Yang, Wen-ChiWen-ChiYangCHENG-YUAN LIOU2020-05-042020-05-042019https://scholars.lib.ntu.edu.tw/handle/123456789/487713https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073881088&doi=10.23919%2fSICE.2019.8859953&partnerID=40&md5=865d5edd48fe473078e1c152e8d14b10This paper presents a monitoring system to retrieve locomotion semantics based on a minimum configuration of devices. We designed a mechanism to encode the binary signals of few proximity sensors into semantic words that store locomotion information. Then, we fed these words to a non-negative matrix factorization (NMF) algorithm and derived basis vectors of clusters for the usage of online semantic retrieval. Through examining and analyzing the proposed system in a simulated fish pond, we demonstrated this simple system reached a significant level of accuracy and consistency on the categorization of locomotion semantics. The findings suggest that a real adaptive system based on the proposed framework would be feasible and useful in industrial fields. Thus, livestock farms, for example, can benefit from its effective performance with low production cost. © 2019 The Society of Instrument and Control Engineers - SICE.Collective Pattern; Crowd Analysis; Non-negative Matrix Factorization; Proximity Sensor; Semantic RetrievalAgriculture; Body sensor networks; Factorization; Fish ponds; Proximity sensors; Semantics; Collective motions; Collective Pattern; Crowd analysis; Effective performance; Monitoring system; Non-negative matrix factorization algorithms; Nonnegative matrix factorization; Semantic retrieval; Matrix algebraconference paper10.23919/SICE.2019.88599532-s2.0-85073881088https://doi.org/10.23919/SICE.2019.8859953