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Wearable Sensors-Based Human Activity Recognition with Deep Convolutional Neural Network and Fuzzy Classification

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Abstract

The elderly and disabled often live without direct supervision and rely on remote monitoring for their care. However, current methods for information processing and activity recognition in healthcare decision-making have limitations. This paper proposes a professional solution by introducing a Deep Convolutional Neural Network (DeepCNN) and Fuzzy Support Vector Machine (FSVM) to extract significant features and label activities using a Fuzzy Membership Function (FMF). By utilizing FSVM with FMF, the problem of assigning instances to the correct class is solved, resulting in more accurate classification and a flexible system.The proposed method surpasses previous approaches on DLAs and UCI datasets, achieving an accuracy of 97.22% for DeepCNN-FSVM and 96.81% for FSVM on the UCI dataset. This research emphasizes the importance of activity recognition in smart healthcare for the elderly. Recent efforts by researchers have focused on develo** high-performance methods in activity recognition to overcome challenges faced by previous approaches. Overall, this paper highlights the potential of the DeepCNN-FSVM approach in improving activity recognition in smart healthcare for the elderly and disabled.

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Data Availability and Materials

The DLAs and UCI datasets used in this study are publicly available.

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  1. Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Fatemeh Serpush, Behrooz Masoumi & Babak Karasfi

  2. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Bagher Menhaj

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Serpush, F., Menhaj, M.B., Masoumi, B. et al. Wearable Sensors-Based Human Activity Recognition with Deep Convolutional Neural Network and Fuzzy Classification. Wireless Pers Commun 133, 889–911 (2023). https://doi.org/10.1007/s11277-023-10797-3

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