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.

Towards a Unified Approach for Continuously-Variable Impedance Control of Powered Prosthetic Legs over Walking Speeds and Inclines.

Research in powered prosthesis control has explored the use of impedance-based control algorithms due to their biomimetic capabilities and intuitive structure. Modern impedance controllers feature parameters that smoothly vary over gait phase and task according to a data-driven model. However, these recent efforts only use continuous impedance control during stance and instead utilize discrete transition logic to switch to kinematic control during swing, necessitating two separate models for the different parts of the stride.

Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study.

Background: Beyond exposure to cigarette smoking and aging, the factors that influence lung function decline to incident chronic obstructive pulmonary disease (COPD) remain unclear. Advancements have been made in categorizing COPD into emphysema and airway predominant disease subtypes; however, predicting which healthy individuals will progress to COPD is difficult because they can exhibit profoundly different disease trajectories despite similar initial risk factors. This study aimed to identify clinical, genetic, and radiological features that are directly linked-and subsequently predict-abnormal lung function.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject.

A Narrative Review on the Viability of Osteopathic Manipulative Medicine in Treating Irritable Bowel Syndrome With Constipation (IBS-C).

Irritable bowel syndrome (IBS) is a functional gastrointestinal disorder characterized by chronic abdominal pain and alterations in bowel habits, with global prevalence. The etiology of the disease is likely multifactorial; however, autonomic nervous system (ANS) dysfunction and immune-mediated inflammation may contribute the most to the hallmark symptoms of abdominal pain and altered motility of the gut. Current pharmacological therapies operate to modulate intestinal transit, alter the composition of the gut flora and control pain.

Cone Synaptic function is modulated by the leucine rich repeat (LRR) adhesion molecule LRFN2.

Daylight vision is mediated by cone photoreceptors in vertebrates, which synapse with bipolar cells (BCs) and horizontal (HCs) cells. This cone synapse is functionally and anatomically complex, connecting to 8 types of depolarizing BCs (DBCs) and 5 types of hyperpolarizing BCs (HBCs) in mice. The dendrites of DBCs and HCs cells make invaginating ribbon synapses with the cone axon terminal, while HBCs form flat synapses with the cone pedicles.

Robustification of Bayesian-Inference-Based Gait Estimation for Lower-limb Wearable Robots.

Lower-limb wearable robots designed to assist people in everyday activities must reliably recover from any momentary confusion about what the user is doing. Such confusion might arise from momentary sensor failure, collision with an obstacle, losing track of gait due to an out-of-distribution stride, etc. Systems that infer a user's walking condition from angle measurements using Bayesian filters (e.

Data-Driven Phase-Based Control of a Powered Knee-Ankle Prosthesis for Variable-Incline Stair Ascent and Descent.

Powered knee-ankle prostheses can offer benefits over conventional passive devices during stair locomotion by providing biomimetic net-positive work and active control of joint angles. However, many modern control approaches for stair ascent and descent are often limited by time-consuming hand-tuning of user/task-specific parameters, predefined trajectories that remove user volition, or heuristic approaches that cannot be applied to both stair ascent and descent. This work presents a phase-based hybrid kinematic and impedance controller (HKIC) that allows for semi-volitional, biomimetic stair ascent and descent at a variety of step heights.

CellularPotts.jl: simulating multiscale cellular models in Julia.

Summary: CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection.

Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters.

Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with pathological gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based controllers use phase (normalized stride time) and task variables (e.

Improving Amputee Endurance over Activities of Daily Living with a Robotic Knee-Ankle Prosthesis: A Case Study.

Robotic knee-ankle prostheses have often fallen short relative to passive microprocessor prostheses in time-based clinical outcome tests. User ambulation endurance is an alternative clinical outcome metric that may better highlight the benefits of robotic prostheses. However, previous studies were unable to show endurance benefits due to inaccurate high-level classification, discretized mid-level control, and insufficiently difficult ambulation tasks.

Controlling Powered Prosthesis Kinematics over Continuous Transitions Between Walk and Stair Ascent.

One of the primary benefits of emerging powered prosthetic legs is their ability to facilitate step-over-step stair ascent by providing positive mechanical work. Existing control methods typically have distinct steady-state activity modes for walking and stair ascent, where activity transitions involve discretely switching between controllers and often must be initiated with a particular leg. However, these discrete transitions do not necessarily replicate able-bodied joint biomechanics, which have been shown to continuously adjust over a transition stride.

An Energetic Approach to Task-Invariant Ankle Exoskeleton Control.

Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories.

Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles.

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This article presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle.

Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton.

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control.

Improving Sit/Stand Loading Symmetry and Timing Through Unified Variable Impedance Control of a Powered Knee-Ankle Prosthesis.

Individuals using passive prostheses typically rely heavily on their biological limb to complete sitting and standing tasks, leading to slower completion times and increased rates of osteoarthritis and lower back pain. Powered prostheses can address these challenges, but have control methods that divide sit-stand transitions into discrete phases, limiting user synchronization across the motion and requiring long manual tuning times. This paper extends our preliminary work using a thigh-based phase variable to parameterize optimized data-driven impedance parameter trajectories for sitting, standing, and walking, with only two classification modes.

Early Evidence of Chronic Obstructive Pulmonary Disease Obscured by Race-Specific Prediction Equations.

The identification of early chronic obstructive pulmonary disease (COPD) is essential to appropriately counsel patients regarding smoking cessation, provide symptomatic treatment, and eventually develop disease-modifying treatments. Disease severity in COPD is defined using race-specific spirometry equations. These may disadvantage non-White individuals in diagnosis and care.

Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes.

Many powered prosthetic devices use load cells to detect ground interaction forces and gait events. These sensors introduce additional weight and cost in the device. Recent proprioceptive actuators enable an algebraic relationship between actuator torques and ground contact forces.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI.

Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking.

Most impedance-based walking controllers for powered knee-ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking.