Adrián Sánchez-Miguel Ortega

The aging population presents significant challenges for healthcare and social services, emphasizing the need for innovative solutions that support independent living. This study explores the feasibility of identifying Instrumental Activities of Daily Living (IADLs) through power consumption data collected from smart plug-based system. Using a combination of unsupervised and supervised machine learning techniques, including K-Means clustering and Long Short-Term Memory (LSTM) networks, we developed a method to classify and predict IADLs based on energy usage patterns. The REFIT dataset was used to train and validate the models, ensuring generalizability across different households. Results demonstrate that K-means clustering effectively group energy consumption patterns with Silhouette & DB algorithms in a reasonable time (Silhouette score of 0.88 and a Davies-Bouldin Index of 0.29), while LSTM models trained on monthly household data, demonstrated high rates of activities classified over time (with F1-Score of 0.99). IADLs like cooking, cleaning, and entertainment showed the highest classification accuracy due to their distinct energy features. This approach enables non-intrusive monitoring of daily routines, offering potential applications in Ambient Assisted Living (AAL) environments. Despite limitations in detecting activities without direct energy consumption, this study highlights the potential of energy-based activity recognition for promoting independent aging. Future work will focus on refining abnormal behavior detection and integrating additional contextual factors to improve accuracy.

In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments.