title: MOPET: a context-aware and user-adaptive wearable system for fitness training.
abstract: OBJECTIVE
  Cardiovascular disease, obesity, and lack of physical fitness are increasingly common and negatively affect people's health, requiring medical assistance and decreasing people's wellness and productivity. In the last years, researchers as well as companies have been increasingly investigating wearable devices for fitness applications with the aim of improving user's health, in terms of cardiovascular benefits, loss of weight or muscle strength. Dedicated GPS devices, accelerometers, step counters and heart rate monitors are already commercially available, but they are usually very limited in terms of user interaction and artificial intelligence capabilities. This significantly limits the training and motivation support provided by current systems, making them poorly suited for untrained people who are more interested in fitness for health rather than competitive purposes. To better train and motivate users, we propose the mobile personal trainer (MOPET) system.
  
  
  METHODS
  MOPET is a wearable system that supervises a physical fitness activity based on alternating jogging and fitness exercises in outdoor environments. By exploiting real-time data coming from sensors, knowledge elicited from a sport physiologist and a professional trainer, and a user model that is built and periodically updated through a guided autotest, MOPET can provide motivation as well as safety and health advice, adapted to the user and the context. To better interact with the user, MOPET also displays a 3D embodied agent that speaks, suggests stretching or strengthening exercises according to user's current condition, and demonstrates how to correctly perform exercises with interactive 3D animations.
  
  
  CONCLUSIONS
  By describing MOPET, we show how context-aware and user-adaptive techniques can be applied to the fitness domain. In particular, we describe how such techniques can be exploited to train, motivate, and supervise users in a wearable personal training system for outdoor fitness activity.
authors.affiliation: HCI Lab, Department of Mathematics and Computer Science, University of Udine, Via delle Scienze 206, 33100 Udine, Italy. fabio.buttussi@dimi.uniud.it
authors.forename: Fabio
authors.initials: F
authors.lastname: Buttussi
authors.forename: Luca
authors.initials: L
authors.lastname: Chittaro
mesh_terms: D000555:Ambulatory Care Information Systems; D001185:Artificial Intelligence; D003194:Computer-Assisted Instruction / Q000295:instrumentation*; D005081:Exercise Therapy / Q000295:instrumentation* / Q000379:methods; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010353:Patient Education as Topic / Q000379:methods; D010809:Physical Fitness / Q000502:physiology*