Gesture-Based Control Interfaces for AI-Powered Smart Environments

Abstract

Gesture-based control interfaces leverage natural, intuitive human movements to enable touchless interaction within AI-powered smart environments, addressing limitations of traditional input modalities such as touchscreens and voice control. This expanded study presents a comprehensive analysis of system design, algorithmic performance, user experience, and deployment considerations for gesture-recognition solutions integrated into smart-home and smart-building platforms. We develop a unified prototype comprising a depth-sensing camera, preprocessing pipeline, recognition engine (featuring Hidden Markov Models, Convolutional Neural Networks, and Long Short‑Term Memory networks), and a control interface driving real-world IoT devices. A within-subjects evaluation with thirty participants captured five atomic gestures across varied lighting and background conditions. Detailed statistical analysis—anchored by one-way ANOVA and post-hoc testing—confirms that LSTM-based models deliver superior accuracy (M = 94.2 %, SD = 2.9 %) and latency (M = 120 ms) compared to CNN (M = 91.5 %, SD = 3.8 %; M = 135 ms) and HMM (M = 88.7 %, SD = 4.2 %; M = 150 ms). Qualitative feedback highlights critical factors such as calibration ease, feedback mechanisms, and adaptability to individual movement styles. Drawing on these findings, we articulate design guidelines for robust, real-time gesture controls—emphasizing sensor placement, environmental robustness, computational efficiency, and customizable gesture vocabularies. The results demonstrate the feasibility of deploying advanced deep‑learning approaches in consumer-grade smart environments, enhancing accessibility, engagement, and system responsiveness. Future research directions include on-device inference for privacy preservation, multimodal fusion with voice and gaze inputs, and adaptive learning pipelines that evolve with user behavior over time.