Exploring the use of tangible user interfaces for human-robot interaction: a comparative study

By Cheng Guo, Ehud Sharlin. International Conference on Human Factors in Computing Systems, 2008

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Large-Scale Personalized Human Activity Recognition Using Online Multitask Learning (2013, methodology): “After that, we will briefly review conditional random fields (CRFs) and online training, which are related to our work.”
Discovering important people and objects for egocentric video summarization (2012, background): “detection [1], or activity discovery for non-visual sensor y data [10].”
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition (2013, methodology): “The Darmstadt routine dataset –used to study unsupervised activity pattern discovery (Huynh et al., 2008)– is a long recording from body activity collected by the Porcupine system (Van Laerhoven et al.”
Drone & me: an exploration into natural human-drone interaction (2015, background): “Guo et al. [5] show people feel comfortable using a variety of interaction techniques.”
Deep Learning for Sensor-based Human Activity Recognition (2020, background): “ency is usually in the range of tens to hundreds of Hz. For recognizing human activities, accelerometers can be mounted on various parts of a body, such as the waist [7], arm [173], ankle [10], wrist [61], et al. There are three axes in an often-used accelerometer. Therefore, a tri-variate time seri”
Fast unsupervised ego-action learning for first-person sports videos (2011, background): “working at the office, eating dinner) [4] and segmenting a signal into primitive units [9].”