IoT-enabled smart home activity classification using BiLSTM and attention mechanisms
- 1Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Abstract
IoT-enabled smart homes require accurate activity classification to enhance safety, healthcare monitoring, and energy efficiency through continuous sensing and intelligent interpretation of human behavior. Common approaches employ wearable or ambient sensors, signal preprocessing, feature extraction, and deep learning models such as CNNs, LSTMs, BiLSTM, and attention-based classifiers. These techniques generally achieve high classification accuracy, robust temporal modeling, and improved detection of complex activities, supporting reliable automation, health assessment, and anomaly recognition. Challenges include sensor noise, data imbalance, privacy concerns, limited generalization across homes, computational complexity, and reduced performance in real-time or resource-constrained environments. The proposed IoT framework integrates normalized sensor vectors with BiLSTM and attention mechanisms, achieving improved temporal feature learning and more accurate activity and anomaly classification. The proposed BiLSTM-Attention framework achieved 97.2% accuracy, 95.1% Dice coefficient, 90.6% Jaccard index, 96.5% sensitivity, and 98.1% specificity on the smart home activity dataset.
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BibTeX
@article{pradhan2026iotenabled,
title = {{IoT-enabled smart home activity classification using BiLSTM and attention mechanisms}},
author = {Sasmita Kumari Pradhan and Suryakanth V. Gangashetty},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0355599},
url = {https://doi.org/10.1371/journal.pone.0355599},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - IoT-enabled smart home activity classification using BiLSTM and attention mechanisms
AU - Sasmita Kumari Pradhan
AU - Suryakanth V. Gangashetty
PY - 2026
DA - 2026/09/24
JO - PLOS One
DO - 10.1371/journal.pone.0355599
UR - https://doi.org/10.1371/journal.pone.0355599
AB - IoT-enabled smart homes require accurate activity classification to enhance safety, healthcare monitoring, and energy efficiency through continuous sensing and intelligent interpretation of human behavior. Common approaches employ wearable or ambient sensors, signal preprocessing, feature extraction, and deep learning models such as CNNs, LSTMs, BiLSTM, and attention-based classifiers. These techniques generally achieve high classification accuracy, robust temporal modeling, and improved detection of complex activities, supporting reliable automation, health assessment, and anomaly recognition. Challenges include sensor noise, data imbalance, privacy concerns, limited generalization across homes, computational complexity, and reduced performance in real-time or resource-constrained environments. The proposed IoT framework integrates normalized sensor vectors with BiLSTM and attention mechanisms, achieving improved temporal feature learning and more accurate activity and anomaly classification. The proposed BiLSTM-Attention framework achieved 97.2% accuracy, 95.1% Dice coefficient, 90.6% Jaccard index, 96.5% sensitivity, and 98.1% specificity on the smart home activity dataset.
SN - 1932-6203
ER -