Smart textiles embedded with capacitive touch sensors offer significant potential for intuitive gesture-based interaction, yet recognizing complex gestures on resource-constrained wearable devices remains challenging. This paper presents a minimalist neural network architecture specifically optimized for knitted capacitive touch interfaces. Our approach efficiently recognizes single and multi-touch gestures including taps, swipes, and pinches with accuracy exceeding 90% on training data and 80% on testing data. To assess real-world usability, we conducted a comparative user study evaluating participant performance with our knitted interface against a conventional trackpad in a gesture-controlled gaming scenario. Results demonstrated comparable overall performance between both interfaces, with participants achieving similar game scores despite the novelty of the textile interface. Statistical analysis revealed rapid user adaptation to the textile interface, with performance stabilizing after initial trials while the standard trackpad showed continuous improvement throughout testing. Quantitative metrics were supplemented by qualitative feedback highlighting the comfort and tactile appeal of the textile interface. This work advances the practical deployment of smart textile gesture recognition systems by addressing both technical performance requirements and usability considerations for real-world applications.