International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Review on Privacy-Preserving Multimodal Patient Monitoring using Edge AI |
|---|---|
| ABSTRACT | Remote healthcare monitoring is increasingly expected to do two things at once: catch behavioural anomalies early and keep patient data private. Most existing systems rely on cloud-based processing, where raw physiological and behavioural signals travel to remote servers for analysis, adding latency, communication overhead, and privacy exposure along the way. This review looks at recent work across five connected areas: Edge Artificial Intelligence (Edge AI), multimodal sensing, behavioural anomaly detection, Federated Learning (FL), Differential Privacy (DP), and voice-assisted healthcare interaction. Across the studies surveyed, edge-based processing consistently lowers latency and reduces how much raw data leaves the device, and wearable-sensor deep learning models have reported strong performance in detecting falls, abnormal gait, and related behavioural anomalies. Federated learning lets multiple devices train a shared model collaboratively without pooling raw patient data in one place, and differential privacy adds a further layer of protection by perturbing the model updates that are shared. What stands out, though, is how separately these strands have been developed — very few studies bring anomaly detection, privacy-preserving distributed learning, and voice-mediated intervention together into one closed-loop system. This gap motivates the framework proposed here: an integrated, privacy-preserving multimodal patient-monitoring system built around lightweight CNN-LSTM models, federated learning, differential privacy, and voice-assisted escalation. |
| AUTHOR | SHREYA ROY, MAYANK BALPANDE, PRACHI MISHRA, ROMA DIVE B. Tech Scholar, Department of Computer Engineering, St. Vincent Pallotti College of Engineering & Technology, Nagpur, Maharashtra, India |
| VOLUME | 188 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1409016 |
| pdf/16_A Review on Privacy-Preserving Multimodal Patient Monitoring using Edge AI.pdf | |
| KEYWORDS | |
| References |
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