LemurDx: Objective Measurement of Hyperactivity WithMobile Sensing, Machine Learning, and Context Modeling
Riku Arakawa [1]Sam Shaaban [2], Kristie Mak [3], Mayank Goel [1], Oliver Lindhiem [4]1: Carnegie Mellon University, 2: NuRelm, 3: University of Pittsburgh Medical Center, 4: University of Pittsburgh

1

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Background• Current Problems with Measuring Hyperactivity• Reliance on parents’ / teachers’ subjective answers
Inattentive
Objective MeasureHyperactive✅

=> Need to add objectivity to the diagnosis and measurement of hyperactivity.
We want to make a tool that supports doctors’ decision making.

2APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Background

3APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Related Work
Jiang et al. (CHI’20)=> Measurements happened during speci c times.

fi

4

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHDOur Goal
Hyperactivity detection from smartwatches’ data worn by children freely intheir daily lives to enable a longer-term monitoring of children.
=> Challenge: lack of context in the motion data=> We leverage machine-learning (ML)-based approach to contextualize
motion data.

5APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Data Collection• Smartwatch App• acceleration at 50 Hz• Heart rate
• GPS
• Bluetooth device

6

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Data Collection• Activity Context• Sleeping• Sitting/Quiet• Everyday/Household• Exercise• At school• Not wearing• Other• Reported at the end of each day, with 30 minutes granularity
7

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Data Collection• Participants• 5 - 12 years• 68 completed• 32 ADHD, 36 Control• Criteria: K-SADS-PL, VADPRS• Data Cleaning• 61 participants• 25 ADHD, 36 Control
8APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Data Collection: randomly-extracted examples

Importance of making ML models aware of activity context
9APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Baseline

10APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Baseline (Results)

11APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

With context ltering using parent-annotated labels

fi

12

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

With context ltering using parent-annotated labels (Results)fi

13

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Approach + Results

14

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Approach + Results

15

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Risk Score

16

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Result
Error bar: standard deviation※ We used parent-annotated labels for generating this graph.
17

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Result
Coe cient ~ 0.3※ We used parent-annotated labels for generating this graph.

ffi

18

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Interface Design

19

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD

Summary• collected data using Apple Watch in an unconstrained setting with 61 participants.
• demonstrated that context ltering based on parent-annotated activity labels is e ective,
achieving 85.2% (F1 score = 81.6%) accuracy.

• enabled the pipeline to estimate the activity context from motion data only to achieve a fully
automated detection system, which achieved 82.0% (F1 score = 78.4%) accuracy.

• analyzed the result of the model based on the risk score and designed an initial interface to help
clinicians make decisions.

• Our next step is the deployment and evaluation of the interface.
APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD
fffi20Acknowledgements• National Institute of Mental Health (R41MH119644)• PINch21

APSARD’23, Leveraging Mobile Technology to Enhance Interventions for ADHD