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

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 activit...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-04, Vol.20 (8), p.2216
Main Authors: Javed, Abdul Rehman, Sarwar, Muhammad Usman, Khan, Suleman, Iwendi, Celestine, Mittal, Mohit, Kumar, Neeraj
Format: Article
Language:English
Subjects:
accelerometer sensor
Accelerometers
Accelerometry - instrumentation
Accelerometry - methods
Activity recognition
Datasets
Humans
Logistic Models
Machine Learning
Motor Activity
Multilayer perceptrons
Neural networks
Physical fitness
Reliability analysis
Running
Sensors
Sitting Position
smart health
Smartphone
Smartphones
Walking
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20082216