Human Activity and Posture Classification Using Single Non-Contact Radar Sensor

Baird, Zachary

doi:10.22215/etd/2017-12170

Cited by 3 publications

(2 citation statements)

References 35 publications

(49 reference statements)

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“…It also does not have a problem with privacy or removal of wearable vital sign-monitoring devices by the wearer. It can detect posture changes and could possibly for detection bed-exiting [36].…”

Section: Discussionmentioning

confidence: 99%

Night-Time Monitoring System (eNightLog) for Elderly Wandering Behavior

Cheung

Tam

Mak

et al. 2021

Sensors

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Wandering is a common behavioral disorder in the community-dwelling elderly. More than two-thirds of caregivers believe that wandering would cause falls. While physical restraint is a common measure to address wandering, it could trigger challenging behavior in approximately 80% of the elderly with dementia. This study aims to develop a virtual restraint using a night monitoring system (eNightLog) to provide a safe environment for the elderly and mitigate the caregiver burden. The eNightLog system consisted of remote sensors, including a near infra-red 3D time-of-flight sensor and ultrawideband sensors. An alarm system was controlled by customized software and algorithm based on the respiration rate and body posture of the elderly. The performance of the eNightLog system was evaluated in both single and double bed settings by comparing to that of a pressure mat and an infrared fence system, under simulated bed-exiting scenarios. The accuracy and precision for the three systems were 99.0%, 98.8%, 85.9% and 99.2%, 97.8%, 78.6%, respectively. With higher accuracy, precision, and a lower false alarm rate, eNightLog demonstrated its potential as an alternative to physical restraint to remedy the workload of the caregivers and the psychological impact of the elderly.

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Section: Discussionmentioning

confidence: 99%

Night-Time Monitoring System (eNightLog) for Elderly Wandering Behavior

Cheung

Tam

Mak

et al. 2021

Sensors

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“…The radar is equipped with 6-8.5 GHz directional patch antennas with a beam of 60° in both azimuth and elevation axes. The sampling rate of this radar is 23 GS/s in fast time, up to 20S/s in slow time (Baird, 2017 ) and it was set to 17 S/s in this work. The output data is in the form of a matrix where rows represent observations or samples in slow time and the columns represent samples in fast time (corresponding to range bins).…”

Section: Materials and Protocolsmentioning

confidence: 99%

A Real-Time Respiration Monitoring and Classification System Using a Depth Camera and Radars

Han

Iglesias

et al. 2022

Front. Physiol.

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Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects.

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