Aiden Doherty
Researcher / author (OpenAlex)
Facts
2 properties- Affiliation
- Open Data Instituteas of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
🔖 Identifiers · 2
- ORCID iD
- 0000-0003-1840-0451
- OpenAlex ID
- A5083886220, A5123105775, A5124163278, A5129002432, A5133100973, A5137756211as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Affiliated with 10
Author of 8
Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative,
A validation study of the Eurostat harmonised European time use study (HETUS) diary using wearable technology,
CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition,
Increasing Production Efficiency via Compressor Failure Predictive Analytics Using Machine Learning,
Sedentary Behavior in Children by Wearable Cameras: Development of an Annotation Protocol,
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data,
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank,
Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants
← Authored by 8
Advancing the Use of Mobile Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative,
A validation study of the Eurostat harmonised European time use study (HETUS) diary using wearable technology,
CAPTURE-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition,
Increasing Production Efficiency via Compressor Failure Predictive Analytics Using Machine Learning,
Sedentary Behavior in Children by Wearable Cameras: Development of an Annotation Protocol,
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data,
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank,
Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants
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Summary
Aiden Doherty is a human[1].