Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome.

Background And Aims: The importance of risk stratification in patients with chest pain extends beyond diagnosis and immediate treatment. This study sought to evaluate the prognostic value of electrocardiogram feature-based machine learning models to risk-stratify all-cause mortality in those with chest pain.

Methods: This was a prospective observational cohort study of consecutive, non-traumatic patients with chest pain.

Design and Validation of a Modular, Backdrivable Ankle Exoskeleton.

Partial-assist ankle exoskeletons have been limited by inherent trade-offs between favorable characteristics including high torque capacity, high control bandwidth, back-drivability, compliance, and low mass. Emerging quasi-direct drive actuators have a rigid transmission with a low gear ratio, enabling inherent backdrivability and compliance with accurate torque and position control. Our existing modular, backdrivable exoskeleton system () uses quasi-direct drive actuators at the hip and/or knee to deliver high assistive torques alongside low dynamic backdrive torques, enabling natural interaction with users with remnant voluntary motion.

Controlling Powered Prosthesis Kinematics Over Continuous Inter-Leg Transitions Between Walking and Stair Ascent/Descent.

Although powered prosthetic legs have enabled more biomimetic joint kinematics during steady-state activities like walking and stair climbing, transitions between these activities are usually handled by discretely switching controllers without considering biomimicry or the distinct role of the leading leg. This study introduces two data-driven, phase-based kinematic control approaches for seamless inter-leg transitions (i.e.

Improving Task-Agnostic Energy Shaping Control of Powered Exoskeletons with Task/Gait Classification.

Emerging task-agnostic control methods offer a promising avenue for versatile assistance in powered exoskeletons without explicit task detection, but typically come with a performance trade-off for specific tasks and/or users. One such approach employs data-driven optimization of an energy shaping controller to provide naturalistic assistance across essential daily tasks with passivity/stability guarantees. This study introduces a novel control method that merges energy shaping with a machine learning-based classifier to deliver optimal support accommodating diverse individual tasks and users.