Wearable Motion Sensors in the Detection of ADHD: A Critical Review
Basic, Jakov; Uusimaa, Johanna; Salmi, Juha (2024-05-05)
Springer
05.05.2024
Basic, J., Uusimaa, J., Salmi, J. (2024). Wearable Motion Sensors in the Detection of ADHD: A Critical Review. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2084. Springer, Cham. https://doi.org/10.1007/978-3-031-59091-7_12
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405273967
https://urn.fi/URN:NBN:fi:oulu-202405273967
Tiivistelmä
Abstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with inattention, hyperactivity, and impulsivity as core symptoms. Current diagnostic methods of ADHD consisting of interviews and self-ratings come with a risk of subjective bias and are dependent on the limited availability of healthcare professionals. However, recent technological advances have opened new opportunities to develop objective and scalable methods for precision diagnostics. The present critical review covers the current literature concerning one of the promising technologies, the use of motion sensors or accelometers for detecting ADHD, particularly evaluating the related clinical potential. Several studies in this field, especially recent studies with advanced computational methods, have demonstrated excellent accuracy in detecting individual participants with ADHD. Machine learning methods provide several benefits in the analysis of rich sensor data, but the existing studies still have critical limitations in explaining the underlying cognitive functions and demonstrating the capacity for differential diagnostics is still underway. Clinical utility of sensor-based diagnostic methods could be improved by conducting rigorous cross-validation against other methods in representative samples and employing multi-sensor solutions with sophisticated analysis methods to improve interpretation of the symptom manifestation. We conclude that motion sensors provide cost-effective and easy-to-use solutions with strong potential to increase the precision and availability of ADHD diagnostics. Nevertheless, these methods should be employed with caution, as only a fraction of ADHD symptoms relate to hyperactivity captured by motion sensors. At best, this technique could complement the existing assessment methods or be used along with other digital tools such as virtual reality.
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with inattention, hyperactivity, and impulsivity as core symptoms. Current diagnostic methods of ADHD consisting of interviews and self-ratings come with a risk of subjective bias and are dependent on the limited availability of healthcare professionals. However, recent technological advances have opened new opportunities to develop objective and scalable methods for precision diagnostics. The present critical review covers the current literature concerning one of the promising technologies, the use of motion sensors or accelometers for detecting ADHD, particularly evaluating the related clinical potential. Several studies in this field, especially recent studies with advanced computational methods, have demonstrated excellent accuracy in detecting individual participants with ADHD. Machine learning methods provide several benefits in the analysis of rich sensor data, but the existing studies still have critical limitations in explaining the underlying cognitive functions and demonstrating the capacity for differential diagnostics is still underway. Clinical utility of sensor-based diagnostic methods could be improved by conducting rigorous cross-validation against other methods in representative samples and employing multi-sensor solutions with sophisticated analysis methods to improve interpretation of the symptom manifestation. We conclude that motion sensors provide cost-effective and easy-to-use solutions with strong potential to increase the precision and availability of ADHD diagnostics. Nevertheless, these methods should be employed with caution, as only a fraction of ADHD symptoms relate to hyperactivity captured by motion sensors. At best, this technique could complement the existing assessment methods or be used along with other digital tools such as virtual reality.
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