Heloise Greeff
University of Oxford
10 Papers
5 Citations
Heloise Greeff is an academic researcher from University of Oxford. The author has contributed to research in topics: System identification & Data collection. The author has an hindex of 3, co-authored 8 publications. Previous affiliations of Heloise Greeff include University of Cape Town.
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Papers
Distributed Inference Condition Monitoring System for Rural Infrastructure in the Developing World
TL;DR: This novel method has proven that it is possible for rural operating, resource-constrained devices to use lightweight, onboard machine learning approaches to perform anomaly detection in the embedded system, making the possibility of a large-scale monitoring system feasible.
Wearable remote monitoring for patients with COVID-19 in low-resource settings: Case study
Nguyen Van Vinh Chau,Ho Bich Hai,Heloise Greeff,Khanh Phan Nguyen Quoc,Huynh Trung Trieu,Le Dinh Van Khoa,Chi Ngo Nguyen,Hoang Minh Tu Van,Lam Minh Yen,Le Van Tan,Nguyen Thanh Dung,David A. Clifton,Sophie Yacoub,C. Louise Thwaites +13 more
- 01 Mar 2021
TL;DR: A large-scale trial is needed to assess the importance of knowing the carrier and removal of canine coronavirus, as a source of infection for other animals, not necessarily belonging to the same breeds.
Shallow aquifer monitoring using handpump vibration data
Achut Manandhar,Heloise Greeff,Patrick Thomson,Rob Hope,David A. Clifton +4 more
- 01 Aug 2020
TL;DR: In this paper, the authors use accelerometer sensors attached to the handles of nine handpumps in the study site in Kenya, instrumented for a year, to track the changes in the water level with respect to the bottom of the rising main.
8
vital_sqi: A Python package for physiological signal quality control
Van-Khoa D. Le,Hai Bich Ho,Štefan Karolčík,Bernard Hernandez,Heloise Greeff,Van Hao Nguyen,Nguyen Quoc Khanh Phan,Thanh Phuong Le,C. Louise Thwaites,G. Georgiou,David A. Clifton +10 more
TL;DR: The vital_sqi package as mentioned in this paper provides a unified interface to the state-of-the-art SQIs for ECG and photoplethysmogram (PPG) signals.
4
Smart Handpumps: A Preliminary Data Analysis
Farah E. Colchester,Heloise Greeff,Patrick Thomson,Rob Hope,David A. Clifton +4 more
- 01 Jan 2014
TL;DR: It is shown that features derived from the accelerometry data exhibit stable, similar behaviour suggesting that users and pump locations may be characterised, and a machine learning system can classify the data according to person and pump and accurately differentiate between different users.