Using deep learning to analyze sitting habits and sedentary behavior from wearable sensors
Official title: Leveraging deep learning to classify sitting posture and measure sedentary patterns from accelerometer data in diverse cohorts
Funded at University of California, San Diego · NIH-10997425 · Data updated 2026-11-12
This study is looking at how smart technology can help us understand different ways people sit and how long they stay seated, using data from wearable devices, to help everyone find better ways to move and stay healthy.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Diego NIH-funded |
| Lab location | 1 site (La Jolla, United States) |
| Project start | December 10, 2023 |
| Project end | November 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $622,196 |
| NIH project number | 5R01HL168535-02 |
| Project ID | NIH-10997425 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals of all ages who are interested in understanding their sedentary behavior and its health implications.
✕Less likely to help
Patients who are unable to wear accelerometers or those with conditions that prevent them from engaging in typical daily activities may not benefit from this research.
What this research studies
This research investigates how deep learning techniques can be applied to data collected from wearable accelerometers to classify different sitting postures and measure patterns of sedentary behavior. By analyzing data from a diverse group of individuals, the study aims to develop accurate models that can quantify how long people sit and how often they break up their sitting time. The approach involves training advanced neural networks on extensive datasets to improve the understanding of sedentary behavior and its health impacts. This could lead to better guidelines for reducing sedentary time and improving overall health.
Why it matters
Potential benefit: If successful, this research could provide clearer guidelines for reducing sedentary behavior, potentially lowering the risk of serious health conditions like diabetes and cardiovascular disease.
How similar studies have gone: Previous research using wearable sensors and machine learning has shown promise in understanding physical activity patterns, suggesting that this approach could yield valuable insights.
Where this research is happening
-
University of California, San Diego
La Jolla, United States Active
Researchers
- Principal investigator: Natarajan, Loki — University of California, San Diego
- Study coordinator: Natarajan, Loki
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.