@article{oai:ipsj.ixsq.nii.ac.jp:00147419,
 author = {梶, 克彦 and 河口, 信夫 and Katsuhiko, Kaji and Nobuo, Kawaguchi},
 issue = {1},
 journal = {情報処理学会論文誌},
 month = {Jan},
 note = {本稿では歩行センシングデータから高精度に3次元歩行軌跡を推定する手法を提案する．使用するセンサは加速度・角速度・気圧の3種類であり，詳細な建物構造情報を必要としない．装着型センサを用いた行動推定において，センサ信号の変化が少ない状態が継続している区間の検出は，センサ信号が短時間に大きく変化する区間を直接検出するよりも高い精度を期待できる．本稿ではこのような区間を安定センシング区間と定義し，そのコンセプトの適用例として，角速度センサを用いた安定歩行区間検出に基づく進行方向推定と，気圧センサを用いた安定フロア区間検出に基づくフロア間移動推定を行う．さらにそれら推定情報を統合して3次元歩行軌跡を求める．屋内歩行センシングコーパスHASC-IPSCを用いた評価実験の結果，進行方向推定，フロア間移動推定ともに，大きな変化量を直接推定する手法よりも高い精度を達成できた．最終的な3次元歩行軌跡推定の誤差蓄積速度は，10秒間の移動の間に1mの誤差が蓄積する程度であることが確認された．, A highly accurate estimation method of 3-D pedestrian trajectories from walking activity sensing data is proposed. This method uses data from an accelerometer, a gyrometer, and an air pressure sensor, and does not require detailed information on the building structure. In activity sensing using wearable sensors, higher accuracy can be expected from detection of zones in which there is continuously little change in the states of the sensor signals than from detection of zones in which there are large changes in the sensor signals within a short time. We focus on such stability of sensing signal and, as an application example, we used the concept to estimate walking direction on the basis of stable walking zone detection using a gyrometer and estimation of movement between the floors of a building by detection of stable floor zones with an air pressure sensor. We then integrated these estimations to obtain a 3-D pedestrian trajectory. The results of evaluation experiments using an indoor pedestrian sensing corpus (HASC-IPSC) showed that this method achieved higher accuracy for both walking direction and movement between floors than was achieved by a method based on large changes in the sensor signals. We also confirmed that the cumulative error rate for estimation of the 3-D pedestrian trajectory was 1m per 10 seconds of movement.},
 pages = {12--24},
 title = {安定センシング区間検出に基づく3次元歩行軌跡推定手法},
 volume = {57},
 year = {2016}
}