International Journal of Science, Engineering and TechnologyAuthors: Rugved Daphal, Priya Karnawat, Harita Karnawat, Kadambari Nagare, Prof. Jameer Kotwal, Prof. Punam Raskar, Prof. Rucha Madali
Abstract: Smart mirrors represent chronically underutilized ambient interaction surfaces, yet existing implementations remain constrained by single-modality designs, absent user identification, insecure IoT integration, and inadequate empirical evaluation. This work introduces the Hybrid Smart Mirror (HSM) — a multi-modal ambient intelligence platform that fuses real-time biometric identification, natural language voice interaction, IoT device orchestration, and adaptive information rendering within a conventional mirror form factor, deployed entirely at the edge. A lightweight MobileNetV2 pipeline achieves 94.3% identification accuracy (F1 = 0.941) at 187 ms inference latency, with a 387 ms end-to-end pipeline from motion detection to personalized display. Voice command recognition achieves 6.8% Word Error Rate under controlled conditions; IoT commands are dispatched via TLS-encrypted MQTT at 43 ms round-trip latency. A STRIDE-informed security analysis underpins privacy-preserving countermeasures including on-device biometric storage and liveness detection. A proof-of-concept usability study (N=18) yields a SUS score of 82.4, exceeding the 68-point industry average and statistically outperforming five prior smart mirror systems (ANOVA F(3,68)=41.3, p<0.001). The HSM demonstrates that Raspberry Pi-class hardware can simultaneously achieve edge-native latency, sub-dollar-per-month operation, and GDPR-compliant privacy without sacrificing usability.
DOI: http://doi.org/10.5281/zenodo.20229647
