Smart Insoles are in-shoe measurement systems primarily designed to measure forces, exerted by the ground on a human body, as a result of gait. In this paper, we test the applicability of force measurements from a smart insole, in enforcing good lifting ergonomics for manual material handling. This was achieved by classifying a lift as either good or bad using DTW (Dynamic time warping), a popular algorithm for speech recognition. Our experiments have revealed that lift classifications using this technique is 98.8% accurate with 30 seconds of computation time.