Study of Cardiorespiratory and Sweat Monitoring Wearable Architecture for Coal Mine Workers

Sarkar, Supriya; Ghosh, Aayushman; Ghosh, Shiuli Subhra

doi:10.1109/tencon50793.2020.9293818

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…The most commonly used physiological signals to detect stress state are cardiac activity, respiratory activity, brain activity [18], body temperature [19], sweating, eye movements, facial expressions and gestures. Previous studies about human health monitoring systems and the measurement of vital parameters have been considered for general purposes such as driving assistance and fatigue recognition [20] [21], office worker stress monitoring [22], coal mine worker safety [23], and remote videomediated assistance [24]. In human-robot interaction, human emotions, especially negative ones, influence the performance of robotic interventions.…”

Section: B Vital Parameters Monitoring Of An Operatormentioning

confidence: 99%

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

et al. 2023

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Remote robotic interventions and maintenance tasks are frequently required in hazardous environments. Particularly, missions with a redundant mobile manipulator in the world's most complex machine, the CERN Large Hadron Collider, are performed in a sensitive underground environment with radioactive or electromagnetic hazards, bringing further challenges in safety and reliability. The mission's success depends on the robot's hardware and software, and when the tasks become too unpredictable to execute autonomously, the operators need to make critical decisions. However, in most current humanmachine systems, the state of the human is neglected. In this context, a novel 3D Mixed Reality (MR) human-robot interface with the Operator Monitoring System (OMS) was developed to advance safety and task efficiency with improved spatial awareness, advanced manipulator control, and collision avoidance. However, new techniques could increase the system's sophistication and add to the operator's workload and stress. Therefore, for operational validation, the 3D MR interface had to be compared with an operational 2D interface, which has been used in hundreds of interventions. With the 3D MR interface, the execution of precise approach tasks was faster, with no increased workload or physiological response. The new 3D MR techniques improved the teleoperation quality and safety while maintaining similar effects on the operator. The OMS worked jointly with the interface and performed well with operators with varied teleoperation backgrounds facing a stressful real telerobotic scenario in the LHC. The paper contributes to the methodology for human-centred interface evaluation incorporating the user's physiological state: heart rate, respiration rate and skin electrodermal activity, and combines it with the NASA TLX assessment method, questionnaires, and task execution time. It provides novel approaches to operator state identification, the GUI-OMS software architecture, and the evaluation of the 3D MR techniques. The solutions can be practically applied in mission-critical applications, such as telesurgery, space robotics, uncrewed transport vehicles and semi-autonomous machinery.

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Section: B Vital Parameters Monitoring Of An Operatormentioning

confidence: 99%

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

et al. 2023

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“…Some hospitals are promoting the use of wearable devices to track the habits of hospital workers and improve corporate wellness among workers [ 4 ]. Similarly, the coal mining industry has adopted a sweat monitoring wearable architecture to enable the observation of critical indicators of performance as well as the stress of mineworkers [ 5 ]. Additionally, medical professionals also utilize several wearable devices, including fitness trackers, to acquire physiological, behavioral, and contextual data for the diagnosis, treatment, and management of chronic diseases [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

Xu,

He,

et al. 2023

Sensors

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To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers’ health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers’ health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.

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“…Wearable sensors have been widely used to monitor physiological parameters in many industries such as health (7)(8)(9)(10)(11), sports (12,13), mining (14,15), and construction (16)(17)(18)(19)(20). Within each industry, several commercial wearable sensors are currently available (8).…”

Section: Introductionmentioning

confidence: 99%

Test-retest reliability, validity, and responsiveness of a textile-based wearable sensor for real-time assessment of physical fatigue in construction bar-benders

Anwer

Antwi‐Afari

et al. 2021

Journal of Building Engineering

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Study of Cardiorespiratory and Sweat Monitoring Wearable Architecture for Coal Mine Workers

Cited by 7 publications

References 14 publications

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

Enhanced Human–Robot Interface With Operator Physiological Parameters Monitoring and 3D Mixed Reality

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

Test-retest reliability, validity, and responsiveness of a textile-based wearable sensor for real-time assessment of physical fatigue in construction bar-benders

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