Position-Aware Indoor Human Activity Recognition and Fall Detection

With increasing life expectancy, particularly in developed nations, the proportion of elderly individuals is rising rapidly, necessitating advanced systems for continuous monitoring and timely intervention to support independent living and enhance safety in assisted care environments. Falls are among the leading causes of hospitalisations and deaths related to injuries in this demographic, highlighting the urgent need for intelligent fall detection systems. However, most existing solutions struggle with real-world deployment due to incomplete anomaly modelling and a lack of contextual location awareness. This paper introduces a novel position-aware indoor activity recognition and fall detection approach that uses spatial and motion data to detect falls with high accuracy and contextual relevance. The system integrates Ultra-Wideband (UWB) positioning technology with a Multilayer Perceptron (MLP) model to achieve indoor localisation. Furthermore, accelerometer and gyroscope data are used for activity monitoring, which is processed using a hybrid deep learning architecture that combines a Variational Autoencoder (VAE), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks. This architecture takes advantage of temporal and spatial feature extraction for improved fall detection. The localisation module achieves over 96% accuracy. For activity recognition, the VAE CNN-LSTM model achieving fall detection accuracy exceeding 97%. A late fusion decision layer combines spatial and activity-level insights to enable precise detection and localisation of fall events within indoor environments. The proposed system is validated in a real-world smart home setting and demonstrates strong performance in terms of accuracy, scalability, and adaptability.