MODEL SELECTION METHOD, MODEL SELECTION APPARATUS AND PROGRAM

To provide a model selection method and a model selection apparatus in which an estimated model being individually matched can efficiently be selected only by measuring an interval of cardiac rhythm pulsation before operation.SOLUTION: A model selection method is configured such that: physical features (RRI) and fatigue degrees (CFF) of a predetermined number of users at normal and active times are acquired, respectively; a physical feature table (13b) storing the acquired physical features of the predetermined number of users, and a fatigue degree estimation table (13d) storing information ( β,,,β) concerning a change (ΔCFF) of the fatigue degrees with a change (ΔHRV) of the acquired physical features of the predetermined number of users are created, respectively; a user having the physical features being close to a physical features (HRV) in the normal time of estimated target users is selected from the created physical feature table (13b); and the fatigue degree (ΔCFF) in the active time of the estimated target users is estimated by using the created fatigue degree estimation table (13d) based on the selected user.SELECTED DRAWING: Figure 6 【課題】運転前の心拍の拍動の間隔の計測のみで、個々に合った推定モデルを効率的に選択することが可能なモデル選択方法およびモデル選択装置を提供すること。【解決手段】所定数のユーザの平常時および活動時での身体特徴(RRI)と疲労度(CFF)とをそれぞれ取得し、前記取得された前記所定数のユーザの身体特徴が記憶された身体特徴テーブル(13b)と、前記取得された前記所定数のユーザの当該身体特徴の変化(ΔHRV)に伴う疲労度の変化(ΔCFF)に関する情報(β1,,,β10)が記憶された疲労度推定テーブル(13d)と、をそれぞれ作成し、前記作成された前記身体特徴テーブル(13b)の中から、推定対象ユーザの前記平常時での身体特徴(HRV)と近い身体特徴を有するユーザを選択し、前記選択された前記ユーザに基づき、前記作成された前記疲労度推定テーブル(13d)を利用して、前記推定対象ユーザの活動時での疲労度(ΔCFF)を推定する。【選択図】図６