ArticleSensors (Basel, Switzerland)2025
Personalized Smart Home Automation Using Machine Learning: Predicting User Activities.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Accelerator-Free Thermal Pose Estimation: Accuracy-Efficiency Trade-Offs on the Raspberry Pi 5.Sensors (Basel, Switzerland) · 2026Article
- Sensor-Driven Deep Learning for Smart Home Intelligence: Signal Analysis, Multimodal Perception, and System-Level Applications.Sensors (Basel, Switzerland) · 2026Review
- Efficient Thermal Pose Estimation: Balancing Accuracy and Edge Deployment for Smart Home Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- From Edge Transformer to IoT Decisions: Offloaded Embeddings for Lightweight Intrusion Detection.Sensors (Basel, Switzerland) · 2026Article
- A Joint Gesture-Identity Recognition Framework Based on 4D Millimeter-Wave Radar Sensing.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A personalized framework for smart home automation is introduced, utilizing machine learning to predict user activities and allow for the context-aware control of living spaces. Predicting user activities, such as 'Watch_TV', 'Sleep', 'Work_On_Computer', and 'Cook_Dinner', is essential for improving occupant comfort, optimizing energy consumption, and offering proactive support in smart home settings. The Edge Light Human Activity Recognition Predictor, or EL-HARP, is the main prediction model used in this framework to predict user behavior. The system combines open-source software for real-time sensing, facial recognition, and appliance control with affordable hardware, including the Raspberry Pi 5, ESP32-CAM, Tuya smart switches, NFC (Near Field Communication), and ultrasonic sensors. In order to predict daily user activities, three gradient-boosting models-XGBoost, CatBoost, and LightGBM (Gradient Boosting Models)-are trained for each household using engineered features and past behaviour patterns. Using extended temporal features, LightGBM in particular achieves strong predictive performance within EL-HARP. The framework is optimized for edge deployment with efficient training, regularization, and class imbalance handling. A fully functional prototype demonstrates real-time performance and adaptability to individual behavior patterns. This work contributes a scalable, privacy-preserving, and user-centric approach to intelligent home automation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.