LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion matrices, showed the model's proficiency in distinguishing between normal activities and falls. This study contributes to the advancement of smart home safety, presenting a robust framework for real-time anomaly monitoring.