The use of wearable devices in cognitive fatigue: current trends and future intentions

Arachchige, Sachini N. K. Kodithuwakku; Burch, Reuben F.; Chander, Harish; Turner, Alana J.; Knight, Adam C.

doi:10.1080/1463922x.2021.1965670

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…The development of modern sports is characterized by the combination of sports and technology, from athletes' clothing, and sports equipment to training methods, all of which are rich in high-tech components. Therefore, the technology of sports training is the common goal of sports and science and technology workers [3]. Volleyball, with the continuous innovation of competition rules, has led to the innovation of volleyball techniques and tactics.…”

Section: Introductionmentioning

confidence: 99%

Research on Training Model of Volleyball Based on Flexible Strain Sensing Network for Training

Yin

Chen

et al. 2022

Journal of Sensors

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This paper provides an in-depth investigation and analysis of volleyball training patterns using a sensing network composed of flexible strain sensors. This paper models the motion of the hand and leg joints based on an improved human posture estimation technique. The pattern recognition of human motion used the fusion of acceleration sensor and spiral meter data, the output of human motion information from the spiral meter, combined with GA-BP neural network algorithm to repair and fuse the output information, effectively improving the accuracy of posture angle measurement. Based on wireless positioning technology, an improved RFID positioning algorithm combining the LANDMARC algorithm and weighted center-of-mass algorithm in wireless sensor network is proposed, and the positioning accuracy reaches 89.2%; and the error feedback mechanism is introduced to improve the improved algorithm, and the positioning accuracy is improved again by 3.2%. Finally, the two-way dynamic time regularization algorithm is used to align the input action with the standard action, and the action pose evaluation index is compared and analyzed for the action sequence after alignment to obtain the final action quality comparison analysis results. In this paper, the proposed method is applied in practical training, which can effectively locate the stance of athletes and evaluate their movement quality without disturbing them and can provide valuable analysis information to support coaches’ guidance.

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Section: Introductionmentioning

confidence: 99%

Research on Training Model of Volleyball Based on Flexible Strain Sensing Network for Training

Yin

Chen

et al. 2022

Journal of Sensors

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“…For example, recent years have witnessed the emergence of nonintrusive physiological measurements (such as smart wearables) of people’s health status, which provide abundant observable data that could facilitate fatigue estimation. Researchers have already begun to realize the potential of smartphone or smart wearable data in assessment and monitoring of psychological states (Kodithuwakku Arachchige et al, 2021). Specifically, vital signs such as heart rate and heart rate variability, which may be tracked via smartwatches or bands, are useful in predicting mental fatigue (Luo et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

The effects of mental fatigue on effort allocation: Modeling and estimation.

Wang¹,

Chang²,

Schmeichel³

et al. 2022

Psychological Review

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“…This accessibility allows for monitoring in multiple settings. Real-time data from measuring the respiratory rate give us feedback so that we can intervene or make adjustments during activities to prevent or alleviate cognitive fatigue effectively [105]. Unlike self-reported measures, which are subjective, the respiratory rate provides an objective physiological marker, reducing the potential biases associated with subjective assessments.…”

Section: Respiratory Ratementioning

confidence: 99%

“…Recent advancements in miniaturized sensors and wearables have shown promise [189,207], but challenges remain in balancing data quality with user comfort. Furthermore, the precision and reliability of identifying cognitive fatigue through wearables are lower compared to the detection of physical fatigue, primarily due to the influence of concurrent factors like emotions or exercise [105]. Research should prioritize the development of unobtrusive, portable sensing systems that capture diverse physiological and behavioral markers seamlessly during daily activities.…”

Section: Research Gaps and Future Directionsmentioning

confidence: 99%

Examining the Landscape of Cognitive Fatigue Detection: A Comprehensive Survey

Karim,

Pavel,

Nikanfar

et al. 2024

Technologies

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Cognitive fatigue, a state of reduced mental capacity arising from prolonged cognitive activity, poses significant challenges in various domains, from road safety to workplace productivity. Accurately detecting and mitigating cognitive fatigue is crucial for ensuring optimal performance and minimizing potential risks. This paper presents a comprehensive survey of the current landscape in cognitive fatigue detection. We systematically review various approaches, encompassing physiological, behavioral, and performance-based measures, for robust and objective fatigue detection. The paper further analyzes different challenges, including the lack of standardized ground truth and the need for context-aware fatigue assessment. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and address the multifaceted challenge of cognitive fatigue detection.

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The use of wearable devices in cognitive fatigue: current trends and future intentions

Cited by 9 publications

References 51 publications

Research on Training Model of Volleyball Based on Flexible Strain Sensing Network for Training

Research on Training Model of Volleyball Based on Flexible Strain Sensing Network for Training

The effects of mental fatigue on effort allocation: Modeling and estimation.

Examining the Landscape of Cognitive Fatigue Detection: A Comprehensive Survey

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