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035 _a.b12421649
099 _aAIT Thesis no.DSAI-22-02
100 0 _aSiraphat Boonchan
245 1 0 _aAnomaly detection in the home with seismic sensors
260 _aPathum Thani, Thailand :
_bAsian Institute of Technology,
_c2022
300 _a81 leaves :
_bill.
490 1 _aThesis ;
_vno. DSAI-22-02
500 _aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Data Science and Artificial Intelligence, School of Engineeing and Technology
502 _aThesis (M. Eng.) - Asian Institute of Technology, 2022
520 _aFalls are a global public health problem. Falls happen to people of all ages, especially on the elderly. Throughout the last decade, we have seen improvements in fall detection system due to technology development and the revolution of deep learning. However, using vibration signal analysis can compensate the weakness and also overcomes the drawbacks associated with the traditional system, and this is a novel idea that needs to be studied further. This thesis studies the embedded system and design space for unsupervised anomaly detection model using modern deep learning best practices. The performance and effectiveness of this system to immediately send alert message to user via LINE apllication when abnormal events occur. Accordingly, this study can help the home residents when an anomolous event or falling down event is occurring.
650 0 _aAnomaly detection (Computer security)
650 0 _aArtificial intelligence
_xData processing
650 0 _aDeep learning (Machine learning)
700 1 _aDailey, Matthew N.,
_eChairperson
700 0 _aMongkol Ekpanyapong,
_eExamination Committee
700 0 _aChaklam Silpasuwanchai,
_eExamination Committee
710 2 _aRoyal Thai Government Fellowship,
_eScholarship Donor
810 2 _aAsian Institute of Technology.
_tThesis ;
_vno. DSAI-22-02
856 4 0 _3Full-Text
_uhttp://203.159.5.9/ait-thesis/Viewer/viewer.php?id=B20415
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