An Efficient Framework for Personalizing EMG-Driven Musculoskeletal Models Based on Reinforcement Learning.

This study aimed to develop a novel framework to quickly personalize electromyography (EMG)-driven musculoskeletal models (MMs) as efferent neural interfaces for upper limb prostheses. Our framework adopts a generic upper-limb MM as a baseline and uses an artificial neural network-based policy to fine-tune the model parameters for MM personalization. The policy was trained by reinforcement learning (RL) to heuristically adjust the MM parameters to maximize the accuracy of estimated hand and wrist motions from EMG inputs.

Angle-programmed tendril-like trajectories enable a multifunctional gripper with ultradelicacy, ultrastrength, and ultraprecision.

Achieving multicapability in a single soft gripper for handling ultrasoft, ultrathin, and ultraheavy objects is challenging due to the tradeoff between compliance, strength, and precision. Here, combining experiments, theory, and simulation, we report utilizing angle-programmed tendril-like grasping trajectories for an ultragentle yet ultrastrong and ultraprecise gripper. The single gripper can delicately grasp fragile liquids with minimal contact pressure (0.

Offline Evaluation Matters: Investigation of the Influence of Offline Performance of EMG-Based Neural-Machine Interfaces on User Adaptation, Cognitive Load, and Physical Efforts in a Real-Time Application.

There has been controversy about the value of offline evaluation of EMG-based neural-machine interfaces (NMIs) for their real-time application. Often, conclusions have been drawn after studying the correlation of the offline EMG decoding accuracy/error with the NMI user's real-time task performance without further considering other important human performance metrics such as adaptation rate, cognitive load, and physical effort. To fill this gap, this study aimed to investigate the relationship between the offline decoding accuracy of EMG-based NMIs and user adaptation, cognitive load, and physical effort in real-time NMI use.

Assessing workload in using electromyography (EMG)-based prostheses.

Using prosthetic devices requires a substantial cognitive workload. This study investigated classification models for assessing cognitive workload in electromyography (EMG)-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes. Features selection algorithm, hyperparameter tuning with grid search, and k-fold cross-validation were applied to select the most important features and find the optimal models.

Offline Evaluation Matters: Investigation of the Influence of Offline Performance on Real-time Operation of Electromyography-based Neural-Machine Interfaces.

There has been a debate on the most appropriate way to evaluate electromyography (EMG)-based neural-machine interfaces (NMIs). Accordingly, this study examined whether a relationship between offline kinematic predictive accuracy (R2) and user real-time task performance while using the interface could be identified. A virtual posture-matching task was developed to evaluate motion capture-based control and myoelectric control with artificial neural networks (ANNs) trained to low (R ≈ 0.

Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface.

Objective: In this study, we aimed to develop a novel electromyography (EMG)-based neural machine interface (NMI), called the Neural Network-Musculoskeletal hybrid Model (N2M2), to decode continuous joint angles. Our approach combines the concepts of machine learning and musculoskeletal modeling.

Methods: We compared our novel design with a musculoskeletal model (MM) and 2 continuous EMG decoders based on artificial neural networks (ANNs): multilayer perceptrons (MLPs) and nonlinear autoregressive neural networks with exogenous inputs (NARX networks).

Comparing Reinforcement Learning Agents and Supervised Learning Neural Networks for EMG-Based Decoding of Continuous Movements.

Recent work on electromyography (EMG)-based decoding of continuous joint kinematics has included model-based approaches, such as musculoskeletal modeling, as well as model-free approaches such as supervised learning neural networks (SLNN). This study aimed to present a new kinematics decoding framework based on reinforcement learning (RL), which combines machine learning and model-based approaches together. We compared the performance and robustness of our new method with those of the SLNN approach.

Hepatocellular Carcinoma Without Cirrhosis Presenting With Hypercalcemia: Case Report and Literature Review.

Background: Hepatocellular carcinoma (HCC) in non-cirrhotic livers is an uncommon finding and can present insidiously in patients. Another uncommon finding in HCC, and one of poor prognosis, is the presence of paraneoplastic diseases such as hypercalcemia. We report a case of a 66-year-old previous healthy Filipina woman who after routine laboratory evaluation was discovered to have hypercalcemia as the first sign of an advanced HCC without underlying cirrhosis.

Bactericidal and cytotoxic effects of chloramine-T on wound pathogens and human fibroblasts in vitro.

Objectives: To evaluate cytotoxicity and bactericidal effects of chloramine-T.

Methods: In vitro study of various concentrations and exposure times to preparations containing human fibroblasts or 1.5 x 10 colony forming units per milliliter (CFU/mL) of 3 gram-positive bacteria-Staphylococcus aureus, methicillin-resistant S aureus, and vancomycin-resistant Enterococcus faecalis-and 2 gram-negative bacteria-Escherichia coli and Pseudomonas aeruginosa-with and without fetal bovine serum present.

Effect of computer keyboard slope on wrist position and forearm electromyography of typists without musculoskeletal disorders.

Background And Purpose: Positioning a computer keyboard with a downward slope reduces wrist extension needed to use the keyboard and has been shown to decrease pressure in the carpal tunnel. However, whether a downward slope of the keyboard reduces electromyographic (EMG) activity of the forearm muscles, in particular the wrist extensors, is not known.

Subjects And Methods: Sixteen experienced typists participated in this study and typed on a conventional keyboard that was placed on slopes at angles of 7.

A randomized controlled clinical trial to evaluate the effects of noncontact normothermic wound therapy on chronic full-thickness pressure ulcers.

Objective: To determine the effect of noncontact normothermic wound therapy (NNWT) versus standard wound care on chronic full-thickness pressure ulcers.

Design: Prospective, randomized, controlled trial.

Setting: Veterans administration medical center and 7 long-term-care facilities.