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LemurDx: Using Unconstrained Passive Sensing for an Objective Measurement of Hyperactivity in Children with no Parent Input

Published: 12 June 2023 Publication History

Abstract

Hyperactivity is the most dominant presentation of Attention-Deficit/Hyperactivity Disorder in young children. Currently, measuring hyperactivity involves parents' or teachers' reports. These reports are vulnerable to subjectivity and can lead to misdiagnosis. LemurDx provides an objective measure of hyperactivity using passive mobile sensing. We collected data from 61 children (25 with hyperactivity) who wore a smartwatch for up to 7 days without changing their daily routine. The participants' parents maintained a log of the child's activities at a half-hour granularity (e.g., sitting, exercising) as contextual information. Our ML models achieved 85.2% accuracy in detecting hyperactivity in children (using parent-provided activity labels). We also built models that estimated children's context from the sensor data and did not rely on activity labels to reduce parent burden. These models achieved 82.0% accuracy in detecting hyperactivity. In addition, we interviewed five clinicians who suggested a need for a tractable risk score that enables analysis of a child's behavior across contexts. Our results show the feasibility of supporting the diagnosis of hyperactivity by providing clinicians with an interpretable and objective score of hyperactivity using off-the-shelf watches and adding no constraints to children or their guardians.

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  • (2024)Digital assessments for children and adolescents with ADHD: a scoping reviewFrontiers in Digital Health10.3389/fdgth.2024.14407016Online publication date: 8-Oct-2024
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  • (2024)Multi-stakeholder Perspectives on Mental Health Screening Tools for ChildrenProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642604(1-15)Online publication date: 11-May-2024
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  1. LemurDx: Using Unconstrained Passive Sensing for an Objective Measurement of Hyperactivity in Children with no Parent Input

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        cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
        Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies  Volume 7, Issue 2
        June 2023
        969 pages
        EISSN:2474-9567
        DOI:10.1145/3604631
        Issue’s Table of Contents
        This work is licensed under a Creative Commons Attribution International 4.0 License.

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        Association for Computing Machinery

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        Publication History

        Published: 12 June 2023
        Published in IMWUT Volume 7, Issue 2

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        Author Tags

        1. ADHD
        2. activity recognition
        3. diagnosis support tool
        4. health sensing
        5. hyperactivity

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        View all
        • (2024)Digital assessments for children and adolescents with ADHD: a scoping reviewFrontiers in Digital Health10.3389/fdgth.2024.14407016Online publication date: 8-Oct-2024
        • (2024)ConverSearch: Supporting Experts in Human Behavior Analysis of Conversational Videos with a Multimodal Scene Search ToolACM Transactions on Interactive Intelligent Systems10.1145/370901215:1(1-31)Online publication date: 23-Dec-2024
        • (2024)Multi-stakeholder Perspectives on Mental Health Screening Tools for ChildrenProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642604(1-15)Online publication date: 11-May-2024
        • (2024)Towards a Multimodal Approach for Assessing ADHD Hyperactivity BehaviorsProceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024)10.1007/978-3-031-77571-0_1(3-14)Online publication date: 21-Dec-2024

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