AIMC Topic: Activities of Daily Living
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Wearable-Enabled Algorithms for the Estimation of Parkinson's Symptoms Evaluated in a Continuous Home Monitoring Setting Using Inertial Sensors.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Oct 29, 2024
Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson's disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of thi...
Tremor
Male
Dyskinesias
Middle Aged
Female
Monitoring, Ambulatory
Reproducibility of Results
Wearable Electronic Devices
Humans
Aged
Accelerometry
Parkinson Disease
Machine Learning
Hypokinesia
Sensitivity and Specificity
Algorithms
Equipment Design
Feasibility Studies
Activities of Daily Living
Video Recording
Exploring key factors influencing depressive symptoms among middle-aged and elderly adult population: A machine learning-based method.
Archives of gerontology and geriatrics
Oct 2, 2024
OBJECTIVE: This paper aims to investigate the key factors, including demographics, socioeconomics, physical well-being, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using mac...
Generisch-Net: A Generic Deep Model for Analyzing Human Motion with Wearable Sensors in the Internet of Health Things.
Sensors (Basel, Switzerland)
Sep 24, 2024
The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs...
Transparent RFID tag wall enabled by artificial intelligence for assisted living.
Scientific reports
Sep 16, 2024
Current approaches to activity-assisted living (AAL) are complex, expensive, and intrusive, which reduces their practicality and end user acceptance. However, emerging technologies such as artificial intelligence and wireless communications offer new...
A Novel Bilateral Underactuated Upper Limb Exoskeleton for Post-Stroke Bimanual ADL Training.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 16, 2024
This paper introduces a lightweight bilateral underactuated upper limb exoskeleton (UULE) designed to assist chronic stroke patients with distal joint (Elbow-Wrist) impairments during bimanual activities of daily living (ADL). The UULE aims to assist...
Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults.
Scientific reports
Sep 6, 2024
Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Her...
Prediction of cognitive impairment among Medicare beneficiaries using a machine learning approach.
Archives of gerontology and geriatrics
Sep 5, 2024
OBJECTIVE: Developing machine learning (ML) models to predict cognitive impairment among Medicare beneficiaries in the United States.
Empowering High-Level Spinal Cord Injury Patients in Daily Tasks With a Hybrid Gaze and FEMG-Controlled Assistive Robotic System.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 20, 2024
Individuals with high-level spinal cord injuries often face significant challenges in performing essential daily tasks due to their motor impairments. Consequently, the development of reliable, hands-free human-computer interfaces (HCI) for assistive...
CASL: Capturing Activity Semantics Through Location Information for Enhanced Activity Recognition.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for correspondin...
Machine learning-based prediction model of lower extremity deep vein thrombosis after stroke.
Journal of thrombosis and thrombolysis
Jul 27, 2024
This study aimed to apply machine learning (ML) techniques to develop and validate a risk prediction model for post-stroke lower extremity deep vein thrombosis (DVT) based on patients' limb function, activities of daily living (ADL), clinical laborat...
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