Software Defined Radio Based Testbed for Large Scale Body Movements

Ashleibta, Aboajeila Milad; Zahid, Adnan; Shah, Syed Aziz

doi:10.1109/ieeeconf35879.2020.9330027

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…This work will use 64 subcarriers. Orthogonal frequency division multiplexing (OFDM) is used for 64 points of fast Fourier transformer (FFT) producing 64 frequency carriers (subcarriers) [26]. Lower frequencies are able to detect the smaller movements while higher frequencies are able to detect larger movements [27].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Taylor

Shah

Dashtipour

et al. 2020

Sensors

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116

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Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

Taylor

Shah

Dashtipour

et al. 2020

Sensors

Self Cite

116

View full text Add to dashboard Cite

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“…The SDR-based approach is a specifically built hardware that can be employed to sense several human activities [ 33 , 34 ]. WiSee utilises USRP to detect the Doppler shifts in wireless signals and accomplish activity identification with an accuracy of up to 94% [ 35 ].…”

Section: Related Workmentioning

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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“…However, in indoor situations, the location services are not precise due to weak signals and low penetration [19]. To accomplish indoor localization, researchers have suggested some well-known technologies like radio-frequency identification (RFID) [20], UWB [21], Bluetooth [22], Wi-Fi [23], light [24] and audio [25]. Since many households already have Wi-Fi infrastructures in place, this article uses RF-based Wi-Fi sensing to avoid the need to incorporate extra sensing technologies.…”

Section: Introductionmentioning

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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Software Defined Radio Based Testbed for Large Scale Body Movements

Cited by 7 publications

References 6 publications

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare

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

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

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