A Study of Real-Time Physical Activity Recognition from Motion Sensors via Smartphone Using Deep Neural Network

Uddin, Mohammad Helal; Ara, Jannat Mir Kanon; Rahman, Md Hafizur; Yang, Shan

doi:10.1109/eict54103.2021.9733607

Cited by 6 publications

(8 citation statements)

References 14 publications

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“…HAR is currently a vital and popular research area due to its numerous applications in various fields such as health monitoring, analysis of sports events [1], entertainment events [2], home care for the aging person [3], etc. The literature reveals [4][5][6] that computer vision and inertial sensor-based techniques are commonly employed for HAR. However, both of these methods have their own limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Wearable or inertial sensor-based techniques always force the users to wear a variety of tracking devices, which are bothersome and inconvenient for the user [7]. WiFi-HAR methods [4,8] have emerged as a solution because of their ability to overcome the aforementioned limitations. Advantages include (i) low cost, (ii) no privacy violation, (iii) compact size, (iv) contactless, and (v) hardware facilities are universal.…”

Section: Introductionmentioning

confidence: 99%

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HHI-AttentionNet: An Enhanced Human-Human Interaction Recognition Method Based on a Lightweight Deep Learning Model with Attention Network from CSI

Shafiqul

Jannat

Kim

et al. 2022

Sensors

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Nowadays WiFi based human activity recognition (WiFi-HAR) has gained much attraction in an indoor environment due to its various benefits, including privacy and security, device free sensing, and cost-effectiveness. Recognition of human-human interactions (HHIs) using channel state information (CSI) signals is still challenging. Although some deep learning (DL) based architectures have been proposed in this regard, most of them suffer from limited recognition accuracy and are unable to support low computation resource devices due to having a large number of model parameters. To address these issues, we propose a dynamic method using a lightweight DL model (HHI-AttentionNet) to automatically recognize HHIs, which significantly reduces the parameters with increased recognition accuracy. In addition, we present an Antenna-Frame-Subcarrier Attention Mechanism (AFSAM) in our model that enhances the representational capability to recognize HHIs correctly. As a result, the HHI-AttentionNet model focuses on the most significant features, ignoring the irrelevant features, and reduces the impact of the complexity on the CSI signal. We evaluated the performance of the proposed HHI-AttentionNet model on a publicly available CSI-based HHI dataset collected from 40 individual pairs of subjects who performed 13 different HHIs. Its performance is also compared with other existing methods. These proved that the HHI-AttentionNet is the best model providing an average accuracy, F1 score, Cohen’s Kappa, and Matthews correlation coefficient of 95.47%, 95.45%, 0.951%, and 0.950%, respectively, for recognition of 13 HHIs. It outperforms the best existing model’s accuracy by more than 4%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HHI-AttentionNet: An Enhanced Human-Human Interaction Recognition Method Based on a Lightweight Deep Learning Model with Attention Network from CSI

Shafiqul

Jannat

Kim

et al. 2022

Sensors

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“…Recently, research on HAR has drawn more academic and commercial interest because of its numerous applications in different fields, such as smart home [ 1 ] (managing different appliances based on human actions for minimizing energy consumption), healthcare [ 2 ] (monitoring the health of ageing citizens and disabled people), security [ 3 ] (bio-metric or face recognition), and video gaming, etc. Traditional HAR approaches use several sensing technologies: computer vision [ 4 ], wearable sensors [ 5 ], and radars [ 6 ]. Computer vision is a salient sensing technique used to collect information for humans from the surrounding environment and achieve satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI

Islam

Jannat

Hossain

et al. 2022

Sensors

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Human activity recognition (HAR) has emerged as a significant area of research due to its numerous possible applications, including ambient assisted living, healthcare, abnormal behaviour detection, etc. Recently, HAR using WiFi channel state information (CSI) has become a predominant and unique approach in indoor environments compared to others (i.e., sensor and vision) due to its privacy-preserving qualities, thereby eliminating the need to carry additional devices and providing flexibility of capture motions in both line-of-sight (LOS) and non-line-of-sight (NLOS) settings. Existing deep learning (DL)-based HAR approaches usually extract either temporal or spatial features and lack adequate means to integrate and utilize the two simultaneously, making it challenging to recognize different activities accurately. Motivated by this, we propose a novel DL-based model named spatio-temporal convolution with nested long short-term memory (STC-NLSTMNet), with the ability to extract spatial and temporal features concurrently and automatically recognize human activity with very high accuracy. The proposed STC-NLSTMNet model is mainly comprised of depthwise separable convolution (DS-Conv) blocks, feature attention module (FAM) and NLSTM. The DS-Conv blocks extract the spatial features from the CSI signal and add feature attention modules (FAM) to draw attention to the most essential features. These robust features are fed into NLSTM as inputs to explore the hidden intrinsic temporal features in CSI signals. The proposed STC-NLSTMNet model is evaluated using two publicly available datasets: Multi-environment and StanWiFi. The experimental results revealed that the STC-NLSTMNet model achieved activity recognition accuracies of 98.20% and 99.88% on Multi-environment and StanWiFi datasets, respectively. Its activity recognition performance is also compared with other existing approaches and our proposed STC-NLSTMNet model significantly improves the activity recognition accuracies by 4% and 1.88%, respectively, compared to the best existing method.

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“…The recognition of human activity (HAR) in an indoor environment using Wi-Fi applications has emerged as a significant and widely researched field [2]. Literature review [3,4,5] indicates that camera-based and sensor-based HAR systems are widely utilized in addressing this issue, however, these approaches are often associated with high costs or the requirement of wearable devices, which can be Chia-Yu Lin is with the department of Computer Science and Information Engineering from National Central University, Taoyuan, Taiwan. (e-mail: linjiayou6@gmail.com)…”

Section: Introductionmentioning

confidence: 99%

“…(e-mail: timothykshih@gmail.com) uncomfortable and inconvenient for users [6]. Given that Wi-Fi has the potential to overcome these limitations, it is being considered as a promising alternative solution for HAR [1,3,7]. Although Wi-Fi has its advantages in indoor environments, it still faces certain challenges such as limited coverage and difficulties in handling multi-subject scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Mobile Temporal Convolution Network for Multi-Location Human Activity Recognition based on Wi-Fi

Jiang

et al. 2021

2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops)

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The utilization of Wi-Fi-based human activity recognition (HAR) has gained considerable interest in recent times, primarily owing to its applications in various domains such as healthcare for monitoring breath and heart rate, security, elderly care, and others. These Wi-Fi-based methods exhibit several advantages over conventional state-of-the-art techniques that rely on cameras and sensors, including lower costs and ease of deployment. However, a significant challenge associated with Wi-Fi-based HAR is the significant decline in performance when the scene or subject changes. To mitigate this issue, it is imperative to train the model using an extensive dataset. In recent studies, the utilization of CNN-based models or sequenceto-sequence models such as LSTM, GRU, or Transformer has become prevalent. While sequence-to-sequence models can be more precise, they are also more computationally intensive and require a larger amount of training data. To tackle these limitations, we propose a novel approach that leverages a temporal convolution network with augmentations and attention, referred to as TCN-AA. Our proposed method is computationally efficient and exhibits improved accuracy even when the data size is increased threefold through our augmentation techniques. Our experiments on a publicly available dataset indicate that our approach outperforms existing state-of-the-art methods, with a final accuracy of 99.42%.

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A Study of Real-Time Physical Activity Recognition from Motion Sensors via Smartphone Using Deep Neural Network

Cited by 6 publications

References 14 publications

HHI-AttentionNet: An Enhanced Human-Human Interaction Recognition Method Based on a Lightweight Deep Learning Model with Attention Network from CSI

HHI-AttentionNet: An Enhanced Human-Human Interaction Recognition Method Based on a Lightweight Deep Learning Model with Attention Network from CSI

STC-NLSTMNet: An Improved Human Activity Recognition Method Using Convolutional Neural Network with NLSTM from WiFi CSI

A Lightweight Mobile Temporal Convolution Network for Multi-Location Human Activity Recognition based on Wi-Fi

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