Validity and reliability of a wearable insole pressure system for measuring gait parameters to identify safety hazards in construction

Purpose Construction workers are frequently exposed to safety hazards on sites. Wearable sensing systems (e.g. wearable inertial measurement units (WIMUs), wearable insole pressure system (WIPS)) have been used to collect workers' gait patterns for distinguishing safety hazards. However, the performance of measuring WIPS-based gait parameters for identifying safety hazards as compared to a reference system (i.e. WIMUs) has not been studied. Therefore, 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. Design/methodology/approach Five fall-risk events were conducted in a laboratory setting, and the performance of the proposed approach was assessed by calculating the mean difference (MD), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and intraclass correlation coefficient (ICC) of five gait parameters. Findings Comparable results of MD, MAE, MAPE and RMSE were found between WIPS-based gait parameters and the reference system. Furthermore, all measured gait parameters had validity (ICC = 0.751) and test-retest reliability (ICC = 0.910) closer to 1, indicating a good performance of measuring WIPS-based gait parameters for distinguishing safety hazards. Research limitations/implications Overall, this study supports the relevance of developing a WIPS as a noninvasive wearable sensing system for identifying safety hazards on construction sites, thus highlighting the usefulness of its applications for construction safety research. Originality/value This is the first study to examine the performance of a wearable insole pressure system for identifying safety hazards in construction

Wearable sensing and mining of the informativeness of older adults : physiological, behavioral, and cognitive responses to detect demanding environmental conditions

Due to the decline in functional capability, older adults are more likely to encounter excessively demanding environmental conditions (that result in stress and/or mobility limitation) than the average person. Current efforts to detect such environmental conditions are inefficient and are not person-centered. This study presents a more efficient and person-centered approach that involves using wearable sensors to collect continuous bodily responses (i.e., electroencephalography, photoplethysmography, electrodermal activity, and gait) and location data from older adults to detect demanding environmental conditions. Computationally, this study developed a Random Forest algorithm—considering the informativeness of the bodily response—and a hot spot analysis-based approach to identify environmental locations with high demand. The approach was tested on data collected from 10 older adults during an outdoor environmental walk. The findings demonstrate that the proposed approach can detect demanding environmental conditions that are likely to result in stress and/or limited mobility for older adults

Evaluation of Construction Workers Physical Demands Through Computer Vision-Based Kinematic Data Collection and Analysis.

Construction workers are frequently exposed to considerable physical demands as construction tasks largely rely on manual handling tasks. Excessive physical demands beyond one’s capabilities may lead to productivity, safety, and health issues in construction. Assessing physical demands from work helps not only to identify the fundamental cause of the gap between physical demands and capabilities, but also to find an appropriate method of intervention to eliminate the gap. Although many researchers have worked on methods for evaluating physical demands, the use of these methods in construction is limited due to the difficulty in collecting reliable kinematic data with the required level of detail according to evaluation methods. In addition, a discussion on how excessive physical demands affect workers’ time and cost performance in construction is sparse. With this background, the overarching goal of this dissertation is twofold: 1) to enable practitioners to evaluate construction workers’ physical demands on sites in a timely manner without technical sophistication or skill, and 2) to enhance our understanding of the impact of excessive physical demands on construction operations. Specifically, computer vision-based approaches are proposed to non-invasively collect kinematic data by recording and processing video sequences. This data can be used to quantify and evaluate physical demands through postural ergonomic risk assessment and biomechanical analysis. Also, worker-oriented modeling and simulation of construction operations is proposed to capture the interactive effects between excessive physical demands and construction operations by combining a Discrete Event Simulation (DES) model with biomechanical and fatigue models. This approach enables us to evaluate workers’ fatigue from operations in the early design stages, and then to quantify the impact of fatigue on workers’ time and cost performance. The proposed approaches have been tested through a series of laboratory tests and case studies, proving their feasibility and applicability under real conditions at construction sites. Ultimately, continuous evaluation and monitoring of physical demands during construction tasks using the proposed approaches will enhance the understanding of the gap between physical work demands and workers’ capability, and offer a firm foundation for the improvement of workers’ health (e.g., reducing WMSDs), as well as productivity in construction.PhDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133251/1/junoseo_1.pd

A case study of motion data-driven biomechanical assessment for identifying and evaluating ergonomic interventions in reinforced-concrete work

Physical ergonomic intervention (e.g., use of tools) is adopted to improve working postures in the reinforced-concrete trade. However, evaluating its effectiveness often focuses on a specific body part mostly concerned although posture modification in one part may physically affect another. This paper presents a case study to comprehensively examine the effectiveness of existing ergonomic interventions. In the experiment, a subject repeated typical motions 15 times, which served as the baseline of biomechanical simulation with the 50th percentile of the anthropometric size of the U.S. population. 3D-motion-capture and biomechanical simulation were then adopted to collect full-body posture data and compute the load exerted on body parts with population strength capability. The results indicated that the disc compressions and joint moments were reduced by 45.41% and 31.86% whereas the effectiveness varied among the body parts (e.g., elbow, shoulder, knee). These results suggest that ergonomic interventions can lessen physical demands by carefully selecting an appropriate intervention for specific tasks and body parts in practice

BIM-Based Spatial Augmented Reality (SAR) for Architectural Design Collaboration: A Proof of Concept

Three-dimensional (3D) visualization technology, such as augmented reality (AR), has served as the display for building information modeling (BIM)-based architectural design collaboration to provide more effective design observation and communication for stakeholders. That said, AR has several technical limitations in terms of personal device issues, user experience, and visualization quality. A new form of AR called spatial augmented reality (SAR) has been introduced to address these issues, which uses a digital projector to present graphics on physical objects for augmenting real-world objects. Therefore, SAR has great benefits and potentials to combine with BIM for design collaboration. This paper introduces a BIM-based SAR operational framework, where 3D building models generated from BIM software are imported to projection mapping tools to display building surface textures on physical white building models. A case study using Revit and 3ds Max as the BIM software, and MadMapper as the projection mapping tool, was conducted to demonstrate the feasibility of the proposed framework and to evaluate the projection performance of SAR. The case study showed that the texture of BIM models could be projected on the objects clearly and realistically. Additionally, the proposed SAR method potentially offers intuitive observation of building models and comfortable wear-free experience for collaborative design, and the qualitative analysis by changing the parameters was conducted to test the different projection conditions. Since it is expected that the use of SAR can be promoted by overcoming the discussed technical limitations and possible solution application, this study aims to traceability provide the whole process of BIM-based SAR for architectural design collaboration