Results 101 to 110 of about 156,269,237 (277)
Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders
ABSTRACT Objective To explore whether routine outpatient video combined with deep learning‐based pose estimation and clinically interpretable kinematic features can support multi‐label phenotyping of co‐occurring hyperkinetic movement disorders (HMDs).
Laura Cif +17 more
wiley +1 more source
Simultaneous Forward and Inverse Human-in-the-Loop Optimization
Subjective user experience is important to human-robot interaction, but the outcomes users value, and how those preferences vary across individuals and contexts, are often unknown. While inverse learning approaches using human data can help identify user rewards, in many assistive settings the experimental costs of executing a control policy, measuring
Park, Kyeongwon, Collins, Steven H.
openaire +2 more sources
The Open-Loop Linear Quadratic Differential Game Revisited [PDF]
In this note we reconsider the indefinite open-loop Nash linear quadratic differential game with an infinite planning horizon.In particular we derive both necessary and sufficient conditions under which the game will have a unique equilibrium.linear ...
Engwerda, J.C.
core
ABSTRACT Objective Febrile seizures (FS) are currently classified as simple or complex primarily to describe seizure characteristics, with limited ability to identify the underlying etiology. We evaluated whether molecular diagnosis could complement conventional classification and improve the diagnosis and management of FS.
Jia Wang +22 more
wiley +1 more source
Human-in-the-loop: Real-time Preference Optimization
Optimization with preference feedback is an active research area with many applications in engineering systems where humans play a central role, such as building control and autonomous vehicles. While most existing studies focus on optimizing a static user utility, few have investigated its closed-loop behavior that accounts for system transients.
Wang, Wenbin +2 more
openaire +2 more sources
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
To create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task. However, reward engineering can be a difficult and time-consuming process. Instead, human-in-the-loop (HitL) RL approaches allow agents to learn reward functions from human feedback.
Calarina Muslimani, Matthew E. Taylor
openaire +4 more sources
This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks.
Ming Pi
doaj +1 more source
Objective The aim of this study was to test the hypothesis that multijoint osteoarthritis would modify the relation between time and frailty progression, in which more affected joints would lead to larger declines over six years compared to those without osteoarthritis using data from the Canadian Longitudinal Study on Aging.
Carson Halliwell +2 more
wiley +1 more source
Power Management of a Wind-Powered Microgrid Based on Qualitative Needs
Power management strategies for microgrids are typically designed around quantitative performance metrics such as cost, efficiency, and reliability. While effective in many settings, these approaches often do not fully account for qualitative, human ...
Maryam Yaghoubirad, John Hall
doaj +1 more source
Objective We describe the frequency, risk factors, severity, and management of actionable and serious adverse events (AAE and SAE) in children with newly diagnosed Juvenile Idiopathic Arthritis (JIA) in Canada. Methods We enrolled patients within 3 months of JIA diagnosis in the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry,
Bashayer Alnuaimi +10 more
wiley +1 more source

