Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Saeed, Umer; Shah, Syed Yaseen; Zahid, Adnan; Ahmad, Jawad; Imran, Muhammad Ali; Shah, Syed Aziz

doi:10.1109/jsen.2021.3096641

Cited by 24 publications

(11 citation statements)

References 42 publications

(41 reference statements)

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“…Various research has focused on creating CSI-based applications such as those for spotting individuals, counting individuals in a crowd, localizing individuals in indoor settings and recognising elderly activity if collapses [42]. Recent studies assert that WiFi transmissions can distinguish between even the tiniest movements of the human body, including those generated by the mouth, the fingertips on a keypad and the heart rate and respiratory rate [43]. Moreover, authors in [44] explored a novel approach towards localization-based activity recognition using CSI and made the dataset publicly available, which inspired our research to conduct analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition

et al. 2023

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This paper presents a study on contactless localization for activity recognition based on radio-frequency sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing and walking. Specifically, SDR device, Universal Software Radio Peripheral (USRP) models X300/X310 are utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labelled based on the region, zone and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score and training time [1], [2]. The results demonstrate that the proposed ensemblebased approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition.

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Section: Literature Reviewmentioning

confidence: 99%

Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition

et al. 2023

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“…In the past, ML-based approaches have been effectively used on a variety of classification problems [52][53][54][55][56]. We used a deep learning-based scheme called ResNet in this work to identify different human activities and detect falling using the generated spectrograms.…”

Section: Residual Neural Network (Resnet) For Classificationmentioning

confidence: 99%

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Saeed

Shah

et al. 2021

Electronics

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Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

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“…The reflection of the wireless signals from the human body is used to assess different actions of humans. As a result, patients such as pregnant women, children, and the elderly will find it easier to be monitored by contactless technology [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

Saeed

Shah

et al. 2022

Sensors

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Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal’s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking.

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Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Cited by 24 publications

References 42 publications

Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition

Software-Defined Radio-Based Contactless Localization for Diverse Human Activity Recognition

Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

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