Sensor-Integrated Behavioral Analytics System for Early Detection of Dysgraphia.

Dysgraphia arises from disrupted neural connections among the premotor cortex, cerebellum, and parietal lobes, affecting motor planning and kinesthetic feedback. Traditional diagnosis relies on subjective handwriting assessments lacking quantitative neuro-motor analysis.

DysCover addresses this by using sensor-integrated behavioural analytics and machine learning for early detection. It includes three games that test letter tracing (motor precision), pattern repetition (temporal coordination), and reaction-based posture control (visuomotor coupling). Participants wear an IMU wristband with a vision module to record motion parameters like jerk magnitude, tremor frequency, grip stability, and trajectory deviation. These features are analyzed through a Random Forest-assisted CNN-LSTM model, integrating spatial and temporal cues to classify dysgraphic movements with 92% accuracy.

Bycombining biomechanical sensing and visual analytics, the system establishes a quantitative behavioural biomarker framework enabling objective, early Dysgraphia screening and timely neuropsychological intervention