Thawatchai Promratthawatchai.pro@mail.kmutt.ac.th Pornchai Mongkolnam Suree Funilkul Sumet Angkasirikul Data Science and Engineering Laboratory (D-Lab), School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand Currently, the number of elderly people worldwide has substantially increased. The elderly living alone are prone to accidents such as falls, which sometimes lead to fatalities without timely notifications and help. This research applies sensors in smartphone, data mining, web services and cloud computing techniques. We have developed an Android application for a smartphone to detect daily activities of the elderly. Smartphone communicates with the cloud computing through web services. The cloud integrates with data mining to classify activities done by the elderly. In addition, we have developed the Android application for a tablet computer to monitor the daily activities done by the elderly, including lying, sitting, standing, walking, running, fall, and any changes occurring in their routines. This can help to perceive the risk as well as the daily activities of the elderly and find a solution for a timely assistance. Furthermore, it also helps a caregiver or family members to monitor the elderly’s activities when they need to go out of their homes. Activity classification cloud computing daily life activities sensor fusion