Smartphone Sensor-Based Human Activity Recognition Using Feature Fusion and Maximum Full a Posteriori

Human activity recognition (HAR) using smartphone sensors has attracted great attention due to its wide range of applications. A standard solution for HAR is to first generate some features defined based on domain knowledge ( handcrafted features) and then to train an activity classification model b...

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Veröffentlicht in:	IEEE transactions on instrumentation and measurement 2020-07, Vol.69 (7), p.3992-4001
Hauptverfasser:	Chen, Zhenghua, Jiang, Chaoyang, Xiang, Shili, Ding, Jie, Wu, Min, Li, Xiaoli
Format:	Artikel
Sprache:	eng
Schlagworte:	Acceleration Algorithms Data collection Datasets Deep learning Feature extraction feature fusion Feature recognition Frequency-domain analysis Gyroscopes Heuristic algorithms Human activity recognition Human activity recognition (HAR) Inference algorithms Machine learning maximum full <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">a posteriori (MFAP) Moving object recognition smartphone sensors Smartphones
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