SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

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A survey of data fusion in smart city applications (2019, methodology): “Also, similar study on the user daily activity patterns can be found in [110].”
A Survey on Activity Detection and Classification Using Wearable Sensors (2017, unknown): “[148] use a webcam,”
Elderly activities recognition and classification for applications in assisted living (2013, methodology): “Firstly, Fleury et al. (2010) used multiple sensors inputs (on-body and environment ) and SVM based classification to detect seven ADLs: sleeping, preparing and having meal, dressing/undressing, hygiene activities (wash hand and teeth), bowel movement, communication.”
Data Mining for Internet of Things: A Survey (2014, methodology): “Another study [142] divided the application domain into society, industry, and environment based on the activities of the IoT on the kind of events, such as where it will be used and who will use it.”
A Tensor-Based Information Framework for Predicting the Stock Market (2016, background): “Examples include detecting moving objects in video data [Cucchiara et al. 2003], context-aware mobile recommendations [Oku et al. 2006], and health care monitoring [Fleury et al. 2010].”
The Sweet-Home speech and multimodal corpus for home automation interaction (2014, background): “, 2005) as in Ambient Assisted Living domain for behaviour monitoring in health applications (Fleury et al., 2010).”