Human Activity Classification Based On Breathing Patterns Using IR-UWB Radar

Kumar, Deepak; Sarkar, Amit; Kerketta, Shilpi Ruchi; Ghosh, Debalina

doi:10.1109/indicon47234.2019.9029107

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…The ML‐based breath identification scheme captured four sorts of breath patterns of 20 different humans through Novelda radar chip X4M200 in [39] and preprocessed by short‐time Fourier transform (SIFT), occupied SVM with five‐fold cross‐validation of STFT, they achieved around 99% exactness of classifying vital signs.…”

Section: Related Workmentioning

confidence: 99%

Smart factory floor safety monitoring using UWB sensor

Islam

Lee

Kim

2022

IET Science Measure & Tech

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Chemical asphyxiation at petrochemical factories can provoke the unconsciousness or death of factory workers through suffocation. Some chemicals vaporize and mix with air without showing any warning properties that raise the risk of oxygen deficiency. In light of this, Industry 5.0 focuses more on human‐centricity than technology‐driven implementations to ensure secured and work‐friendly environments in industries. Recently, research on factory safety management dependent on the Internet of things (IoT) sensors have been executed unwaveringly. In this work, the ultra‐wideband (UWB) sensor is adopted to recognize the motion and breathing pattern of workers in smart factory scenarios. After capturing the data from the UWB sensor in real‐time, the proposed dataset is further inspected by the deep learning (DL) and traditional machine learning (ML) approaches. Twofold detection schemes are considered where the movement and vital patterns are distinguished first by the stacked ensemble (SE) and the long short‐term memory (LSTM) frameworks. The Bayesian optimized ensemble learning (EL) and bidirectional (Bi‐LSTM) models are further occupied to analyze abnormalities in the breathing rate of a worker in the smart shop floors. The investigated outcome shows that the DL frameworks (LSTM and Bi‐LSTM) outperformed the others by acquiring 99.90% and 99.94% accuracy in 147 s and 293 s, respectively. The devised perception indicates prominent attainment to the smart factory shop floor, Internet of medical things (IoMT), the smart city paradigm, and e‐health appliances.

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Section: Related Workmentioning

confidence: 99%

Smart factory floor safety monitoring using UWB sensor

Islam

Lee

Kim

2022

IET Science Measure & Tech

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“…Dicho sensor contiene un algoritmo para la detección de la respiración de las personas por medio de radar [22]. Existe en la literatura evidencia suficiente que demuestra la fiabilidad de este sensor para medir la frecuencia respiratoria cercana a grado médico [1,10,12,13].…”

Section: Medición De La Frecuencia De Respiraciónunclassified

An IoMT system for health monitoring in athletes

et al. 2020

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Continuous health monitoring in real-time has become essential to improve people's quality of life through medical prescription or personal control. Our goal is to develop a wearable IoMT device with real-time monitoring of heart rate and breathing patterns while an athlete performs physical exercise at high-intensity intervals. The wearable IoMT device incorporates vital signs sensors to record and display information in a mobile application, allowing users to track their health and receive an alert if the data exceeds normal parameters.

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Detection of Onset and Progression of Osteoporosis Using Machine Learning

Kerketta

Ghosh

2021

Machine Learning for Healthcare Applications

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Human Activity Classification Based On Breathing Patterns Using IR-UWB Radar

Cited by 9 publications

References 9 publications

Smart factory floor safety monitoring using UWB sensor

Smart factory floor safety monitoring using UWB sensor

An IoMT system for health monitoring in athletes

Detection of Onset and Progression of Osteoporosis Using Machine Learning

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