Mental fatigue, characterized by subjective feelings of "tiredness" and "lack of energy", can degrade individual performance in a variety of situations, for example, in motor vehicle driving or while performing surgery. Thus, a method for nonintrusive monitoring of mental fatigue status is urgently needed. Recent research shows that physiological signal-based fatigueclassification methods using wearable electronics can be sufficiently accurate; by contrast, rigid, bulky devices constrain the behavior of those wearing them, potentially interfering with test signals. Recently, wearable electronics, such as epidermal electronics systems (EES) and electronic tattoos (E-tattoos), have been developed to meet the requirements for the comfortable measurement of various physiological signals. However, comfortable, effective, and nonintrusive monitoring of mental fatigue levels remains to be fulfilled. In this work, an EES is established to simultaneously detect multiple physiological signals in a comfortable and nonintrusive way. Machine-learning algorithms are employed to determine the mental fatigue levels and a predictive accuracy of up to 89% is achieved based on six different kinds of physiological features using decision tree algorithms. Furthermore, EES with the trained predictive model are applied to monitor in situ human mental fatigue levels when doing several routine research jobs, as well as the effect of relaxation methods in relieving fatigue.

Highly sensitive strain sensors that can detect small strain are in high demand in the fields of displays, robotics, fatigue detection, body monitoring, in vitro diagnostics, and advanced therapies. However, resistive-type sensors that are composed of electrically conductive sensing films coupled with flexible substrates suffer from the limits that their gauge factors (GFs) at small strains (e.g., 0.1-1%) are not high. Herein, through frictional direct-writing of graphite rod on the composite paper substrates, we produced strain sensors with extremely high gauge factor at small strains. The sensors exhibited a gauge factor of 9720 at a small strain of 0.9%, minimum strain detection up to 0.05%, strain resolution of 0.05%, response time of 40 ms, and high stability (>5000 bending-unbending cycles). Compared with the literature results so far, our sensors hold the highest GF value at small strains. Such high sensitivities are due to the precise control of narrow two-dimensional percolative conductive pathway, which means the content of conductive graphite sheets is close to the conductive percolation threshold. The strain sensors have a rapid response to microdeformation changes and can monitor various structural changes, including human motion, through facilitative and effective installation of device designs.

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