Comparing online wrist and forearm EMG-based control using a rhythm game-inspired evaluation environment.

Objective.The use of electromyogram (EMG) signals recorded from the wrist is emerging as a desirable input modality for human-machine interaction (HMI). Although forearm-based EMG has been used for decades in prosthetics, there has been comparatively little prior work evaluating the performance of wrist-based control, especially in online, user-in-the-loop studies. Furthermore, despite different motivating use cases for wrist-based control, research has mostly adopted legacy prosthesis control evaluation frameworks.Approach.Gaining inspiration from rhythm games and the Schmidt's law speed-accuracy tradeoff, this work proposes a new temporally constrained evaluation environment with a linearly increasing difficulty to compare the online usability of wrist and forearm EMG. Compared to the more commonly used Fitts' Law-style testing, the proposed environment may offer different insights for emerging use cases of EMG as it decouples the machine learning algorithm's performance from proportional control, is easily generalizable to different gesture sets, and enables the extraction of a wide set of usability metrics that describe a users ability to successfully accomplish a task at a certain time with different levels of induced stress.Main results.The results suggest that wrist EMG-based control is comparable to that of forearm EMG when using traditional prosthesis control gestures and can even be better when using fine finger gestures. Additionally, the results suggest that as the difficulty of the environment increased, the online metrics and their correlation to the offline metrics decreased, highlighting the importance of evaluating myoelectric control in real-time evaluations over a range of difficulties.Significance.This work provides valuable insights into the future design and evaluation of myoelectric control systems for emerging HMI applications.

Understanding the influence of confounding factors in myoelectric control for discrete gesture recognition.

Discrete myoelectric control-based gesture recognition has recently gained interest as a possible input modality for many emerging ubiquitous computing applications. Unlike the continuous control commonly employed in powered prostheses, discrete systems seek to recognize the dynamic sequences associated with gestures to generate event-based inputs. More akin to those used in general-purpose human-computer interaction, these could include, for example, a flick of the wrist to dismiss a phone call or a double tap of the index finger and thumb to silence an alarm. Moelectric control systems have been shown to achieve near-perfect classification accuracy, but in highly constrained offline settings. Real-world, online systems are subject to 'confounding factors' (i.e. factors that hinder the real-world robustness of myoelectric control that are not accounted for during typical offline analyses), which inevitably degrade system performance, limiting their practical use. Although these factors have been widely studied in continuous prosthesis control, there has been little exploration of their impacts on discrete myoelectric control systems for emerging applications and use cases. Correspondingly, this work examines, for the first time, three confounding factors and their effect on the robustness of discrete myoelectric control: (1)limb position variability, (2)cross-day use, and a newly identified confound faced by discrete systems (3)gesture elicitation speed. Results from four different discrete myoelectric control architectures: (1) Majority Vote LDA, (2) Dynamic Time Warping, (3) an LSTM network trained with Cross Entropy, and (4) an LSTM network trained with Contrastive Learning, show that classification accuracy is significantly degraded (p<0.05) as a result of each of these confounds. This work establishes that confounding factors are a critical barrier that must be addressed to enable the real-world adoption of discrete myoelectric control for robust and reliable gesture recognition.

Context-informed incremental learning improves both the performance and resilience of myoelectric control.

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control.

Plant necrotrophic bacterial disease resistance phenotypes, QTL, and metabolites identified through integrated genetic mapping and metabolomics in Solanum species.

Most food crops are susceptible to necrotrophic bacteria that cause rotting and wilting diseases in fleshy organs and foods. All varieties of cultivated potato (Solanum tuberosum L.) are susceptible to diseases caused by Pectobacterium species, but resistance has been demonstrated in wild potato relatives including S. chacoense. Previous studies demonstrated that resistance is in part mediated by antivirulence activity of phytochemicals in stems and tubers. Little is known about the genetic basis of antivirulence traits, and the potential for inheritance and introgression into cultivated potato is unclear. Here, the metabolites and genetic loci associated with antivirulence traits in S. chacoense were elucidated by screening a sequenced S. tuberosum x S. chacoense recombinant inbred line (RIL) population for antivirulence traits of its metabolite extracts. Metabolite extracts from the RILs exhibited a quantitative distribution for two antivirulence traits that were positively correlated: quorum sensing inhibition and exo-protease inhibition, with some evidence of transgressive segregation, supporting the role of multiple loci and metabolites regulating these resistance-associated systems. Metabolomics was performed on the highly resistant and susceptible RILs that revealed 30 metabolites associated with resistance, including several alkaloids and terpenes. Specifically, several prenylated metabolites were more abundant in resistant RILs. We constructed a high-density linkage map with 795 SNPs mapped to 12 linkage groups, spanning a length of 1,507 cM and a density of 1 marker per 1.89 cM. Genetic mapping of the antivirulence and metabolite data identified five quantitative trait loci (QTLs) related to quorum sensing inhibition that explained 8-28% of the phenotypic variation and two QTLs for protease activity inhibition that explained 14-19% of the phenotypic variation. Several candidate genes including alkaloid, and secondary metabolite biosynthesis that are related to disease resistance were identified within these QTLs. Taken together, these data support that quorum sensing inhibition and exo-protease inhibition assays may serve as breeding targets to improve resistance to nectrotrophic bacterial pathogens in potato and other plants. The identified candidate genes and metabolites can be utilized in marker assisted selection and genomic selection to improve soft- rot and blackleg disease resistance.