# Leveraging deep learning to classify sitting posture and measure sedentary patterns from accelerometer data in diverse cohorts

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $738,273

## Abstract

PROJECT ABSTRACT
High sedentary behavior (SB) increases risk for all-cause mortality, cardiovascular disease (CVD),
cancer, and type 2 diabetes. However, mixed evidence on how much to limit SB or how to break up
SB to reduce its negative health impacts has inhibited specific quantified SB guidelines The ubiquity
of wearable sensors, able to collect data at fine granularity (e.g., 30Hz), enables rich, nuanced SB
assessment. Computational methods to accurately quantify SB accumulation patterns (e.g., long
uninterrupted bouts of SB versus fragmented SB with numerous breaks) are needed. Building on our
previous work, in this project we will implement deep learning methods to derive posture-based
SB measures for the widely used ActiGraph sensor from raw accelerometer (at 30 Hz
granularity) or processed count outputs from hip- or wrist-worn devices across a broad range
of population groups (Aims 1, 2). We will develop a deep-learned convolution neural network
(CNN) bidirectional long short-term memory (Bi-LSTM) model. We will use extensive training and
held-out testing to reduce overfitting and improve accuracy and reproducibility on future samples. To
develop our models, we will leverage data from seven existing separately funded studies
comprising 6390 unique free-living individuals with hip- or wrist-accelerometer data (>200,000
hours of device wear), and concurrent criterion posture assessment. We will apply deep transfer
learning, novel in SB research, which exploits the basic neural architecture of an existing model, and
finetunes it for a new cohort or application. Deep transfer learning can markedly reduce computational
complexity and avoids the need for large criterion datasets. We will apply our new classifiers to
quantify SB from hip- and wrist-worn ActiGraphs for participants from the NHANES and WHISH
Studies (N= 42,496), and evaluate cross-sectional and longitudinal associations between SB
patterns and cardiometabolic health in youth and adults (Aim 3). Enabling use of a single device,
i.e., the ActiGraph, to obtain posture-based SB metrics, as well as energy-expenditure based physical
activity measures, will obviate the need for multiple devices, and improve participant compliance. Our
publicly available algorithms and accurate SB metrics will advance SB research, allowing researchers
to transfer our models to other cohorts, enabling specific quantified public health SB guidelines
(similar to physical activity guidelines) and laying the foundation for future development of self-
monitoring tools for clinicians or interventionists to personalize SB goals for their patients, and
facilitate SB change.

## Key facts

- **NIH application ID:** 10801176
- **Project number:** 1R01HL168535-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Loki Natarajan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $738,273
- **Award type:** 1
- **Project period:** 2023-12-10 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10801176

## Citation

> US National Institutes of Health, RePORTER application 10801176, Leveraging deep learning to classify sitting posture and measure sedentary patterns from accelerometer data in diverse cohorts (1R01HL168535-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10801176. Licensed CC0.

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