# Using Machine Learning and Sensing to Contextualize Hyperactivity Measurement on Wearable Devices

> **NIH NIH R44** · NURELM E-BUSINESS SOFTWARE · 2024 · $615,908

## Abstract

PROJECT SUMMARY/ABSTRACT
ADHD is the most common early childhood diagnosis, affecting around 9% of US children. LemurDx is a software
system that uses state-of-the-art sensor technology to measure hyperactivity. LemurDx passively collects data
from smartwatch sensors which are transmitted to a HIPAA-compliant server. Machine learning (ML) algorithms
are used to assess context (a combination of activity and environment) using multi-level classification to first
identify the wearer's class of activity, then contextualize the level of motion, and evaluate activity level based on
that context. Context (e.g., classroom versus playground) is critical to differentiate children with clinical vs normal
levels of hyperactivity. LemurDx is superior to research-focused tools such as traditional actigraphy in a number
of ways. Beyond the improved precision afforded by modern smartwatch sensors and ML algorithms, we are
designing an integrated system to address clinical trial needs. LemurDx has the potential to improve the process
of medication titration and outcome assessment in ADHD clinical trials by providing investigators with rapid
feedback and an objective outcome measure. In this project, we will test the utility of LemurDx to measure
changes in hyperactivity in children on days when they are on versus off medication in standardized and real-
life settings. This pilot data will allow us to test the utility of LemurDx as an objective measure of hyperactivity to
measure response to medication and help investigators assess medication efficacy in clinical trials. This SBIR
(R44) project will accomplish two aims over two phases. Aim 1 is to refine the LemurDx product to enhance ease
of use among clinical researchers, including the automated collection, cleaning, and feature extraction of
wearable sensor data, the ML processing pipeline, and an updated smartwatch app. Aim 2 is to test LemurDx in
100 children (ages 6-11 years) with ADHD-Hyperactive Type or Combined Type who are prescribed stimulant
medication on days when they are on versus off medication, both in real-world and standardized lab settings.
We will refine the ML algorithms to detect the presence and severity of hyperactivity. We will iteratively test and
refine LemurDx’s ML algorithms to improve its generalizability and to achieve high sensitivity and specificity in
estimating hyperactivity severity. We will use these insights to design a scalable clinical delivery system.
LemurDx innovations include a readily available and reliable measurement technology combined with accurate
ML algorithms to objectively measure hyperactivity and its context in a system that is intentionally designed to
meet the needs of clinical trial investigators. We will demonstrate the commercialization potential of LemurDx by
validating the feasibility of using LemurDx to measure medication response in children with ADHD-Hyperactive
Type or Combined Type and the acceptability of the system to ADHD clinical investigators...

## Key facts

- **NIH application ID:** 11004217
- **Project number:** 1R44MH138145-01
- **Recipient organization:** NURELM E-BUSINESS SOFTWARE
- **Principal Investigator:** Mayank Goel
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $615,908
- **Award type:** 1
- **Project period:** 2024-08-15 → 2027-08-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11004217

## Citation

> US National Institutes of Health, RePORTER application 11004217, Using Machine Learning and Sensing to Contextualize Hyperactivity Measurement on Wearable Devices (1R44MH138145-01). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/11004217. Licensed CC0.

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