Author Description

The topic of the Internet-of-Things (IoT) has been discussed and studied extensively since 2010. It provides various solutions for enhancing the user's experience, monitoring the user's behaviors, and improving the lifestyle. With careful design, these systems can be built with off-the-shelf smartphones and wearables. The detected result can be used as feedback for the user to understand his/her behavior, improve the lifestyle, or avoid the danger. Furthermore, the result also provides a valuable data source for the studies in psychology and sociology. However, designing an IoT system to monitor human activities is challenging due to multiple factors. Some systems require high computing capability or a long time of data collection; some systems must detect some specific behaviors as quickly as possible in real-time; some systems suffer constant and irregular noise. In order to address these challenges, the designer must carefully consider the use case of the IoT system and select proper machine learning algorithms. This dissertation shows three designs of the IoT systems for the improvement of family mealtime experience and driving safety. The procedure for each design is introduced in detail, including the architecture of the system, the selection of features, and the evaluation of algorithms. From the case studies in this dissertation, several special aspects of monitoring human activities are discovered. Since human activity is strongly related to the time-series and may change along time, the algorithm should be sensitive to context, be adaptive to dynamic conditions, process readable features, and benefit directly from prior knowledge. This discovery will serve as a guide about how to analyze and solve a problem with the IoT systems in the future.