# SBIR TOPIC 425 PHASE I:  POSTURE ANALYSIS THROUGH MACHINE LEARNING (PATHML)

> **NIH NIH N43** · SENTIMETRIX, INC. · 2021 · $399,907

## Abstract

Advances in wearable technology and the availability of low-cost video have tremendous potential to provide new insight into how physical behavior is associated with health, define clinical trial outcomes and assess functional status and activities of daily living patients within their home or a rehabilitation setting. (1-4) Cameras and/or videos can record continuously in a passive and unobtrusive manner, enabling participants to provide a detailed record of daily activity that has applications in health research, memory retention and ethnography. (5-11) However, in health research the use of image processing
remains burdensome and cost prohibitive, often requiring manual annotations by trained staff. To automate annotation of images and video in recent years scientists have been using emerging machine learning technology applied to computer vision. With the help of multi-layered special purpose neural networks (Convolutional Neural Networks, Recurrent Neural Networks) researchers have
been able to accurately classify still images and video frames based on what is depicted in them, recognize the position of objects of interest in an image, recognize humans in an image, and track objects (vehicles, humans) across multiple consecutive frames of a video. (12-18) To date, this technology has been applied to commercial products and sport performance, but not to quantify
levels of physical activity, performance or behavior for health research. The long-term goal of this project is to develop a Commercial Off-The-Shelf (COTS) software program that can accurately classify physical activities (e.g. ’walking’, ‘sitting’ or ‘standing up”), information about behavior (e.g., location and purpose of the activity), and performance (e.g., walking speed and sit to stand transition times).

## Key facts

- **NIH application ID:** 10498198
- **Project number:** 75N91021C00040-0-9999-1
- **Recipient organization:** SENTIMETRIX, INC.
- **Principal Investigator:** VADIM KAGAN
- **Activity code:** N43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $399,907
- **Award type:** —
- **Project period:** 2021-09-22 → 2022-06-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10498198

## Citation

> US National Institutes of Health, RePORTER application 10498198, SBIR TOPIC 425 PHASE I:  POSTURE ANALYSIS THROUGH MACHINE LEARNING (PATHML) (75N91021C00040-0-9999-1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10498198. Licensed CC0.

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