Monitoring System for Prisoner with GPS using Wireless Sensor Network (WSN)

Patil, Pooja B.; Chapalkar, Sneha; Dhamne, Neha D.; Patel, Niharika M.

doi:10.5120/15943-5235

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Video cameras, as an alternative choice, have been used for contact-less monitoring of vital signs. However, cameras invade privacy and their performance is highly affected by the amount of light in the monitored space [4, 5] and pose of the subjects.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Breathing Rate with Confidence Interval Using Single-Channel CW Radar

Nejadgholi

Sadreazami

Baird

et al. 2019

Journal of Healthcare Engineering

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Breathing rate monitoring using continuous wave (CW) radar has gained much attention due to its contact-less nature and privacy-friendly characteristic. In this work, using a single-channel CW radar, a breathing rate estimation method is proposed that deals with system nonlinearity of a single-channel CW radar and realizes a reliable breathing rate estimate by including confidence intervals. To this end, time-varying dominant Doppler frequency of radar signal, in the range of breathing rate, is extracted in time-frequency domain. It is shown through simulation and mathematical modeling that the average of the dominant Doppler frequencies over time provides an estimation of breathing rate. However, this frequency is affected by noise components and random body movements over time. To address this issue, the sum of these unwanted components is extracted in time-frequency domain, and from their surrogate versions, bootstrap resamples of the measured signal are obtained. Accordingly, a 95% confidence interval is calculated for breathing rate estimation using the bootstrap approach. The proposed method is validated in three different postures including lying down, sitting, and standing, with or without random body movements. The results show that using the proposed algorithm, estimation of breathing rate is feasible using single-channel CW radar. It is also shown that even in presence of random body movements, average of absolute error of estimation for all three postures is 1.88 breath per minute, which represents 66% improvement as compared to the Fourier transform-based approach.

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Section: Introductionmentioning

confidence: 99%

Estimation of Breathing Rate with Confidence Interval Using Single-Channel CW Radar

Nejadgholi

Sadreazami

Baird

et al. 2019

Journal of Healthcare Engineering

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show abstract

“…Nowadays, radars are increasingly used as a sensor for breathing rate monitoring, because they are contactless, privacy friendly and generally safe. Contactless monitoring using passive sensors such as video cameras invades the privacy of people and does not perform well in the absence of a line of sight [1, 2]. On the other hand, radars can penetrate occlusions depending on the frequency of transmission and the type of occlusion [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Nejadgholi

Sadreazami

Rajan³

et al. 2019

IET signal process.

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Classification is presented as a pre-processing step in this study. The state of the subject is classified as the unmoving state with normal breathing (normal breathing class), unmoving state with no breathing (stop breathing class) or the state when the subject is moving (erratic signal class) before breathing estimation algorithms are applied. Estimation algorithms may be applied to obtain breathing rate if normal breathing class is detected or alarms may be generated if stop breathing is detected, and fine-grained classification of activities may be pursued if the erratic signal is detected. Experiments were performed using a single-channel pulse-modulated continuous wave radar with three subjects for a total of 135 min. In each experiment, the subject was continuously monitored for 15 min and the subject performed activities that resulted in a signal that belonged to one of the three classes. Besides extracting a feature that assessed the distribution of energy of the signal in the frequency domain, a novel nonlinear time series feature extraction method based on the higher-dimensional embedding technique was applied to ascertain periodicity of the reflected signal. Bayes classifier was used to classify each 5-s segment of radar returns. A 30-fold cross validation resulted in 97% of overall classification accuracy.

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“…Sensors used usually include contact and non‐contact types. Contact sensors such as bangle and necklace monitors should be worn on the body [3, 4]. However, these sensors are inconvenient and vulnerable to damage and must be worn or carried [5].…”

Section: Introductionmentioning

confidence: 99%

Indoor non‐rhythmic human motion classification using a frequency‐modulated continuous‐wave radar

Zou

Ding

Hong

et al. 2019

J. eng.

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Monitoring System for Prisoner with GPS using Wireless Sensor Network (WSN)

Cited by 5 publications

References 7 publications

Estimation of Breathing Rate with Confidence Interval Using Single-Channel CW Radar

Estimation of Breathing Rate with Confidence Interval Using Single-Channel CW Radar

Classification of Doppler radar reflections as preprocessing for breathing rate monitoring

Indoor non‐rhythmic human motion classification using a frequency‐modulated continuous‐wave radar

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