IoT-Based Human Activity Recognition for Smart Living

Saha, Anindita; Roy, Moumita; Chowdhury, Chandreyee

doi:10.1007/978-3-031-26685-0_5

Cited by 1 publication

(1 citation statement)

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“…Wearable sensor-based human activity recognition (HAR) plays a significant role in various applications, including sports [1][2][3], smart homes [4][5][6], and purpose-specific monitoring systems [7][8][9]. By extracting information from different sensor signals, such as accelerometers and gyroscopes, a HAR system can accurately recognize various activities, such as running, walking, and sitting.…”

Section: Introductionmentioning

confidence: 99%

Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors

Vuong,

Doan,

Takasu

2023

Sensors

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Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals. In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain and improve accuracy for multimodal HAR. DWCNN introduces a framework that combines continuous wavelet transforms (CWT) with enhanced deep convolutional neural networks (DCNN) to capture the dependencies of sensing signals in the time-frequency domain, thereby enhancing the feature representation ability for multiple wearable inertial sensor-based HAR tasks. Within the CWT, we further propose an algorithm to estimate the wavelet scale parameter. This helps enhance the performance of CWT when computing the time-frequency representation of the input signals. The output of the CWT then serves as input for the proposed DCNN, which consists of residual blocks for extracting features from different modalities and attention blocks for fusing these features of multimodal signals. We conducted extensive experiments on five benchmark HAR datasets: WISDM, UCI-HAR, Heterogeneous, PAMAP2, and UniMiB SHAR. The experimental results demonstrate the superior performance of the proposed model over existing competitors.

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