Passive task-related (TR) fatigue caused by monotonous driving can negatively affect driving safety by impairing driver alertness and performance. This study aims to evaluate the effectiveness of a motion seat system on the driver’s passive TR fatigue in terms of driving performance, physiological response, and subjective fatigue by using automotive and physiological sensors those applicable to on-road driving environment. Twenty drivers (5 females and 15 males; age = 38.5 ± 12.2) with more than two years of driving experience participated in an on-road experiment with two driving conditions: driving in the static seat condition during the first half of the driving session and then in the static (static–static, SS) or motion seat (static–motion, SM) condition during the second half. The SM condition showed significantly lower passive TR fatigue by 4.4~56.5% compared to the SS condition in terms of the standard deviation of velocity, percentage of eyelid closure rate (PERCLOS), and the ratio of low- to high-frequency power (LF/HF) of electrocardiography signals. The drivers rated significantly lower subjective state changes of overall fatigue, mental fatigue, passive TR fatigue, drowsiness, and decreased concentration in the SM condition than those in the SS condition. The findings of the study support the use of a motion seat system can be an effective countermeasure to reduce passive TR fatigue.

Existing swallowing evaluation methods using X-ray or endoscopy are qualitative. The present study develops a swallowing monitoring and assessment system (SMAS) that is nonintrusive and quantitative. The SMAS comprises an ultrasonic Doppler sensor array, a microphone, and an inertial measurement unit to measure ultrasound signals originating only from swallowing activities. Ultrasound measurements were collected for combinations of two viscosity conditions (water and yogurt) and two volume conditions (3 mL and 9 mL) from 24 healthy participants (14 males and 10 females; age = 30.5 ± 7.6 years) with no history of swallowing disorders and were quantified for 1st peak amplitude, 2nd peak amplitude, peak-to-peak (PP) time interval, duration, energy, and proportion of two or more peaks. The peak amplitudes and energy significantly decreased by viscosity and the PP time interval and duration increased by volume. The correlation between the time measures were higher (r = 0.78) than that of the amplitude measures (r = 0.30), and the energy highly correlated with the 1st peak amplitude (r = 0.86). The proportion of two or more peaks varied from 76.8% to 87.9% by viscosity and volume. Further research is needed to examine the concurrent validity and generalizability of the ultrasonic Doppler sensor-based SMAS.

Uncomfortable smartphone grip postures resulting from inappropriate user interface design can degrade smartphone usability. This study aims to develop a classification model for smartphone grip postures by detecting the positions of the hand and fingers on smartphones using machine learning techniques. Seventy participants (35 males and 35 females with an average of 38.5 ± 12.2 years) with varying hand sizes participated in the smartphone grip posture experiment. The participants performed four tasks (making calls, listening to music, sending text messages, and web browsing) using nine smartphone mock-ups of different sizes, while cameras positioned above and below their hands recorded their usage. A total of 3278 grip posture images were extracted from the recorded videos and were preprocessed using a skin color and hand contour detection model. The grip postures were categorized into seven types, and three models (MobileNetV2, Inception V3, and ResNet-50), along with an ensemble model, were used for classification. The ensemble-based classification model achieved an accuracy of 95.9%, demonstrating higher accuracy than the individual models: MobileNetV2 (90.6%), ResNet-50 (94.2%), and Inception V3 (85.9%). The classification model developed in this study can efficiently analyze grip postures, thereby improving usability in the development of smartphones and other electronic devices.