TY - GEN
T1 - Towards Diagnostic Support of Hyperactivity in Adults with ADHD using a Virtual Reality Based Continuous Performance Test and Motion Sensor Data
AU - Jensen, Tobias Delcour
AU - Katarzyna Korbutt, Weronika
AU - Nedelev, Georgi Petrov
AU - Bemman, Brian
PY - 2022
Y1 - 2022
N2 - Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that affects up to 5% of adults worldwide. Recent research has suggested that diagnostic support technologies for ADHD may be less effective for adults while many focus on identifying attention deficits, leaving assessments of hyperactivity largely to subjective criteria and observations by clinicians. In this paper, we present a virtual reality (VR) based continuous performance test (CPT) intended to provide users with an attention task, during which their physical movements are measured by the system's sensors, within an environment designed to resemble a real-world situation in which symptoms of ADHD would typically manifest. The design of this virtual environment was informed through a series of interviews and collaborative design sessions with clinicians. The VR-CPT system was tested using 20 adult participants with and without ADHD in order to determine which of any single or combined measures of motion by sensor (head-mounted display, arm controller, leg controller) and inertial variable (acceleration, velocity, angular acceleration, angular velocity) can be used to distinguish the two groups. Our results indicate that of our single measures, angular velocity across all sensors, angular acceleration of the leg controller, and velocity of the arm controller proved significant. Additionally, isolating high levels of mean motion activity, as measured by our combined inertial variables measure for a single sensor, proved insufficient at distinguishing between motion activity events corresponding to observations of physical movements considered indicative of hyperactivity and events considered non-indicative by a clinician.
AB - Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition that affects up to 5% of adults worldwide. Recent research has suggested that diagnostic support technologies for ADHD may be less effective for adults while many focus on identifying attention deficits, leaving assessments of hyperactivity largely to subjective criteria and observations by clinicians. In this paper, we present a virtual reality (VR) based continuous performance test (CPT) intended to provide users with an attention task, during which their physical movements are measured by the system's sensors, within an environment designed to resemble a real-world situation in which symptoms of ADHD would typically manifest. The design of this virtual environment was informed through a series of interviews and collaborative design sessions with clinicians. The VR-CPT system was tested using 20 adult participants with and without ADHD in order to determine which of any single or combined measures of motion by sensor (head-mounted display, arm controller, leg controller) and inertial variable (acceleration, velocity, angular acceleration, angular velocity) can be used to distinguish the two groups. Our results indicate that of our single measures, angular velocity across all sensors, angular acceleration of the leg controller, and velocity of the arm controller proved significant. Additionally, isolating high levels of mean motion activity, as measured by our combined inertial variables measure for a single sensor, proved insufficient at distinguishing between motion activity events corresponding to observations of physical movements considered indicative of hyperactivity and events considered non-indicative by a clinician.
KW - ADHD
KW - Hyperactivity
KW - Diagnostic support
KW - Virtual reality
KW - Continuous performance test
KW - Motion sensor data
UR - http://www.scopus.com/inward/record.url?scp=85127844492&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-99194-4_31
DO - 10.1007/978-3-030-99194-4_31
M3 - Article in proceeding
SN - 978-3-030-99193-7
T3 - Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
SP - 505
EP - 521
BT - Pervasive Computing Technologies for Healthcare
A2 - Lewy, Hadas
A2 - Barkan, Refael
PB - Springer
T2 - EAI PervasiveHealth 2021 - 15th EAI International Conference on Pervasive Computing Technologies for Healthcare
Y2 - 6 December 2021 through 8 December 2021
ER -