Elliott Fullerton

ORCID: 0000-0003-4483-654X
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About
Contact & Profiles
Research Areas
  • Pharmaceutical industry and healthcare
  • Winter Sports Injuries and Performance
  • Lower Extremity Biomechanics and Pathologies
  • Physical Activity and Health
  • Cancer Treatment and Pharmacology
  • Context-Aware Activity Recognition Systems
  • Non-Invasive Vital Sign Monitoring
  • Sports Performance and Training
  • Anomaly Detection Techniques and Applications
  • Pharmaceutical Practices and Patient Outcomes
  • Occupational Health and Performance
  • Historical Medical Research and Treatments

Loughborough University
2017-2019

Universidad Carlos III de Madrid
2017

Sheffield Hallam University
2017

Objectives: Recognising human activity is very useful for an investigator about a patient's behaviour and can aid in prescribing future recommendations.The use of body worn accelerometers has been demonstrated to be accurate measure activity, however research looking at the multiple free living environment recognise wide range activities not evident.This study aimed successfully subcategory types through environment.Method: Ten participants (Age = 23.1 ± 1.7 years, height =171.0 4.7 cm, mass...

10.1109/jsen.2017.2722105 article EN IEEE Sensors Journal 2017-06-30

Wearable devices are a popular training tool to measure biomechanical performance indicators during running, including vertical oscillation (VO). VO is contributing factor in running economy and injury risk, therefore feedback can have positive impact on performance. The validity reliability of the measurements from wearable crucial for them be an effective tool. aims this study were test against video analysis single trunk marker. Four compared: INCUS NOVA, Garmin Heart Rate Monitor-Pro...

10.1371/journal.pone.0277810 article EN cc-by PLoS ONE 2022-11-17

Recognition of activities performed during military training may benefit the identification and quantification factors that predispose to high prevalence injury. There is evidence suggest use machine learning classifiers along with features from accelerometry data can achieve accurate activity recognition; however, there no this application within activities. PURPOSE: To develop determine accuracy decision tree (DT), support vector (SVM), k-nearest neighbour (KNN) ensemble bagged (EBT)...

10.1249/01.mss.0000561603.03899.ac article EN Medicine & Science in Sports & Exercise 2019-06-01
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