Wearable devices are recent popular tools to record data, especifically in medicine. They are used to collect continous data with high observation frequency in a determined time period. That helps to montior people’s health and facilitates to diagnose many important diseases such Type II diabetes. Depending to the type of the characteristic that is intended to be measured, different types of wearable sensors are used. For instance, accelerometers have a wide range of use from counting steps to monitoring heartbeats, glucometers are used to monitor blood glucose levels and wearable tensiometres are used to measure continous systolic blood pressure. The continous and high frequency structure of the data requires implementation of alternative statistical methods. Functional Data Analysis (FDA) is one of the popular approaches that handles time-dependent measurements as functions of time and this way allows to reveal the funcional nature of the data recorded at discrete time points. The extensions of multivariate statistical methods to funcional data let us construct linear models to make predictions or let us compute the most important variations of the data defined in a continous time interval. Our objective in organizing this session is to bring together scientists who deal with both theoretical and applied aspects of FDA and to discuss the use of FDA approaches on wearable sensor data.