Accurate motion capture is essential for obtaining quantitative data on muscle contraction, acceleration, and angular velocity. This study presents a motion data acquisition system that meets the requirements of a wearable, wireless system by utilizing a combination of portable inertial measurement units (IMU) and surface electromyography (SEMG) sensors worn on the upper limb to capture functional movements. The system gathers functional movement data from 10 typically developing children as controls. The participants perform five tasks based on Activities of Daily Living (ADL) involving upper limb movements, designed by therapists to improve motor function, under a clinician’s guidance. The collected data were analyzed using mean, Mean Absolute Value (MAV), Root Mean Square (RMS), standard deviation, skewness, and number of zero crossing (NZR), and classified for each task using Principal Component Analysis (PCA). The results from the typically developing children serve as a benchmark for functional movement. Preliminary findings indicate that MAV, mean, RMS, and standard deviation are significant parameters in determining functional movement, with an accuracy of over 80%. The system successfully captures and classifies signals from both the IMU and SEMG for each task.