CNN-based device-free health monitoring and prediction system using WiFi signals

Kumar, Amit; Singh, Shashwat; Rawal, Vibhor; Garg, Shivam; Agrawal, Anand; Yadav, Manisha

doi:10.1007/s41870-022-01023-7

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…Unlike other studies (Al Rasyid et al, 2015;David Chung Hu et al, 2018;Ali et al, 2019;Xiao et al, 2020), this research investigates the feasibility of utilizing contactless smart home technologies alone, without the need for additional wearable devices, making it more practical for daily living contexts. Even when compared to studies that also monitor heart rate in a contactless manner (Adib et al, 2015;Kumar et al, 2022;Liu et al, 2022;Alnaggar et al, 2023), this study demonstrates distinct advantages. Firstly, it is not limited by large body movements since it does not rely on detecting chest or other physiological motions.…”

Section: Public Health Perspectivementioning

confidence: 68%

“…However, the performance of this method is limited by large body movements, as it primarily relies on detecting chest motion. In studies, Kumar et al (2022) and (Liu et al (2022), the authors utilized Wi-Fi technology to monitor vital signs by detecting physiological movements that affect channel state information. However, this technology also has limitations in accurately detecting high heart rates or breathing rates during periods of excessive movement.…”

Section: Related Workmentioning

confidence: 99%

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Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults

Wang,

Cao,

Kaur

et al. 2024

Front. Artif. Intell.

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BackgroundAs global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults.MethodsThe study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron.ResultsAll machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals' daily living activities and their corresponding heart rate responses, with the highest R2 value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance.ConclusionsThe utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals' daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual's cardiovascular health profile. This valuable information can inform the implementation of personalized interventions, preventive measures, and lifestyle modifications to mitigate the risk of cardiovascular diseases and improve overall health outcomes.

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Section: Public Health Perspectivementioning

confidence: 68%

Section: Related Workmentioning

confidence: 99%

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults

Wang,

Cao,

Kaur

et al. 2024

Front. Artif. Intell.

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“…The temporal or static features are then used to train supervised classifiers. State-of-the-art Wi-Fi sensing systems utilise Deep Neural Networks (DNN) [16,32] or Convolutional Neural Networks (CNN) [33,34] to learn non-obvious features; however, instance-based classifiers such as Support Vector Machines (SVM) or K-Nearest-Neighbour (KNN) offer comparable accuracy at much better computational complexity [19,30]. Transfer Learning (TL) has also been applied, as it allows trained networks to be adaptable to deployment in new environments [35][36][37].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Sharma,

Li,

Mishra

et al. 2024

Energies

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Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

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Optimized intelligent framework for peak-to-average power ratio reduction in filter bank multicarrier

Kataksham,

Siddaiah

2023

Int. j. inf. tecnol.

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CNN-based device-free health monitoring and prediction system using WiFi signals

Cited by 7 publications

References 40 publications

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults

Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Optimized intelligent framework for peak-to-average power ratio reduction in filter bank multicarrier

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