A Deep Clustering via Automatic Feature Embedded Learning for Human Activity Recognition

Autor: Colin Shewell, Shuai Zhang, Chris D. Nugent, Jinde Li, Wing W. Y. Ng, Ting Wang, Qiuxia Wu

Publikováno v: IEEE Transactions on Circuits and Systems for Video Technology. 32:210-223

Traditional clustering algorithms are widely used for building bag-of-words (BOW) models to aggregate spatio-temporal feature points extracted from a video for human activity recognition problems. Their performances are restricted by the computationa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b1dc52c011b34ba9346fdd55854ec42e
https://doi.org/10.1109/tcsvt.2021.3057469

Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection

Autor: Alexandros Konios, Chris D. Nugent, Stavros-Richard G. Christopoulos, Gareth Morrison, Mustafa A. Mustafa, Matias Garcia-Constantino, Colin Shewell, Idongesit Ekerete

Publikováno v: Konios, A, Garcia-Constantino, M, Christopoulos, S-R G, Mustafa, M, Ekerete, I, Shewell, C, Nugent, C & Morrison, G 2019, Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection . in 16th IEEE International Conference on Ubiquitous Intelligence and Computing . 16th IEEE International Conference on Ubiquitous Intelligence and Computing, Leicester, United Kingdom, 19/08/19 . https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00158
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI

This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fd97653a905b41758a02eb59a84036a
https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00158

Computer Vision-Based Gait Velocity from Non-Obtrusive Thermal Vision Sensors

Autor: Javier Medina-Quero, Chris D. Nugent, Colin Shewell, Ian Cleland, Joseph Rafferty, Macarena Espinilla Estevez

Publikováno v: PerCom Workshops
2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

Gait velocity is an important measure of independence and functional ability to those within the older population. Detecting changes in gait velocity can aid to provide interventions to avoid hospitalisation, currently gait velocity is assessed in a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08d799f4a757cb6d045029ccfd039826
https://doi.org/10.1109/percomw.2018.8480174

Indoor Localisation Through Object Detection on Real-Time Video Implementing a Single Wearable Camera

Autor: Haiying Wang, Mark P. Donnelly, Colin Shewell, Chris D. Nugent

Publikováno v: XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016 ISBN: 9783319327013

This paper presents an accurate indoor localisation approach to provide context aware support for Activities of Daily Living. This paper explores the use of contemporary wearable technology (Google Glass) to facilitate a unique first-person view of t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6298440c8a08302690a5c1eca1e4f8ab
https://doi.org/10.1007/978-3-319-32703-7_238

Comparison of fiducial marker detection and object interaction in activities of daily living utilising a wearable vision sensor

Autor: Mark P. Donnelly, Chris D. Nugent, Macarena Espinilla, Javier Medina-Quero, Haiying Wang, Colin Shewell

Publikováno v: International Journal of Communication Systems. 30:e3223

Summary This paper presents a comparison between algorithms (Oriented FAST and Rotated BRIEF (ORB) and Aruco) for the detection of fiducial markers placed throughout a smart environment. A series of activities of daily living (ADL) were conducted whi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3e35f2205033c6b1fe4f69b36926d39b
https://doi.org/10.1002/dac.3223