Remote Vital Sign Recognition through Machine Learning augmented UWB

Dudley, Sandra; Rana, Soumya Prakash; Dey, Maitreyee; Brown, R. H.; Siddiqui, Hafeez Ur Rehman

doi:10.1049/cp.2018.0978

Cited by 20 publications

(21 citation statements)

References 8 publications

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“…A Time Domain PulsON P410 monostatic radar module (P410 MRM) have been used to collect all the physiological UWB sensing phenomenon reported here and published in [24], [25], [26], [27], [28], are shown in Figure 2a. The device is a monostatic pulsed Doppler radio transceiver which utilises TW-TOF omni-directional range measurement techniques as a hybrid ranging radio and a radar sensor device for nonintrusive human gait measurement.…”

Section: A Laboratory Set-upmentioning

confidence: 99%

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Rana

Dey

Ghavami

et al. 2021

IEEE Trans. Instrum. Meas.

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Gait disorder diagnosis and rehabilitation is one area where human perception and observation are highly integrated. Predominantly, gait evaluation, comprises technological devices for gait analysis such as, dedicated force sensors, cameras, and wearable sensor based solutions, however they are limited by insufficient gait parameter recognition, post processing, installation costs, mobility, and skin irritation issues. Thus, the proposed study concentrates on the creation of a widely deployable, noncontact and non-intrusive gait recognition method from impulse radio ultra wideband (IR-UWB) sensing phenomenon, where a standalone IR-UWB system can detect gait problems with less human intervention. A 3D human motion model for gait identification from IR-UWB has been proposed with embracing spherical trigonometry and vector algebra to determine knee angles. Subsequently, normal and abnormal walking subjects were involved in this study. Abnormal gait subjects belong to the spastic gait category only. The prototype has been tested in both the anechoic and multipath environments. The outcomes have been corroborated with a simultaneously deployed Kinect Xbox sensor and supported by statistical graphical approach Bland and Altman (B&A) analysis.

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Section: A Laboratory Set-upmentioning

confidence: 99%

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Rana

Dey

Ghavami

et al. 2021

IEEE Trans. Instrum. Meas.

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show abstract

“…A Time Domain PulsON 410 ranging and communications module (P410 RCM) and P410 monostatic radar module (P410 MRM) are used for this and previous work by the team [34], [35], are shown in Figure 3a. The device is a UWB monostatic pulsed Doppler radio transceiver.…”

Section: A Laboratory Set-upmentioning

confidence: 99%

Non-Contact Human Gait Identification Through IR-UWB Edge-Based Monitoring Sensor

et al. 2019

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Non-contact sensors are negating the use of wearables or cameras and providing a rewarding and accepting environment to assist in biomedical applications such as, physiological examinations, physiotherapy, home assistance, rehabilitation success determination, compliance and health diagnostics. In this study, physiological parameter identification of human gait has been demonstrated through an edge based sensor and heuristic approach. Impulse radio ultra-wide band (IR-UWB) pulsed Doppler radar has been employed with a focus on human walking patterns. This work extracts an individual's gait trait from associated biomechanical activity and differentiates the lower limb movement patterns from other body areas via a radar transceiver. It is observed that Doppler shifts alone are not reliable to detect human gait because of frequency shifts occurring across the entire body (including, breathing, heartbeat, and arm movements) where movement occurs. Thus, a heuristic spherical trigonometrical approach has been proposed to augment radar principles and short term fourier transformation (STFT) to identify the gait trait precisely. The experiment presented includes data gathering from a number of male and female participants in both ideal and real environments. Subsequently, the proposed gait identification and parameter characterization has been analysed, tested and validated against popularly accepted smartphone applications where the errors are less than 5%.

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“…Additionally, this system does not suffer in the presence of Wi-Fi and mobile phone signals. IR-UWB radar has advantages over other existing tools due to its non-intrusive, non-tackling capabilities and its potential to penetrate through different materials or obstacles [ 32 , 33 ]. Various investigations have been performed on UWB-based wireless sensing devices to detect vital signs for health care applications [ 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Driver Drowsiness Detection System

Siddiqui

Saleem

Brown

et al. 2021

Sensors

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Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

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Remote Vital Sign Recognition through Machine Learning augmented UWB

Cited by 20 publications

References 8 publications

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

3-D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon

Non-Contact Human Gait Identification Through IR-UWB Edge-Based Monitoring Sensor

Non-Invasive Driver Drowsiness Detection System

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