Development of an Intelligent Catch the Stick System for Measuring Human Motor Coordination and Reaction Speed - Wirawan, Nanda Tommy; Ernes, Risa Nadia (ILKOM Jurnal Ilmiah, 2026)
Conventional clinical methods for assessing sensorimotor coordination, such as the Fugl Meyer Assessment (FMA) and Action Research Arm Test (ARAT), often lack the objectivity and high-resolution sensitivity required to detect subtle micro improvements in motor performance. This study presents the design, development, and validation of an intelligent "Catch the Stick" system aimed at accurately and quantitatively assessing human sensorimotor coordination and reaction speed. The proposed multi-metric system integrates 9 axis inertial measurement units (IMUs), a 60 fps computer vision tracking system, and algorithmic classification to evaluate real-time temporal and spatial responses during random stick-dropping tasks. An experimental study was conducted involving fifteen participants (10 healthy individuals and 5 clinical patients with mild to moderate sensorimotor deficits) tested under varying stimulus loads ranging from 1 to 10 sticks. The system demonstrated strong to excellent test-retest reliability (ICC 0.75) and high detection precision (±15 ms temporal error, 2 mm spatial error). Experimental results revealed that increased stick quantity directly correlated with prolonged reaction times, thereby objectively quantifying cognitive motor overload. Furthermore, the system exhibited strong concurrent validity with conventional tools, showing significant positive correlations with FMA (r = 0.78) and ARAT (r = 0.74) scores. Notably, the intelligent system proved more sensitive to micro improvements in 72% of participants compared to traditional clinical scales, although ceiling effects were observed in low difficulty tasks among healthy users. Overall, the intelligent Catch the Stick platform offers a robust, scalable, and highly sensitive solution for quantifying sensorimotor performance in clinical settings, laying the foundation for future robotic automation and autonomous training protocols