Short-Range Millimetric-Wave Radar System for Occupancy Sensing Application

Santra, Avik; Ulaganathan, Raghavendran Vagarappan; Finke, Thomas

doi:10.1109/lsens.2018.2852263

Cited by 68 publications

(36 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A W-band millimeter-wave radar was successfully designed and tested on a human subject in [123]. In [124], a Doppler radar was designed at 60 GHz for short-range detection of human presence as well as vital signs detection. The experimental results show promising performance for occupancy applications.…”

Section: Biomedical Practicementioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

126

View full text Add to dashboard Cite

Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

show abstract

Section: Biomedical Practicementioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

126

View full text Add to dashboard Cite

show abstract

“…and lat. R and v Undefined Radar [146] Doppler shift data (time, TX channel) [190] [63] Spectrogram (range, velocity) Radar [181] Radar image (x, z) Active imaging [191] Radar image (x, z) Active imaging [67] R, ϕ, and vr Undefined Radar [192] Radar image (x, z) Active imaging [193] Radar image (x, z) Active imaging [69] Heartbeat data (time) Radar [189] Radar image (x, z) Active imaging [72] Spectrogram (x, z) [194] [176] Radar image (x, y) Active imaging [177] Radar image (x, y) Active imaging Point cloud frame (x, y) R, vr , θ, and power Undefined [81] Spectrogram (range, velocity) [195] [83] Spectrogram (range, azimuth) Radar [84] Profile (range) Radar Range compressed down-conversion IF data [89] Point cloud frame (x, y, z) Radar [91] Spectrogram (range, velocity) Radar Point cloud frame (x, y, z) vr Undefined [97] Point cloud frame (x, y, z) Radar [99] R and θ Undefined Radar [196] Reflection intensity Undefined Radar Profile (range) Spectrogram (time, frequency) [104] Radar trace Undefined Radar [178] Spectrogram (x, y) Active imaging [105] Heartbeat data (time) [198] [112] Spectrogram (time, frequency) [199] [185] Positioning data Undefined Spatial sweeping [115] R and θ Undefined Radar [153] RSS signal (time, RX channel) Spatial sweeping Profile (range) Waterfall chart (time, range) Dynamic (time-varying) signal components Point cloud frame (x, y, z) vr Undefined Point cloud frame (x, y, z) vr and intensity Undefined [125] Spectrogram (range, velocity) Radar [131] Spectrogram (x, y) Radar…”

Section: B Pre-processingmentioning

confidence: 99%

“…In addition to filtering the types of corruption already mentioned in Section IV-B.1, data reconstruction and denoising also filter Doppler components caused by transmitter time multiplexing, remove redundant data parts, and filter data transients. Data transients are caused by persons that become stationary after walking into a room [81].…”

Section: Time Domain Frequency Domainmentioning

confidence: 99%

“…and Cartesian coordinates that fall outside certain boundaries [43], removing measured profile and spectrogram (part) averages from profiles and spectrograms [50], [81], [102], [138], deleting a profile belonging to an empty FOV from target measurement profiles [86], and statically removing the 0 Hz component from maximum Doppler frequency data across time [116]. Moving Target Indication (MTI) is not explained in [53], [58].…”

Section: Reconstructon Denoisingmentioning

confidence: 99%

See 1 more Smart Citation

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

et al. 2021

View full text Add to dashboard Cite

The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300 GHz that correspond to wavelengths ranging from 10 to 1 mm. Although millimeter wave is often considered as a communication medium, it has also proved to be an excellent 'sensor', thanks to its narrow beams, operation across a wide bandwidth, and interaction with atmospheric constituents. In this paper, which is to the best of our knowledge the first review that completely covers millimeter wave sensing application pipelines, we provide a comprehensive overview and analysis of different basic application pipeline building blocks, including hardware, algorithms, analytical models, and model evaluation techniques. The review also provides a taxonomy that highlights different millimeter wave sensing application domains. By performing a thorough analysis, complying with the systematic literature review methodology and reviewing 165 papers, we not only extend previous investigations focused only on communication aspects of the millimeter wave technology and using millimeter wave technology for active imaging, but also highlight scientific and technological challenges and trends, and provide a future perspective for applications of millimeter wave as a sensing technology.

show abstract

“…Study towards similar approaches of smart sensing is also carried out by Cao et al [31] with focus on intelligence building process with primary concern about energy management. Santra et al [32] have carried out occupancy sensing using continuous waves of frequency modulation for effective sensing capability. The approach is based on detection as well as counting of the human on the basis of the biosignals.…”

Section: A the Backgroundmentioning

confidence: 99%

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

K.Mane*,

Rao

2019

IJITEE

View full text Add to dashboard Cite

Occupancy sensing is one of predominant technology used in various control and context aware systems. The efficiency of such systems is highly corelated with the level of accuracy of the classification model of activity or stage detection of the subject or an occupant. The classification approaches based on computer need to evolve optimally for balancing the computational and time complexity with the classication accuracy using occupancy data acquired from Doppler radar. The proposed study presents a simplified framework that emphasizes on feature extraction technique as a medium to obtain more precision in the process of monitoring occupancy. The proposed logic has been implemented using analytical methodology convention and the extracted feature has been subjected to different forms of frequently used machine learning process with respect to processing time inclusive of training and testing period, efficiency, and accuracy of the proposed system.

show abstract

Short-Range Millimetric-Wave Radar System for Occupancy Sensing Application

Cited by 68 publications

References 13 publications

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks

AOCM-OAC: Architecture of Optimal Computational Model for Occupant Action Classification using Machine Learning

Contact Info

Product

Resources

About