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 2 publications

(1 citation statement)

References 56 publications

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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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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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Enhanced Aiot Multi‐Modal Fusion for Human Activity Recognition in Ambient Assisted Living Environment

Patel,

Jhaveri,

Patel

et al. 2024

Softw Pract Exp

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MethodologyHuman activity recognition (HAR) has emerged as a fundamental capability in various disciplines, including ambient assisted living, healthcare, human‐computer interaction, etc. This study proposes a novel approach for activity recognition by integrating IoT technologies with Artificial Intelligence and Edge Computing. This work presents a fusion HAR approach that combines data readings from wearable sensors such as accelerometer and gyroscope sensors and Images captured by vision‐based sensors such as cameras incorporating the capabilities of Long Short‐Term Memory (LSTM) and Convolutional Neural Network (CNN) models. The aim of fusing these models is to capture and extract the temporal and spatial information, improving the accuracy and resilience of activity identification systems. The work uses the CNN model to find spatial features from the images that represent the contextual information of the activities and the LSTM model for processing sequential accelerometer and gyroscope sensor data to extract the temporal dynamics from the human activities.ResultsThe performance of our fusion approach is evaluated through different experiments using varying parameters and applies the best‐suited parameters for our model. The results demonstrate that the fusion of LSTM and CNN models outperforms standalone models and traditional fusion methods, achieving an accuracy of 98%, which is almost 9% higher than standalone models.ConclusionThe fusion of LSTM and CNN models enables the integration of complementary information from both data sources, leading to improved performance. The computation tasks are performed at the local edge device resulting to enhanced privacy and reduced latency. Our approach greatly impacts real‐world applications where accurate and reliable HAR systems are essential for enhancing human‐machine interaction and monitoring human activities in various domains.

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IoT-Based Human Activity Recognition for Smart Living

Cited by 2 publications

References 56 publications

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

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

Enhanced Aiot Multi‐Modal Fusion for Human Activity Recognition in Ambient Assisted Living Environment

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