Document Type

Conference Proceeding



Format of Original

9 p.

Publication Date



Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

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

Source ISSN



The aim of this paper is to present a multisensory system that studies abnormal walking patterns to prevent a fall. Due to the growing elderly population, scientific research on smartphone-based gait detection systems has recently become an imperative component in decreasing elderly injuries due to falls. To address the issue of smart gait detection, we propose a gait classification system using smartshoe sensor data in this paper. We used shoe-worn pressure sensors on the foot and validated algorithms to extract the gait parameters during walking trials in a lab environment. This smartshoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. To the best of our knowledge, this is the first system which can automatically detect abnormalities in walking patterns. A unique signal classification approach is presented by recognizing the abnormality in a subject's gait, and modeling the dynamics of a system as they are captured in a reconstructed phase space. Based on our experiments, we have found an 89% walking-based classification accuracy to help prevent falls.


Published as part of the proceedings of the conference, 2015 IEEE 39th Annual Computer Software and Applications Conference (COMPSAC), 2015: 733-741. DOI. © 2015 IEEE. Used with permission.