During demographic shifts towards an older population, healthcare systems face increased demands, highlighting the need for innovative approaches that facilitate supporting older adults’ well-being and safety. This study aims to demonstrate the effectiveness of zero-effort Ambient Assisted Living technology in recognizing daily activities of older adults via machine learning algorithms by comparing with wearable technology.
Conducted in a smart home environment equipped with a comprehensive range of non-intrusive sensors, the study involved 40 participants, during which they were instructed to perform 23 types of predefined daily living activities, organized in five phases. Data from these activities were concurrently captured by both ambient and wearable sensors. Analysis was performed using five machine learning models: K-Nearest Neighbors, Decision Trees, Random Forest, Adaptive Boosting, and Gaussian Naive Bayes.
Ambient sensors, especially using the AdaBoost model, demonstrated high accuracy (0.964) in activity recognition, significantly outperforming wearable sensors (best accuracy 0.367 with Random Forest). When fusing data from both sensor types, the accuracy slightly decreases to 0.909. Despite spatial overlap challenges, ambient sensors accurately recognize activities across various room settings with accuracies all above 0.950. Feature importance analysis reveals that climatic, electrical, and motion-related features are crucial for model classification.
This study showcases the efficacy of Ambient Assisted Living technology in recognizing daily indoor activities of older adults. These findings have implications for public health, highlighting Ambient Assisted Living technology's potential to support older adults' independence and well-being, offering a promising direction for future research and application in smart living environments.
}, year = {2025}, journal = {Internet of Things}, volume = {30}, month = {03/2025}, url = {https://www.sciencedirect.com/science/article/pii/S2542660525000150}, doi = {https://doi.org/10.1016/j.iot.2025.101502}, }