This study aims to build a system for detecting a driver’s internal state using body-worn sensors. Our system is intended to detect inattentive driving that occurs during long-term driving on a monotonous road, such as a high-way road. The inattentive state of a driver in this study is an absent-minded state caused by a decrease in driver vigilance levels due to fatigue or drowsiness. However, it is difficult to clearly define these inattentive states because it is difficult for the driver to recognize when they fall into an absent-minded state. To address this problem and achieve our goal, we have proposed a detection algorithm for inattentive driving that not only uses a heart rate sensor, but also uses body-worn inertial sensors, which have the potential to detect driver behavior more accurately and at a much lower cost. The proposed method combines three detection models: body movement, drowsiness, and inattention detection, based on an anomaly detection algorithm. Furthermore, we have verified the accuracy of the algorithm with the experimental data for five participants that were measured in long-term and monotonous driving scenarios by using a driving simulator. The results indicate that our approach can detect both the inattentive and drowsiness states of drivers using signals from both the heart rate sensor and accelerometers placed on wrists.

ドライバ起因の事故を防止するために，運転への意識低下を招く行動や状態の発生をモニタリングし，潜在的な事故危険性を把握することが重要である． この課題に対して，著者らは手首装着型センサを用いたドライバ状態推定手法の開発に取り組んでいる． これまでの検証は，実験室環境下のみで行われていた．しかし，車両振動や自然な状況下でのドライバの行動の多様性を検証するために，実際の運転環境下でのデータ収集が不可欠である．そこで本研究では，実環境下で運転行動データ収集を行うために手首装着型センサを利用した車載計測システムを開発した．開発した計測システムは，ユーザ自身が簡易な操作のみで利用でき，手先加速度のほか，車両の加速度・位置，操舵角，映像の各データを同期して最大約120分の収録ができる．開発した計測システムの基礎検証として，測定した各種データの同期精度と操舵角の推定精度について評価を行い，開発した計測システムの有効性を検討した．

In recent years, deep learning that can learn features from a dataset has been remarkably developing in the field of face recognition and voice recognition and so on. However, it is difficult to pursue cause of misjudgment result because input-output relation of deep learning is a black box. Furthermore, the content has yet to be elucidated what the judgment is based on. Therefore, when introducing deep Learning into multiple fields, it is important to understand the reason. This study aims to pursue cause of misjudgment result by intervening in the preprocessing part of deep learning using 2-dimensional discrete wavelet packet transform.

This paper presents a millimeter-scale piezoelectric effect-based force sensor that uses the change in its resonant frequency as the detection principle for high sensitivity and a wide measurement range. Such characteristics are suited for robot hand applications that can not only detect a small force but also handle large payloads. In this paper, we develop a methodology to estimate the relationship between the applied force and the resonant frequency shift by combining classic contact theory and the finite element method (FEM) analysis. Although this relationship is non-linear, the designability of the sensitivity and measurement range is demonstrated by the simulation. The simulation results based on the method are verified, showing good agreement with experimental results. The static characteristics, including sensitivity, standard deviation, and resolution, are evaluated using the prototype sensors with characteristic lengths ranging from 1mmto 4 mm. The 4-mm model has a measurement range of 77 mN to 300 N, and the smallest model, which is one of the smallest force sensors suitable for practical implementation, has a measurement range of 9 mN to 20 N.