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1. Zoom Exhaustion & Fatigue Scale

   https://doi.org/10.1016/j.chbr.2021.100119  

   Fauville, G.; Luo, M.; Queiroz, A.C.M.; Bailenson, J.N.; Hancock, J. (August 2021, Computers in Human Behavior Reports)
2. Anti-fatigue-fracture hydrogels

   https://doi.org/10.1126/sciadv.aau8528  

   Lin, Shaoting; Liu, Xinyue; Liu, Ji; Yuk, Hyunwoo; Loh, Hyun-Chae; Parada, German A.; Settens, Charles; Song, Jake; Masic, Admir; McKinley, Gareth H.; et al (January 2019, Science Advances)

   The emerging applications of hydrogels in devices and machines require hydrogels to maintain robustness under cyclic mechanical loads. Whereas hydrogels have been made tough to resist fracture under a single cycle of mechanical load, these toughened gels still suffer from fatigue fracture under multiple cycles of loads. The reported fatigue threshold for synthetic hydrogels is on the order of 1 to 100 J/m 2 . We propose that designing anti-fatigue-fracture hydrogels requires making the fatigue crack encounter and fracture objects with energies per unit area much higher than that for fracturing a single layer of polymer chains. We demonstrate that the controlled introduction of crystallinity in hydrogels can substantially enhance their anti-fatigue-fracture properties. The fatigue threshold of polyvinyl alcohol (PVA) with a crystallinity of 18.9 weight % in the swollen state can exceed 1000 J/m 2 . 

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3. RFID-Based Driving Fatigue Detection

   https://doi.org/10.1109/GLOBECOM38437.2019.9013301  

   Yang, Chao; Wang, Xuyu; Mao, Shiwen (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))
4. Fatigue Prediction for Repetitive Lifting

   https://doi.org/10.1115/DETC2023-116839  

   Barman, Shuvrodeb; Xiang, Yujiang; Rakshit, Ritwik; Yang, James (August 2023, ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Abstract In this study, the fatigue progression and optimal motion trajectory during repetitive lifting task is predicted by using a 10 degrees of freedom (DOFs) two-dimensional (2D) digital human model and a three-compartment controller (3CC) fatigue model. The numerical analysis is further validated by conducting an experiment under similar conditions. The human is modeled using Denavit-Hartenberg (DH) representation. The task is mathematically formulated as a nonlinear optimization problem where the dynamic effort of the joints is minimized subjected to physical and task specific constraints. A sequential quadratic programming method is used for the optimization process. The design variables include control points of (1) quartic B-splines of the joint angle profiles; and (2) the three compartment sizes profiles for the six physical joints of interest — spine, shoulder, elbow, hip, knee, and ankle. Both numerical and experimental liftings are performed with a 15.2 kg box as external load. The simulation reports the human joint torque profiles and the progression of joint fatigue. The joint torque profiles show periodic trends. A maximum of 17 cycles are predicted before the repetitive lifting task fails, which also reasonably agrees with that of the experimental results (16 cycles). This formulation is also a generalized one, hence it can be used for other repetitive motion studies as well. 

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5. Digital biomarker of mental fatigue

   https://doi.org/10.1038/s41746-021-00415-6  

   Tseng, Vincent Wen-Sheng; Valliappan, Nachiappan; Ramachandran, Venky; Choudhury, Tanzeem; Navalpakkam, Vidhya (December 2021, npj Digital Medicine)

   Abstract Mental fatigue is an important aspect of alertness and wellbeing. Existing fatigue tests are subjective and/or time-consuming. Here, we show that smartphone-based gaze is significantly impaired with mental fatigue, and tracks the onset and progression of fatigue. A simple model predicts mental fatigue reliably using just a few minutes of gaze data. These results suggest that smartphone-based gaze could provide a scalable, digital biomarker of mental fatigue. 

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