Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data

• Main Authors: Li, Kenan (Author), Habre, Rima (Author), Deng, Huiyu (Author), Urman, Robert (Author), Morrison, John (Author), Gilliland, Frank D (Author), Ambite, José Luis (Author), Stripelis, Dimitris (Author), Chiang, Yao-Yi (Author), Lin, Yijun (Author), Bui, Alex AT (Author), King, Christine (Author), Hosseini, Anahita (Author), Vliet, Eleanne Van (Author), Sarrafzadeh, Majid (Author), Eckel, Sandrah P (Author)
• Format: Knjiga
• Izdano: JMIR Publications, 2019-02-01T00:00:00Z.
• Teme: article
• Online dostop: Connect to this object online.

BackgroundTime-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activit...