B. Yin and A.H.C. Goris (The Netherlands)
Accelerometer, physical activity level, sensor position, pat tern classification.
In accelerometry-basedactivity monitoring, to correctly as sess the level and recognize the type of physical activity performed by the monitored subject, it is essential to have the knowledge of the sensor wearing position. For con sumer healthcare and lifestyle applications, such as with an activity monitor (AM), to restrict users with a fixed sensor position is not friendly and unrealistic as well. Therefore, it raises a need of developing a sensor position detecting method that allows a flexible sensor wearing manner and is able to extract the sensor location with very limited user device interactions. In this paper, two scenarios for achiev ing this goal are investigated. They are based on compar ing the body position dependent features that are extracted from the measured acceleration data with those in an estab lished feature database. The experiments using naturalisti cally collected data show the effectiveness of both scenar ios, and the one that employs user-specific learning appears more promising for a practical use.
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