Fall risk probability estimation based on supervised feature learning using public fall datasets
2016 (English)In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Vol. 2016, 2016, 752-755 p., 7590811Conference paper (Refereed)
Risk of falling is considered among major threats for elderly population and therefore started to play an important role in modern healthcare. With recent development of sensor technology, the number of studies dedicated to reliable fall detection system has increased drastically. However, there is still a lack of universal approach regarding the evaluation of developed algorithms. In the following study we make an attempt to find publicly available fall datasets and analyze similarities among them using supervised learning. After preforming similarity assessment based on multidimensional scaling we indicate the most representative feature vector corresponding to each specific dataset. This vector obtained from a real-life data is subsequently deployed to estimate fall risk probabilities for a statistical fall detection model. Finally, we conclude with some observations regarding the similarity assessment results and provide suggestions towards an efficient approach for evaluation of fall detection studies.
Place, publisher, year, edition, pages
2016. 752-755 p., 7590811
IdentifiersURN: urn:nbn:se:mdh:diva-34725DOI: 10.1109/EMBC.2016.7590811ScopusID: 2-s2.0-85009115435ISBN: 9781457702204 (print)OAI: oai:DiVA.org:mdh-34725DiVA: diva2:1068733
38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016; Disney's Contemporary ResortOrlando; United States; 16 August 2016 through 20 August 2016;