Wellness Sensing Using Wearable Sensors

January 27, 2014


Title: Wellness Sensing Using Wearable Sensors
Duration: One semester (February – June 2012)
Responsible TA: Dr. Zerrin Yumak
Student Name: Javier Martin De Valmaseda
Goals: Develop a stable platform for mobile sensor data collection, labeling and post visualization method.
Solution: Android mobile application for activity labelling and accelerometer data collection.
Keywords: mobile sensors, pervasive healthcare, time series analysis, ubiquitous computing, data collection

Activity recognition of every day activities can be used for many different purposes: preventive medicine, promotion of health-enhancing physical activities and a developing a healthier lifestyle. It has a high industrial interest and a high impact on society. In the same time wearable sensors are becoming less and less intrusive and more accurate. As well, mobile devices are becoming increasingly sophisticated and smartphones with accelerometer and other sensors are widely popular nowadays. In our study data have been collected by 8 users while performing normal daily activities along 2-3 days. They used two different types of sensors: BodyMedia and Affectiva together with an Android phone. We relied on the SAX method to process the data. Afterwards, it can be analyzed by machine learning algorithms to detect certain activity types. SAX is the first method for the symbolic representation of time series that allows dimensionality reduction and indexing with a lower-bounding distancemeasure. This symbolic approach allows a time series of length n to be reduced to a string length w (w < n).