Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition.

Myoelectric control, the use of electromyogram (EMG) signals generated during muscle contractions to control a system or device, is a promising input, enabling always-available control for emerging ubiquitous computing applications. However, its widespread use has historically been limited by the need for user-specific machine learning models because of behavioural and physiological differences between users. Leveraging the publicly available 612-user EMG-EPN612 dataset, this work dispels this notion, showing that true zero-shot cross-user myoelectric control is achievable without user-specific training.

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.

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.

Characteristics of the Sexual Networks of Men Who Have Sex With Men in Montréal, Toronto, and Vancouver: Insights from Canada's 2022 Mpox Outbreak.

Background: The 2022-2023 global mpox outbreak disproportionately affected gay, bisexual, and other men who have sex with men (GBM). We investigated differences in GBM's sexual partner distributions across Canada's 3 largest cities and over time, and how they shaped transmission.

Methods: The Engage Cohort Study (2017-2023) recruited GBM via respondent-driven sampling in Montréal, Toronto, and Vancouver (n = 2449).

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could play a significant role in its adoption across a range of applications, including assistive devices, robotic and rehabilitation devices. However, current studies have predominately focused on intra-subject modeling, largely neglecting the burden of end-user data acquisition. In this work, we propose the use of transfer learning (TL) to generalize force modeling to a new user by first establishing a baseline model trained using other users' data, and then adapting to the end-user using a small amount of new data (only 10% , 20% , and 40% of the new user data).

Novel Wearable HD-EMG Sensor With Shift-Robust Gesture Recognition Using Deep Learning.

In this work, we present a hardware-software solution to improve the robustness of hand gesture recognition to confounding factors in myoelectric control. The solution includes a novel, full-circumference, flexible, 64-channel high-density electromyography (HD-EMG) sensor called EMaGer. The stretchable, wearable sensor adapts to different forearm sizes while maintaining uniform electrode density around the limb.

Electrical signals in the ER are cell type and stimulus specific with extreme spatial compartmentalization in neurons.

The endoplasmic reticulum (ER) is a tortuous organelle that spans throughout a cell with a continuous membrane containing ion channels, pumps, and transporters. It is unclear if stimuli that gate ER ion channels trigger substantial membrane potential fluctuations and if those fluctuations spread beyond their site of origin. Here, we visualize ER membrane potential dynamics in HEK cells and cultured rat hippocampal neurons by targeting a genetically encoded voltage indicator specifically to the ER membrane.

Effects of Exercise Training on Neurotrophic Factors and Subsequent Neuroprotection in Persons with Multiple Sclerosis-A Systematic Review and Meta-Analysis.

Background: Evidence indicates that exercise holds the potential to counteract neurodegeneration experienced by persons with multiple sclerosis (pwMS), which is in part believed to be mediated through increases in neurotrophic factors. There is a need to summarize the existing evidence on exercise-induced effects on neurotrophic factors alongside neuroprotection in pwMS.

Aim: To (1) systematically review the evidence on acute (one session) and/or chronic (several sessions) exercise-induced changes in neurotrophic factors in pwMS and (2) investigate the potential translational link between exercise-induced changes in neurotrophic factors and neuroprotection.

Resistance exercise training at different loads in frail and healthy older adults: A randomised feasibility trial.

Objectives: This trial aimed to determine the feasibility of recruitment, retention, adherence, and safety of a resistance training (RT) intervention to skeletal muscle failure in both frail and non-frail older adults.

Design: An 8-week randomised feasibility trial.

Setting And Participants: Older adults, with and without frailty, recruited from both clinics and community.

All-Optical Electrophysiology in hiPSC-Derived Neurons With Synthetic Voltage Sensors.

Voltage imaging and "all-optical electrophysiology" in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

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.

The effect of exercise on quality of life and activities of daily life in frail older adults: A systematic review of randomised control trials.

While the positive effects of exercise on frailty are well documented, the effect of exercise on quality of life (QoL) and activities of daily living (ADL) in frail older adults remains less certain. Therefore, this paper aimed to systematically review the literature investigating the effect of exercise on QoL and ADL in this group. Embase, MEDLINE, CENTRAL, PEDro and Web of Science Core Collections were searched systematically using relevant MeSH terms.

Context-dependent plasticity of adult-born neurons regulated by cortical feedback.

In a complex and dynamic environment, the brain flexibly adjusts its circuits to preferentially process behaviorally relevant information. Here, we investigated how the olfactory bulb copes with this demand by examining the plasticity of adult-born granule cells (abGCs). We found that learning of olfactory discrimination elevates odor responses of young abGCs and increases their apical dendritic spines.

Feasibility of Data-driven EMG Signal Generation using a Deep Generative Model.

Despite recent advancements in the field of pattern recognition-based myoelectric control, the collection of a high quality training set remains a challenge limiting its adoption. This paper proposes a framework for a possible solution by augmenting short training protocols with subject-specific synthetic electromyography (EMG) data generated using a deep generative network, known as SinGAN. The aim of this work is to produce high quality synthetic data that could improve classification accuracy when combined with a limited training protocol.

Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.

Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning.

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.

This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.

Feature Extraction and Selection for Pain Recognition Using Peripheral Physiological Signals.

In pattern recognition, the selection of appropriate features is paramount to both the performance and the robustness of the system. Over-reliance on machine learning-based feature selection methods can, therefore, be problematic; especially when conducted using small snapshots of data. The results of these methods, if adopted without proper interpretation, can lead to sub-optimal system design or worse, the abandonment of otherwise viable and important features.

High intensity interval training for people with multiple sclerosis: A systematic review.

Background: Aerobic high intensity interval training (HIIT) is safe in the general population and more efficient in improving fitness than continuous moderate intensity training. The body of literature examining HIIT in multiple sclerosis (MS) is expanding but to date a systematic review has not been conducted. The aim of this review was to investigate the efficacy and safety of HIIT in people with MS.

Access to and Use of Clinical Services and Disease-Modifying Therapies by People with Progressive Multiple Sclerosis in the United Kingdom.

Background: According to current UK guidelines, everyone with progressive multiple sclerosis (MS) should have access to an MS specialist, but levels of access and use of clinical services is unknown. We sought to investigate access to MS specialists and use of clinical services and disease-modifying therapies (DMTs) by people with progressive MS in the United Kingdom.

Methods: A UK-wide online survey was conducted via the UK MS Register.

Validation of smartphone step count algorithm used in STARFISH smartphone application.

Background: Smartphone sensors are underutilised in rehabilitation.

Objective: To validate the step count algorithm used in the STARFISH smartphone application.

Methods: Twenty-two healthy adults (8 male, 14 female) walked on a treadmill for 5 minutes at 0.

Access, delivery and perceived efficacy of physiotherapy and use of complementary and alternative therapies by people with progressive multiple sclerosis in the United Kingdom: An online survey.

Introduction: All people with progressive MS in the United Kingdom should have access to physiotherapy through the National Health Service (NHS). However levels of access and delivery are unknown. Furthermore there is no research on perceived efficacy of physiotherapy or the use of complementary and alternative medicine in people with progressive MS in the United Kingdom.

Foxn4 promotes gene expression required for the formation of multiple motile cilia.

Multiciliated cell (MCC) differentiation involves extensive organelle biogenesis required to extend hundreds of motile cilia. Key transcriptional regulators known to drive the gene expression required for this organelle biogenesis are activated by the related coiled-coil proteins Multicilin and Gemc1. Here we identify foxn4 as a new downstream target of Multicilin required for MCC differentiation in Xenopus skin.

Physiotherapy Rehabilitation for People With Progressive Multiple Sclerosis: A Systematic Review.

Objective: To assess the efficacy of physiotherapy interventions, including exercise therapy, for the rehabilitation of people with progressive multiple sclerosis.

Data Sources: Five databases (Cochrane Library, Physiotherapy Evidence Database [PEDro], Web of Science Core Collections, MEDLINE, Embase) and reference lists of relevant articles were searched.

Study Selection: Randomized experimental trials, including participants with progressive multiple sclerosis and investigating a physiotherapy intervention or an intervention containing a physiotherapy element, were included.