Is wearable data reliable for monitoring behavior? Design of a wearable-based IoMT puzzle game for remote behavior monitoring

Abstract— Wearable Internet of Medical Things (IoMT) platforms are transforming remote health monitoring by enabling continuous, real-world data acquisition for chronic and neurodevelopmental conditions. However, ensuring data quality and system reliability remains a key challenge, particularly outside controlled clinical environments. Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects approximately 11.8 million children in the US. While existing treatment plans can help manage symptoms, current assessment methods rely heavily on subjective clinical rating scales, which are prone to errors and may lead to inaccurate evaluations. To address this, we present MindGame, a wearable IoMT system that integrates a commercially available smartwatch (Samsung Galaxy Watch 4) and a digital puzzle game to remotely monitor ADHD related behaviors. MindGame synchronizes gameplay data with physiological signals (accelerometer, gyroscope, heart rate, and computer mouse movement. We conducted a week-long study with 5 ADHD and 7 control participants, who used the system in both remote and laboratory environments. Twenty-seven unique puzzles were created. We analyzed four key data quality metrics: signal-to-noise ratio, percentage data loss, percentage of zeros (e.g., 0 bpm heart rate), and sample rate consistency. MindGame successfully collected data from 2427 puzzles with multimodal sensors from different devices. Our results highlight critical differences in data quality across remote and laboratory settings, validating the importance of adaptive quality check algorithms for real-world sensing.