Daily Activity

Research on daily activity focuses on understanding and modeling human behavior in everyday settings, primarily to improve healthcare, particularly for the elderly, and to enhance human-computer interaction. Current research employs diverse methods, including sensor data analysis (e.g., from wearables, smartphones, and thermal sensors), machine learning algorithms (like SVMs, LSTMs, and GRUs), and knowledge graph construction from multimodal data (video, audio, sensor readings) to classify activities, predict future behavior, and even identify individuals based on their activity patterns. This work has significant implications for developing assistive technologies, personalized healthcare interventions, and more intuitive human-robot interaction.