PROBLEM TO BE SOLVED: To enable drowsiness to be estimated appropriately by following an activity index that changes with time in consideration of a lapse of time based on human physiology.SOLUTION: A drowsiness estimation device includes RRI acquisition means 21 for acquiring RRI data, which is data on an R-R interval, index value calculation means 23 for subjecting the RRI data to statistical processing, and calculating an index value on an activity index concerning an activity of autonomic nerves, drowsiness estimation means 24 for evaluating the index value calculated by the index value calculation means and estimating drowsiness based on a drowsiness estimation rule configured including an estimation function for evaluation by a threshold for each activity index, and threshold function calculation means 26 that, based on index value time series data in which the index values are arranged in a time-series manner, acquires an approximate function close to a change in the index value time series data, obtains a threshold function by giving a change of a predetermined value to the approximate function, and uses the threshold function as the threshold.SELECTED DRAWING: Figure 1COPYRIGHT: (C)2018,JPO&INPIT【課題】人間の生理に基づく時間経過を考慮し、時間変化する活動指標に追従して適切な眠気推定を可能とする。【解決手段】R-R間隔のデータであるRRIデータを取得するRRI取得手段21と、 前記RRIデータを統計処理し、自律神経の活動に関する活動指標について指標値を計算する指標値計算手段23と、各活動指標に関する閾値により評価する推定関数を含んで構成される眠気推定ルールに基づき、前記指標値計算手段により算出された指標値を評価し眠気を推定する眠気推定手段24と、前記指標値を時系列に並べた指標値時系列データに基づき、この指標値時系列データの変化に近似する近似関数を得て、この近似関数に対して所定値の変化を与えた閾値関数を求め、この閾値関数を前記閾値とする閾値関数算出手段26とを具備する。【選択図】図1
