Monitoring worker fatigue using wearable devices: A case study to detect changes in gait parameters

Baghdadi, Amir; Cavuoto, Lora; Jones-Farmer, Allison; Rigdon, Steven E.; Esfahani, Ehsan T.; Megahed, Fadel M.

doi:10.1080/00224065.2019.1640097

Cited by 49 publications

(34 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, to label the training data in fatigue/non-fatigue states, subjective self-reported thresholds were used for the participants in a study ( Zhang J. et al, 2014 ). Still, the participant’s perception of fatigue is not necessarily aligned with the physical changes and walking patterns due to fatigue ( Baghdadi et al, 2021 ). Even in stricter clinical settings where experts manually label the data, there is the problem of changed gait due to the controlled environment.…”

Section: Health and Wellnessmentioning

confidence: 99%

“…In some scenarios, the number of input features is larger than the sample size ( Zhang et al, 2015 ). In others, the sample size is too small to be representative enough to support any assertions fully and reduces the paper to the level of an exploratory effort ( Baghdadi et al, 2021 ). In others, researchers generate synthetic data ( Arifoglu and Bouchachia, 2017 ) that reflect features similar to the disease for an abnormal gait detection task; apply deep AL technique to reduce the number of required labels and consequently the time cost of manual labeling in a gait phase detection task.…”

Section: Health and Wellnessmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

View full text Add to dashboard Cite

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

show abstract

Section: Health and Wellnessmentioning

confidence: 99%

Section: Health and Wellnessmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

View full text Add to dashboard Cite

show abstract

“…Safety is one of the most critical challenges in the construction industry, which suffers from a high number of accidents (highest among all U.S. industries) [20]. These WSDs have been found to be very useful on construction sites for detecting near-miss falls or reducing fall-related injuries [10,[21][22][23], identifying unsafe posture in workers and potential work-related ergonomic risks [9,24], monitoring workers' fatigue and workload stress [18,25,26], and other applications. Many of these wearable devices are still in development and not commercially available.…”

Section: Background 21 Wsd Research Overviewmentioning

confidence: 99%

“…A few studies have evaluated the functions and applications of WSDs and how they can be used to effectively enhance worker safety and health. For instance, physiological data such as heart rate, breathing rate, body posture, etc., can be automatically tracked and analyzed using different sensors and systems to monitor workers' fatigue, identify unsafe posture in workers, and prevent fall-related injuries [10,21,24,25,29]. In environmental sensing, hazardous gases, air quality, air particles, and possibly toxic chemical leaks, and inclement atmospheric conditions can be monitored on jobsites to provide early warning signals to construction workers [17,30].…”

Section: Wsd Functions and Applications In Constructionmentioning

confidence: 99%

Wearable Sensing Devices: Towards the Development of a Personalized System for Construction Safety and Health Risk Mitigation

Nnaji

Awolusi

Park

et al. 2021

Sensors

View full text Add to dashboard Cite

Wearable sensing devices (WSDs) are increasingly helping workers stay safe and healthy in several industries. However, workers, especially in the construction industry, have shown some aversion towards the use of WSDs due to their ability to capture specific information that may be considered personal and private. However, this revered information may provide some critical insight needed by management to plan and optimize worksite safety and support technology adoption in decision making. Therefore, there is a need to develop personalized WSD systems that are mutually beneficial to workers and management to ensure successful WSD integration. The present study aims to contribute to knowledge and practice by filling this critical gap using insight from 330 construction workers with experience using WSDs. The results from this study indicate that all 11 WSD functions identified through this study play a vital role in improving worker safety and health and that approximately two out of three workers are open to sharing the physiological and environmental information captured using these WSDs with their management. However, functions for detecting workers’ proximity to workplace hazards, specifically energized electrical materials, toxic gas, and fire/smoke, were the most critical functions that had mutual value to workers and management. Finally, the present study proposed and evaluated a phased personalized WSD system that should encourage successful WSD integration.

show abstract

“…Although our case study focuses on the use of predictive monitoring to improve the quality of education, the presented methods can be used in any setting where clear hierarchical data structures exist. Baghdadi et al (2019) stated that the ability to estimate when the performance will deteriorate and what type of intervention optimizes recovery can improve the quality and productivity and reduce risk concerning worker fatigue. Our case study offers a very similar approach to improve the quality and productivity of high school education by monitoring student performance.…”

Section: Predictive Monitoringmentioning

confidence: 99%

Multilevel process monitoring: A case study to predict student success or failure

Huberts

Schoonhoven

Does

2020

Journal of Quality Technology

View full text Add to dashboard Cite

In this case study, we demonstrate the use of multilevel process monitoring in quality control. Using high school data, we answer three research questions related to high school student progress during an academic year. The questions are (1) What determines student performance? (2) How can statistical process monitoring be used in monitoring student progress? (3) What method can be used for predictive monitoring of student results? To answer these questions, we worked together with a Dutch high school and combined hierarchical Bayesian modeling with statistical and predictive monitoring procedures. The results give a clear blueprint for student progress monitoring.

show abstract

Monitoring worker fatigue using wearable devices: A case study to detect changes in gait parameters

Cited by 49 publications

References 62 publications

A Survey of Human Gait-Based Artificial Intelligence Applications

A Survey of Human Gait-Based Artificial Intelligence Applications

Wearable Sensing Devices: Towards the Development of a Personalized System for Construction Safety and Health Risk Mitigation

Multilevel process monitoring: A case study to predict student success or failure

Contact Info

Product

Resources

About