Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Shahverdi, Hossein; Nabati, Mohammad; Moshiri, Parisa Fard; Asvadi, Reza; Ghorashi, Seyed Ali

doi:10.3390/info14070404

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…HAR 21 : In this study, the author used CNN to perform edge detection on CSI data, enhancing human activity recognition based on WiFi.…”

Section: Experiments and Analysismentioning

confidence: 99%

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WiFi-based non-contact human presence detection technology

Zhang,

Wang,

Wen

et al. 2024

Sci Rep

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In the swiftly evolving landscape of Internet of Things (IoT) technology, the demand for adaptive non-contact sensing has seen a considerable surge. Traditional human perception technologies, such as vision-based approaches, often grapple with problems including lack of sensor versatility and sub-optimal accuracy. To address these issues, this paper introduces a novel, non-contact method for human presence perception, relying on WiFi. This innovative approach involves a sequential process, beginning with the pre-processing of collected Channel State Information (CSI), followed by feature extraction, and finally, classification. By establishing signal models that correspond to varying states, this method enables the accurate perception and recognition of human presence. Remarkably, this technique exhibits a high level of precision, with sensing accuracy reaching up to 99$$\%$$ % . The potential applications of this approach are extensive, proving to be particularly beneficial in contexts such as smart homes and healthcare, amongst various other everyday scenarios. This underscores the significant role this novel method could play in enhancing the sophistication and effectiveness of human presence detection and recognition systems in the IoT era.

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“…HAR 21 : In this study, the author used CNN to perform edge detection on CSI data, enhancing human activity recognition based on WiFi.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Overall performance evaluation : To effectively assess its overall performance, the method was compared with FreeSense 17 , Wi-alarm 18 , and HAR 21 . As shown in Figs.…”

Section: Experiments and Analysismentioning

confidence: 99%

WiFi-based non-contact human presence detection technology

Zhang,

Wang,

Wen

et al. 2024

Sci Rep

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“…For example, sharp edges can be better understood by the use of the Sobel filter, which is comprised of operators that apply gradients [21]. Two convolution layers, one of which is a modified counterpart of the other, make up a Sobel edge detector.…”

Section: Pre-processing Using Sobel and Canny Filter Sobel Filtermentioning

confidence: 99%

Development of Image Processing and AI Model for Drone Based Environmental Monitoring System

Anitha,

Devi,

Nassa

et al. 2024

JMC

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Data from environmental monitoring can be used to identify possible risks or adjustments to ecological patterns. Early detection reduces risks and lessens the effects on the environment and public health by allowing for prompt responses to ecological imbalances, pollution incidents, and natural disasters. Decision-making and analysis can be done in real time when Artificial Intelligence (AI) is integrated with Unmanned Aerial Vehicles (UAV) technology. With the help of these technologies, environmental monitoring is made possible with a more complete and effective set of tools for assessment, analysis, and reaction to changing environmental conditions. Multiple studies have shown that forest fires in India have been happening more often recently. Lightning, extremely hot weather, and dry conditions are the three main elements that might spontaneously ignite a forest fire. Both natural and man-made ecosystems are affected by forest fires. Forest fire photos are pre-processed using the Sobel and Canny filter. A Convolutional Neural Network (CNN)–based Forest Fire Image Classification Network (DFNet) using the publicly accessible Kaggle dataset is proposed in this study. The suggested DFNet classifier's hyperparameters are fine-tuned with the help of Spotted Hyena Optimizer (SHO). With a performance level of 99.4 percent, the suggested DFNet model outperformed the state-of-the-art models, providing substantial backing for environmental monitoring.

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“…Ref. [64] looked at volunteers who performed activities within a duration of 10-20 s; therefore, no specific time was set for this and the experiment was a bit more relaxed. Moshiri et al [53] experimented with 3 volunteers and asked them to perform 7 different activities 20 times, so no time was set on this, it was a case of performing it 20 times.…”

Section: Experiments Setupmentioning

confidence: 99%

Classifying Participant Standing and Sitting Postures Using Channel State Information

Custance,

Khan,

Parkinson

2023

Electronics

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Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand.

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Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Cited by 7 publications

References 23 publications

WiFi-based non-contact human presence detection technology

WiFi-based non-contact human presence detection technology

Development of Image Processing and AI Model for Drone Based Environmental Monitoring System

Classifying Participant Standing and Sitting Postures Using Channel State Information

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