Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks. INDEX TERMS Counterfactual explanations, digital twins, Internet of Things, machine learning, smart health, sociotechnical design, wearables.

Abstract. With the widespread of Internet of Things' devices, sensors, and applications the quantity of collected data grows enormously and the need of extracting, merging, analyzing, visualizing, and sharing it paves the way for new research challenges. This ongoing revolution of how personal devices are used and how they are becoming more and more wearable has important influences on the most well established definitions of end user and end-user development. The paper presents an analysis of the most diffused applications that allow end users to aggregate quantified-self data, originated by several sensors and devices, and to use it in personalized ways. From the outcomes of the analysis, we present a classification model for Internet of Things and new EUD paradigm and language that extends the ones existing in the current state of the art Internet of Things.

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