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. 2020 Apr 14;20(8):2216.
doi: 10.3390/s20082216.

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Abdul Rehman Javed  1 , Muhammad Usman Sarwar  2 , Suleman Khan  1 , Celestine Iwendi  3 , Mohit Mittal  4 , Neeraj Kumar  5
Affiliations

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Abdul Rehman Javed et al. Sensors (Basel). .

Abstract

Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

Keywords: accelerometer sensor; activity recognition; smart health; smartphone.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Block diagram of the proposed activity recognition approach.
Figure 2
Figure 2
Illustration of LR classifier.
Figure 3
Figure 3
Structure of MLP classifier.
Figure 4
Figure 4
Recognition rate comparison of j48, LR and MLP classifier concerning each activity.
Figure 5
Figure 5
Comparison of evaluation measures with respect to j48, LR and MLP classifier.
Figure 6
Figure 6
Confusion matrix of activity recognition using j48 classifier.
Figure 7
Figure 7
Confusion matrix of activity recognition using LG classifier.
Figure 8
Figure 8
Confusion matrix of activity recognition using MLP classifier.
Figure 9
Figure 9
Acceleration plots of daily life physical activities.
Figure 10
Figure 10
Recognition rate comparison of different combination of accelerometer axis with respect to each activity using MLP.
Figure 11
Figure 11
Comparison results of the proposed approach with the state-of-the-art research works.
See this image and copyright information in PMC

References

    1. Kivimäki M., Singh-Manoux A., Pentti J., Sabia S., Nyberg S.T., Alfredsson L., Goldberg M., Knutsson A., Koskenvuo M., Koskinen A., et al. Physical inactivity, cardiometabolic disease, and risk of dementia: An individual-participant meta-analysis. BMJ. 2019;365:l1495. doi: 10.1136/bmj.l1495. - DOI - PMC - PubMed
    1. Bao L., Intille S.S. International Conference on Pervasive Computing. Springer; Berlin/Heidelberg, Germany: 2004. Activity recognition from user-annotated acceleration data; pp. 1–17.
    1. Kwapisz J.R., Weiss G.M., Moore S.A. Activity recognition using cell phone accelerometers. ACM SigKDD Explor. Newsl. 2011;12:74–82. doi: 10.1145/1964897.1964918. - DOI
    1. Bulling A., Blanke U., Schiele B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. (CSUR) 2014;46:1–33. doi: 10.1145/2499621. - DOI
    1. Lockhart J. Mobile sensor data mining. Fordham Undergrad. Res. J. 2013;1:11.

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