HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing | Zendy

Pratool Bharti | Zendy; Debraj De | Zendy; Sriram Chellappan | Zendy; Sajal K. Das | Zendy

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HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing

Author(s) -

Pratool Bharti,

Debraj De,

Sriram Chellappan,

Sajal K. Das

Publication year - 2018

Publication title -

ieee transactions on mobile computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.276

H-Index - 140

eISSN - 1558-0660

pISSN - 1536-1233

DOI - 10.1109/tmc.2018.2841905

Subject(s) - computer science , wearable computer , beacon , context (archaeology) , bluetooth , activity recognition , lying , modal , feature extraction , feature (linguistics) , artificial intelligence , set (abstract data type) , machine learning , pattern recognition (psychology) , human–computer interaction , data mining , real time computing , embedded system , wireless , telecommunications , chemistry , polymer chemistry , medicine , paleontology , linguistics , philosophy , radiology , programming language , biology

Current state-of-the-art systems in the literature using wearables are not capable of distinguishing a large number of fine-grained and/or complex human activities, which may appear similar but with vital differences in context, such as lying on floor versus lying on bed versus lying on sofa. This paper fills the gap by proposing a novel system, called HuMAn, that recognizes and classifies complex...

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