Using deep learning to analyze sitting habits and sedentary behavior from wearable sensors
Leveraging deep learning to classify sitting posture and measure sedentary patterns from accelerometer data in diverse cohorts
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 ID | NIH-10997425 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: Ideal candidates for this research include individuals of all ages who are interested in understanding their sedentary behavior and its health implications.
Not a fit: 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.
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 performed: 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
La Jolla, United States
- University of California, San Diego — La Jolla, United States (Active)
Researchers
- Principal investigator: Natarajan, Loki — University of California, San Diego
- Study coordinator: Natarajan, Loki
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 below.