A hybrid deep learning model for UWB radar-based human activity recognition

In today's world, energy efficiency in buildings has become a top priority due to the significant energy waste caused by the operation of inefficient electrical appliances. Conventional methods of reducing energy waste cause discomfort for occupants inside buildings. One promising way to optimize energy consumption is to synchronize appliance operation with building occupants' dynamic behavior. Internet of Things (IoT) technologies, which allow for widespread data collection and execution of Machine Learning (ML) algorithms, enabled the creation of Smart Buildings (SBs). SBs can learn patterns from the inhabitant's behavior residing in, and adjust their operations in accordance with these behaviors. By doing so, these SBs could reduce energy waste, enhancing resource efficiency and consequently reduce CO2 gas emissions. Furthermore, they could improve the overall comfort of the living environment and help with sustainability initiatives. In this context, this paper proposes a novel approach that uses a hybrid deep-learning model to recognize complex human activities based on data collected from ultra-wideband (UWB) radar technology. Our approach, called Hybrid Deep Learning Model for Activity Recognition (HDL4AR), includes long-short-term memory (LSTM) and a one-dimensional convolutional neural network (1D-CNN). We deploy a real-time case study by collecting data from 22 participants involved in 10 diverse activities at the headquarters of the ICAR-CNR in the IoT Laboratory, Italy. Moreover, we conducted a comprehensive benchmark of the HDL4AR approach against various statistical techniques and other deep learning models recently introduced in the literature. Results show that our proposed approach outperformed conventional methods and achieved an impressive accuracy of 98.42%.