Multi-occupant tracking with radar and wearable devices for enhanced accuracy in indoor environments

This work explores the integration of millimetre-wave (mmWave) radar and a minimal configuration of ultra-wideband (UWB) devices for enhanced multi-occupant tracking in real domestic environments. Using a low-cost, non-intrusive, and rapidly deployable device setup, our approach addresses key challenges in multi-occupant tracking, including individual identification and ease of installation. While mmWave radar precisely detects occupant presence, it lacks individual recognition and exhibits limited sensitivity. This limitations are addressed by incorporating a minimal configuration of UWB (wearable tags and ambient anchors), enabling individual identification through signal strength measurements. Several data autoencoder models, such as long short-term memories (LSTMs), Convolutional Neural Networks (CNN) or Transformers, were evaluated. Experiments conducted in two real-world domestic settings, each with up to three inhabitants, demonstrate the effectiveness of combining mmWave and UWB technologies for indoor multi-occupant tracking. Our results show that ConvLSTM achieves the best performance with a mean squared error (MSE) between 0,0142 and 0,0433 in single and multi-occupation, respectively. These findings suggest promising applications for accurate inhabitant tracking in ambient assisted living and other smart environment contexts.