Feature extraction and selection for myoelectric control based on wearable EMG sensors

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly (p < 0.05) from 2% to 56% depending on the evaluated features when using the lower sampling rate, and especially for transradial amputee subjects. Importantly, for these subjects, no number of existing features can be combined to compensate for this loss in higher-frequency content. From these results, we identify two new sets of recommended EMG features (along with a novel feature, L-scale) that provide better performance for these emerging low-sampling rate systems

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier

Navigating features: A topologically informed chart of electromyographic features space

The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variability, even the same classification problem on different datasets can display variations in the respective optimal sets, casting doubts on the generalizability of relevant features. Here, we approach this problem by leveraging topological tools to create charts of features spaces. These charts highlight feature sub-groups that encode similar information (and their respective similarities) allowing for a principled and interpretable choice of features for classification and analysis. Using multiple electromyographic (EMG) datasets as a case study, we use this feature chart to identify functional groups among 58 state-of-the-art EMG features, and to show that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions. We find that these groups describe meaningful non-redundant information, succinctly recapitulating information about different regions of feature space. We then recommend representative features from each group based on maximum class separability, robustness and minimum complexity

Embodied Musical Interaction

Music is a natural partner to human-computer interaction, offering tasks and use cases for novel forms of interaction. The richness of the relationship between a performer and their instrument in expressive musical performance can provide valuable insight to human-computer interaction (HCI) researchers interested in applying these forms of deep interaction to other fields. Despite the longstanding connection between music and HCI, it is not an automatic one, and its history arguably points to as many differences as it does overlaps. Music research and HCI research both encompass broad issues, and utilize a wide range of methods. In this chapter I discuss how the concept of embodied interaction can be one way to think about music interaction. I propose how the three “paradigms” of HCI and three design accounts from the interaction design literature can serve as a lens through which to consider types of music HCI. I use this conceptual framework to discuss three different musical projects—Haptic Wave, Form Follows Sound, and BioMuse

A three-experiment examination of iliotibial band strain characteristics during different conditions using musculoskeletal simulation.

PURPOSE: Iliotibial band syndrome (ITBS) is a common chronic pathology mediated via excessive Iliotibial band (ITB) strain. The purpose using a three-experiment approach is to provide insight into the differences in strain between different athletic movements, the incidence of ITBS in females, the efficacy of different prophylactic modalities for ITBS and also the kinematic parameters associated with ITB strain. METHODS: Experiment 1 examined male and female athletes performing run, 45° cut and one-legged hop movements, experiment 2 observed males and females, whilst running in five different orthotic conditions and experiment 3 examined males and females riding a cycle ergometer at 70, 80 and 90RPM whilst in prophylactic knee brace and no-brace conditions. In each experiment, kinematics were obtained using a motion capture system and ITB strain was measured using a musculoskeletal simulation approach. RESULTS: In experiment 1 ITB strain was greater in the run (male=3.87% & female=4.37%; P<0.001) and cut (male=3.12% & female=4.06%; P<0.001) movements compared to hop (male=0.87% & female=1.54%). Experiment 2 showed that females exhibited increased ITB strain (male=6.34% & female=8.91%; P<0.05) and ITB strain velocity (male=57.17%/s & female=77.41%/s; P<0.05) and also in females that ITB strain velocity was greater (P≤0.01) in lateral (80.22%/s) and no-orthotic (83.01%/s) conditions compared to medial (72.58%/s) and off the shelf orthoses (74.52%/s). The regression analyses across movements showed that ITB strain was predicted by sagittal and coronal plane mechanics at the hip (R2=0.15-0.30; P<0.05) and sagittal, coronal and transverse plane kinematics at the knee joint (R2=0.15-0.22; P<0.05). CONCLUSION: Further insight is provided into differences in ITB strain across functional athletic movements, the increased incidence of ITBS in females and the parameters linked most strongly with ITB strain during different movements is provided; whilst also highlighting the prophylactic efficacy of medial and off the shelf orthoses in female runners