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Artificial neural networks for human activity recognition using sensor based dataset

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Abstract

The use of wearable devices, sensors, machine learning, and deep learning for human activity recognition (HAR) applications has increased in recent years. Lots of researchers introduce different methods and techniques for HAR but accuracy and efficiency are still a gap to work in the HAR domain for other researchers. In this study, we design a simple architecture of MLP with a stack of dense layers which can perform accurately and efficiently on small and large features set for HAR. This study used a dataset that contain smartphone sensors (gyroscope and accelerometer) data against six daily human activities. Each sensor has three feature values corresponding to an instance for an activity. First, we train our proposed model individually on gyroscope and accelerometer data and then we combine both sensors data to train the model. We train the model using 70% of the dataset and evaluate the performance of the model on 30% data. MLP outperforms all other stat of the art models in comparison such as random forest, decision tree, logistic regression, and K nearest neighbor. The MLP accuracy scores are 0.74, 0.77, and 0.98 using features of gyroscope, accelerometer, and combination of both respectively. The results show that the proposed approach is also good with single sensor data. In comparison with the proposed MLP, we also deployed state-of-the-art models such as LSTM, CNN, and other machine learning models. Proposed MLP outperforms all used models in terms of all evaluation parameters. We also did a statistical T-test to show the significance of the proposed approach.

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Authors and Affiliations

  1. University of California Riverside, Riverside, CA, 92521, USA

    Shahab Geravesh

  2. School of Computing and Information Sciences, Florida International University, 11200 SW 8th St ECS 354, Miami, FL, 33199, USA

    Vaibhav Rupapara

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  1. Shahab Geravesh
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Correspondence to Vaibhav Rupapara.

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Geravesh, S., Rupapara, V. Artificial neural networks for human activity recognition using sensor based dataset. Multimed Tools Appl 82, 14815–14835 (2023). https://doi.org/10.1007/s11042-022-13716-z

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  • DOI: https://doi.org/10.1007/s11042-022-13716-z

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