UWB localization employing supervised learning method

Rana, Soumya Prakash; Dey, Maitreyee; Siddiqui, Hafeez Ur Rehman; Tiberi, Gianluigi; Ghavami, Mohammad; Dudley, Sandra

doi:10.1109/icuwb.2017.8250971

Cited by 37 publications

(21 citation statements)

References 13 publications

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“…This work build on that presented in [17], where UWB radar signals are learned though supervised machine learning algorithms to remotely determine and pivotally recognize the room position information of persons at different times of a day. Therefore, the MC-SVM is already trained with the time stamp and localization information required here.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Remote Vital Sign Recognition through Machine Learning augmented UWB

Dudley

Rana

Dey

et al. 2018

12th European Conference on Antennas and Propagation (EuCAP 2018)

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This paper describes an experimental demonstration of machine learning (ML) techniques supplementing radar to distinguish and detect vital signs of users in a domestic environment. This work augments an intelligent location awareness system previously proposed by the authors. That research employed Ultra-Wide Band (UWB) radar complemented by supervised machine learning techniques to remotely identify a persons room location via floor plan training and time stamp correlations. Here, the remote breathing and heartbeat signals are analyzed through Short Term Fourier Transformation (STFT) to determine the Micro-Doppler signature of those vital signs in different room locations. Then, Multi-Class Support Vector Machine (MC-SVM) is implemented to train the system to intelligently distinguish between vital signs during different activities. Statistical analysis of the experimental results supports the proposed algorithm. This work could be used to further understand, for example, how active older people are by engaging in typical domestic activities.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Remote Vital Sign Recognition through Machine Learning augmented UWB

Dudley

Rana

Dey

et al. 2018

12th European Conference on Antennas and Propagation (EuCAP 2018)

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“…The silhouette value ranges from -1 to +1. [35,36] is employed on the extracted TU feature by optimizing the distance between support vectors (TUs from different groups) given by Equations (12) and (13).…”

Section: Clustering Internal Evaluationmentioning

confidence: 99%

Smart building creation in large scale HVAC environments through automated fault detection and diagnosis

Dey

Rana

Dudley

2020

Future Generation Computer Systems

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Modernisation and retrofitting of older buildings has created a drive to install Building Energy Management Systems (BEMS) that can assist building managers in paving the way for smarter energy use and indirectly, using appropriate methods, occupant comfort understanding. BEMS may discover problems that can inform managers of building maintenance and energy wastage issues and indirectly , via repetitive data patterns appreciate user comfort requirements. The main focus of this paper is to describe a method to detect faulty Heating, Ventilation and AirConditioning (HVAC) Terminal Unit (TU) and diagnose them in an automatic and remote manner. For this purpose, a typical big-data framework has been constructed to process the very large volume of data. A novel feature extraction method encouraged by Proportional Integral Derivative (PID) controller has been proposed to describe events from multidimensional TU data streams. These features are further used to categorise different TU behaviours using unsupervised data-driven strategy and supervised learning is applied to diagnose faults. X-means clustering has been performed to group diverse TU behaviours which are experimented on daily, weekly, monthly and randomly selected dataset. Subsequently, Multi-Class Support Vector Machine (MC-SVM) has been employed based on categorical information to generate an automated fault detection and diagnosis system towards making the building smarter. The clustering and classification results further compared with wellknown and established algorithms and validated through statistical measurements.

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“…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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UWB localization employing supervised learning method

Cited by 37 publications

References 13 publications

Remote Vital Sign Recognition through Machine Learning augmented UWB

Remote Vital Sign Recognition through Machine Learning augmented UWB

Smart building creation in large scale HVAC environments through automated fault detection and diagnosis

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

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