Multi-Modal Deep Learning for Non-Invasive Elder Safety Monitoring and Hybrid Architectures Using Gas Sensor Arrays and Binary Positional IoT Data (Comprehensive Journal of Science, 2026)
The global demographic shift toward aging populations necessitates innovative, privacy-preserving solutions for continuous elder safety monitoring within domestic environments. This study presents a comprehensive comparative evaluation of three deep learning paradigms Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid CNN-LSTM architectures for the classification of behavioral anomalies using multi-modal sensor data. Leveraging the publicly available Single Elder Home Monitoring: Gas and Position dataset (UCI Repository), comprising 444,631 temporally-resolved instances (20-second intervals) of environmental gas readings (temperature, humidity, CO₂, CO, four MOX sensors) and binary positional infrared motion data across a residential layout, we develop and benchmark architectures capable of distinguishing normative activity patterns from potential safety-critical deviations. Our methodology incorporates rigorous preprocessing to mitigate sensor drift through Principal Component Analysis-based environmental correction, followed by architecture-specific feature extraction strategies. Experimental results demonstrate that the hybrid CNN-LSTM model achieves superior classification performance (F1-score: 0.942 ± 0.018) compared to standalone LSTM (0.911 ± 0.023) and CNN (0.887 ± 0.031) configurations, attributable to its dual capacity for spatial feature localization within the sensor array topology and temporal dependency modeling across sequential observations. The proposed framework operates exclusively on non-invasive, low-cost sensor modalities, thereby addressing critical privacy concerns inherent to video-based monitoring systems while maintaining robust detection sensitivity. These findings substantiate the viability of chemical sensing arrays coupled with multi-modal deep learning as a scalable, ethically-aligned foundation for next-generation aging-in-place support technologies.
Keywords: Elder safety monitoring, multi-modal deep learning, LSTM, CNN, hybrid architectures, gas sensor arrays, IoT, non-invasive sensing, activity anomaly detection, aging-in-place