Reinforcement-Learning-Based Assistance Reduces Squat Effort with a Modular Hip-Knee Exoskeleton
This study evaluates a reinforcement-learning-based controller for a modular hip-knee exoskeleton to reduce physiological effort during repetitive squatting tasks.
Maximum Pressure Gradient Based Fall Risk Comparison of Passive and Active Back-Support Exoskeletons
This study compares the impact of active and passive back-support exoskeletons on worker stability by analyzing plantar pressure distribution to determine associated fall risks during carpentry tasks.
Stability Assessment during Active Back-Support Exoskeleton Use: Pressure–Time Integral Analysis
This study evaluates how active back-support exoskeletons influence stability and plantar pressure distribution across various construction carpentry tasks.
Influence of Active Back-Support Exoskeleton on Fall Hazard in Construction
This study examines the implications of using active back-support exoskeleton on fall risk during construction framing tasks, incorporating wearable pressure insoles for data collection.
Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device
This study aims to utilize a wearable insole device to identify and classify physical fatigue levels in construction workers.
Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data
The research objective is to automatically recognize and classify different types of awkward working postures in construction by using deep learning-based networks and wearable insole sensor data.
Wearable Sensing and Mining of the Informativeness of Older Adults’ Physiological, Behavioral, and Cognitive Responses to Detect Demanding Environmental Conditions
This study presents a more efficient and person-centered approach that involves using wearable sensors to collect continuous bodily responses and location data from older adults to detect demanding environmental conditions.
Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles
The paper presents a machine learning algorithm to detect and classify MMH tasks using minimally-intrusive instrumented insoles and chest-mounted accelerometers.
Forces at the Feet, Gait Timing, and Trunk Flexion/Extension Excursion While Walking with a Gear Belt or Gear Vest Load
The aim of this study was to evaluate the differences in gait and trunk posture for gear load carried on a gear belt and a gear vest.
Automated detection and classification of construction workers’ loss of balance events using wearable insole pressure sensors
The objective of the current study was to develop a novel method to detect and classify loss of balance events that could lead to falls.
Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System
This study examined the feasibility of using acceleration and foot plantar pressure distribution data captured by a wearable insole pressure system for automated recognition of overexertion-related construction workers’ activities and for assessing ergonomic risk levels.
Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction
This study examined the validity and reliability of measuring WIPS-based gait parameters as compared to WIMU-based gait parameters for distinguishing safety hazards in construction.
Trunk Flexion/Extension Excursion, Forces at the Feet and Gait Timing in Tactical Belt and Gear Vest Load Carriage
The aim of this study was to evaluate the differences in gear load carriage for law enforcement agents while walking.
Quantifying the physical intensity of construction workers, a mechanical energy approach
This paper proposes a novel framework for investigating the mechanical energy expenditure (MEE) of workers using a 3D biomechanical model based on computer vision-based techniques.
Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers
This study developed a novel and non-invasive method to automatically detect and classify awkward working postures based on foot plantar pressure distribution data measured by a wearable insole pressure system.
Fall risk assessment of construction workers based on biomechanical gait stability parameters using wearable insole pressure system
This research examined the changes in spatial foot regions and loss of balance events associated with biomechanical gait stability parameters based on foot plantar pressure patterns measured by wearable insole pressure system.
