Cvetković Božidara, Janko Vito, Romero Alfonso E, Kafalı Özgür, Stathis Kostas, Luštrek Mitja
Jožef Stefan Institue, Jamova cesta 39, Slovenia.
Jožef Stefan International Postgraduate School, Jamova cesta 39, Slovenia.
J Med Syst. 2016 Dec;40(12):256. doi: 10.1007/s10916-016-0598-y. Epub 2016 Oct 8.
Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines machine-learning and symbolic reasoning to recognise high-level lifestyle activities using sensor data obtained primarily from the patient's smartphone. We compare five methods for machine-learning which differ in the amount of manually labelled data by the user, to investigate the trade-off between the labelling effort and recognition accuracy. In an evaluation on real-life data, the highest accuracy of 83.4 % was achieved by the MCAT method, which is capable of gradually adapting to each user.
糖尿病是一种必须通过适当的生活方式来管理的疾病。技术可以在这方面提供帮助，特别是当它的设计不会给患者带来额外负担时。本文提出了一种将机器学习和符号推理相结合的方法，利用主要从患者智能手机获取的传感器数据来识别高水平的生活方式活动。我们比较了五种机器学习方法，这些方法在用户手动标注数据量上有所不同，以研究标注工作量和识别准确率之间的权衡。在对现实生活数据的评估中，MCAT方法实现了83.4%的最高准确率，该方法能够逐渐适应每个用户。
