorithm. The results on the experimental data were validated by comparison with the decomposition of concurrently recorded intramuscular EMG signals. The GRU network outperformed gCKC at low signal-to-noise ratios, proving superior performance in generalising to new data. Using 12 seconds of experimental data per recording, the GRU performed similarly to gCKC, at rates of agreement of 92.5% (84.5%-97.5%) and 94.9% (88.8%-100.0%) respectively for GRU and gCKC against matched intramuscular sources.

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models.

The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.

An Online Spectral Information-Enhanced Approach for Artifact Detection and Fault Attentuation in Myoelectric Control.

In myocontrol of neuroprosthetic devices, multichannel electromyography (EMG) can be used to decode the intended motor command, based on distributed activation patterns of stump muscles. In this regard, the high density EMG (HD-EMG) approach allows for enhancement of the spatiotemporal resolution for motor intention detection. Despite the advantages of relying on several EMG channels, the challenge of high-density electrode systems is the dynamically changing electrode-skin contact impedance, which can affect a considerable number of electrodes over the time of data acquisition. This can result in obtaining unreliable, low-quality EMG recording with a distributed artifact pattern over the grid of EMG sensors. To address this issue, we propose a novel online approach for adaptive information extraction and enhancement for automatic artifact detection and attenuation in HD-EMG-based myocontrol of prosthetic devices. The method is based on an adaptive weighting scheme that modifies the contribution of each HD-EMG channel considering the spectral information content relative to artifacts. The technique (named IE-HD-EMG) was tested as an online pre-conditioning step for a challenging multiclass classification problem of 4-finger activation, using linear discriminant analysis. It is shown that for this application, the proposed IE-HD-EMG technique led to a superior performance in finger activation recognition (79.25% accuracy, 89% sensitivity, 89.15% specificity) in comparison to the conventional HD-EMG recording under the same condition without the proposed approach (56.25% accuracy, 61.3% sensitivity, 67% specificity). Therefore, the proposed technique can have a significant potential to expand the clinical viability of HD-EMG systems.

Visual Displacement Perception in Parkinson's Disease Analyzed Using a Computer-Generated Graphical Tool.

Parkinson's Disease (PD) is typically classified by the onset of motor impairments, however, non-motor symptoms are also present in all disease stages. Vision abnormalities contribute to the non-motor PD deficits, yet little research has studied how PD affects visual perceptions with no produced motor responses. This provides motivation for the current study which focuses on examining allocentric visual displacement perception - information used for object identification - in PD patients. To study this PD participants OFF and ON Levodopa therapy, and age-matched healthy control participants were tested. A modular graphics toolbox was implemented to carry out the perceptual testing. Individuals with PD were shown to have impairments in displacement perception of the larger tested magnitudes when both OFF and ON Levodopa compared to control participants, suggesting impairments in visual displacement processing pathways. These abnormalities could contribute to difficulties some PD patients have with visual recognition and visuospatial navigation. Furthermore, the study validated the graphical tool as a means of quantifying perceptual abilities that can be expanded to many perceptual modalities and paired with robotic devices.

Haptic Feedback Manipulation During Botulinum Toxin Injection Therapy for Focal Hand Dystonia Patients: A Possible New Assistive Strategy.

Abnormality of sensorimotor integration in the basal ganglia and cortex has been reported in the literature for patients with task-specific focal hand dystonia (FHD). In this study, we investigate the effect of manipulation of kinesthetic input in people living with writer's cramp disorder (a major form of FHD). For this purpose, severity of dystonia is studied for 11 participants while the symptoms of seven participants have been tracked during five sessions of assessment and Botulinum toxin injection (BoNT-A) therapy (one of the current suggested therapies for dystonia). BoNT-A therapy is delivered in the first and the third session. The goal is to analyze the effect of haptic manipulation as a potential assistive technique during BoNT-A therapy. The trial includes writing, hovering, and spiral/sinusoidal drawing subtasks. In each session, the subtasks are repeated twice when (a) a participant uses a normal pen, and (b) when the participant uses a robotics-assisted system (supporting the pen) which provides a compliant virtual writing surface and manipulates the kinesthetic sensory input. The results show (p-value using one-sample t-tests) that reducing the writing surface rigidity significantly decreases the severity of dystonia and results in better control of grip pressure (an indicator of dystonic cramping). It is also shown that (p-value based on paired-samples t-test) using the proposed haptic manipulation strategy, it is possible to augment the effectiveness of BoNT-A therapy. The outcome of this study is then used in the design of an actuated pen as a writing-assistance tool that can provide compliant haptic interaction during writing for FHD patients.