Spasticity is a pathological condition that can occur in people with neuromuscular disorders. Objective, repeatable metrics are needed for evaluation to provide appropriate treatment and to monitor patient condition. Herein, an instrumented bimodal glove with force and movement sensors for spasticity assessment is presented. To mitigate noise artifacts, machine learning techniques are used, specifically a multitask neural network, to calibrate the instrumented glove signals against the ground truth from sensors integrated in a robotic arm. The motorized robotic arm system offers adjustable resistance to simulate different levels of muscle stiffness in spasticity, and the sensors on the robot provide ground‐truth measurements of angular displacement and force applied during flexion and extension maneuvers. The robotic sensor measurements are used to train the instrumented glove data through multitask learning. After processing through the neural network, the Pearson correlation coefficients between the processed signals and the ground truth are above 0.92, demonstrating successful signal calibration and noise mitigation.