Document Type

Conference Proceeding



Format of Original

6 p.

Publication Date



Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC)

Source ISSN



Physical activities detection plays a vital role to healthcare professionals who would like to monitor patients remotely and to develop context-sensitive systems. Major number of physical activity detection systems use accelerometers to collect data from different parts of the body. Since those approaches have limitations from users' point of view, we have used smart phones that are coming with built-in accelerometers and gyroscopes. We have proposed and developed three novel approaches for activity recognition. Firstly, we have developed a multimodal system where we used pressure sensor data from shoes along with accelerometers and gyroscope data from smart phone. Again, we have presented the details of our novel activity detection system along with evaluation. In the second approach, we considered our sensor data as time series shapelets and apply recently developed algorithms to differentiate those shapelets. Finally, we applied Gaussian Mixture Models with time-delay embedding for detecting different activities.


Published as part of the proceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC), 2015: 44-49. DOI. © 2015 IEEE. Used with permission.