2020 Deep Learning for Sensor-based Human ActivityRecognition Overview, Challenges and Opportunities

2020 Deep Learning for Sensor-based Human ActivityRecognition: Overview, Challenges and Opportunities

Authors:
KAIXUAN CHEN∗,University of New South Wales, Australia
DALIN ZHANG∗,University of New South Wales, Australia
LINA YAO,University of New South Wales, Australia
BIN GUO,Northwestern Polytechnical University, China
ZHIWEN YU,Northwestern Polytechnical University, China
YUNHAO LIU,Michigan State University, US

HAR领域的挑战：

feature extraction: Different activities may have similar characteristics (e.g.,walking and running). Therefore, it is difficult to produce distinguishable features to representactivities uniquely.
annotation scarcity: it is expensive and time-consuming to collect and annotate sensory activity data.
class imbalance: data for some emergent or unexpected activities (e.g., accid

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2020 Deep Learning for Sensor-based Human ActivityRecognition Overview, Challenges and Opportunities

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