(Best paper award) A Study on Sensor-based Activity Recognition Having Missing Data

Tahera Hossain, Md Atiqur Rahman Ahad, Hiroki Goto, Sozo Inoue,
International Conference on Informatics, Electronics & Vision (ICIEV) & 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)
(Not Available)
(Not Available)
6 pages
Human activity recognition is an important area for
various applications. Sensor-based activity recognition
deteriorates while partial data are lost. Hence, in this paper,
we study activity recognition in the presence of data loss.
Earlier, we explored sensor-based activity recognition where
we train the data with randomly missed data [1]. It is required
to investigate better features for handling missing data. Here,
we evaluate activity performance result with missing data
environment with various feature combinations for multiple
classifiers. Initially, we developed a simulated environment to
study the impact of features. Afterward, we evaluated our
proposed feature-based method on a benchmark dataset
named HASC dataset. The dataset has no missing data.
However, to evaluate our approach, we added various levels of
missing data randomly and studied the performances. We
explored mean, variance, skewness and kurtosis as statistical
features based on a time-windowing approach. For
classification study, we exploited two classifiers called Naïve
Bayes and Random Forest. Our approach and study
demonstrated satisfactory recognition results under various
feature combinations in different situations of missing data.

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