Supervised and Neural Classifiers for Locomotion Analysis

Reviewed
Swapnil Sayan Saha, Shafizur Rahman, Tahera Hossain, Sozo Inoue, Md Atiqur Rahman Ahad,
Ubicomp Workshop on Human Activity Sensing Corpus and Applications (HASCA)
(Not Available)
(Not Available)
1563-1570
2018-10-12
Singapore
https://dl.acm.org/citation.cfm?id=3267524
The goal of the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge is to classify 8 modes of transportation and locomotion activities recorded using a smartphone inertial sensor. In this paper, Team Orion extracts 36 quantitative features per sensor (totalling 226 features) of the dataset provided in the SHL recognition challenge and provides a processing pipeline for training the classifiers embracing parallel computation and out-of-memory processing. One-vs-one quadratic Support Vector Machine (1-1 QSVM) and Bagged Decision Trees with 45 learners (EoC-45) were used for classification of the DU Mobility Dataset. The same features/pipeline provided 91% and 92% classification accuracies respectively on the SHL recognition challenge dataset. Using one-vs-one cubic Support Vector Machine (1-1 CSVM) on SHL, we received the highest classification accuracy of 92.8%. Afterwards, Artificial Neural Network (ANN) was applied on the dataset and accuracies of 93.6%, 88.18% and 90.05% were obtained for training, testing and validation phases. The results provide promising prospects of supervised and neural network classification for locomotion analysis.

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