Walking-posture Classification from Single-acceleration-sensor Data using Deep Learning

概要: 本研究では腰付近に装着した単一加速度センサで日常歩行動作を監視するために,深層学習を導入した歩行姿勢分類方法を検討した.深層学習を適用した方法はSVMを用いた従来方法と比較し,高い分類精度が得られ,特に畳み込み層とLSTM層を組合わせたネットワーク構成が0.98と最も高い結果であった.

Abstract:    We described a walking-posture classification method from a single accelerator attached to a human waist using a deep learning technique. We considered deep learning architectures for a single accelerator based on previous human activity recognition studies and investigated the classification accuracy of the proposed method using the walking-posture dataset. The results demonstrate that a deep learning approach to walking-posture classification using a single accelerator is more useful than the conventional SVM approach. Additionally, we also confirmed that a hybrid network architecture with three convolutional neural layers, two pooling layers between the convolutional layers, and a long short-term memory layer achieved the best accuracy of 0.9803 compared to other network architectures. We also confirmed the deep learning approach yielded more correct classification for each walking-posture category in spite of the difficulty to detect the classification by the SVM approach.

 

研究成果:

  • 新川怜奈,羽山徹彩: “深層学習を用いた単一加速度センサからの歩行姿勢分類の一考察”, 電子情報通信学会論文誌, 研究速報, 2020.
  • 新川怜奈,   羽山徹彩: “深層学習を用いた単一加速度データからの歩行姿勢の分類”, 第82回情報処理学会全国大会講演論文集, (2020).