MOPET: A context-aware and user-adaptive wearable system for fitness training

@article{citeulike:2342177, abstract = {SummaryObjective 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 and material 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.Results and conclusion 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.}, author = {Buttussi, Fabio and Chittaro, Luca }, booktitle = {Wearable Computing and Artificial Intelligence for Healthcare Applications}, citeulike-article-id = {2342177}, doi = {10.1016/j.artmed.2007.11.004}, journal = {Artificial Intelligence in Medicine}, month = {February}, number = {2}, pages = {153--163}, posted-at = {2008-02-07 01:33:26}, priority = {2}, title = {MOPET: A context-aware and user-adaptive wearable system for fitness training}, url = {http://dx.doi.org/10.1016/j.artmed.2007.11.004}, volume = {42}, year = {2008} }

TY - JOUR ID - citeulike:2342177 L3 - citeulike-article-id:2342177 TI - MOPET: A context-aware and user-adaptive wearable system for fitness training T2 - Wearable Computing and Artificial Intelligence for Healthcare Applications JF - Artificial Intelligence in Medicine VL - 42 IS - 2 SP - 153 EP - 163 N2 - SummaryObjective 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 and material 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.Results and conclusion 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. AU - Buttussi, Fabio AU - Chittaro, Luca PY - 2008/02// UR - http://dx.doi.org/10.1016/j.artmed.2007.11.004 ER -