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2016 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2016
type: Information Content Entity
type: Generically Dependent Continuant
publication venue for: Detection and classification of intra boby acoustic wave signals for wearable applications
type: Continuant
type: Proceedings
type: Document
type: Book
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RDF description of Detection and classification of intra boby acoustic wave signals for wearable applications - http://orbis.uaslp.mx/vivo/individual/scopus7704
rights: termsOfUse
date: 2026-08-28T10:08:08
publisher: vivo
type: Document

Detection and classification of intra boby acoustic wave signals for wearable applications
type: Generically Dependent Continuant
related by: authorship_49070
type: Thing
type: Document
freetext keyword: acoustic features; bio acoustics; gesture recognition; human computer interaction; pattern recognition; short time energy; spectral centroid; spectral flux; wearable devices Acoustic transducers; Acoustic waves; Acoustics; Bayesian networks; Embedded systems; Gesture recognition; Human computer interaction; Neural networks; Pattern recognition; Wearable sensors; Wearable technology; Acoustic features; Short-time energy; spectral centroid; Spectral flux; Wearable devices; Signal detection
has publication venue: 2016 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2016
crisid: 7705
abstract: Wearable Devices (WD) are systems designed to do a specific task, these system are embedded in daily life personal objects. Usual transducers in wearable devices involve accelerometers gyroscopes, cameras etc. In WD is required to design efficiently in terms of power. Therefore, a new trend incorporates acoustic transducer that does not need a power supply to sense. They are very cheap and easy to replace. In WD, these transducers detect acoustic wave signals traveling in human tissue and bones. The processing of these kind of signals is related with many areas such as medical, gesture recognition, haptics etc. The proposed system uses a set of three sensors to capture Intra Body Acoustic Wave Signals (IBAWS) from the wrist of the right hand. A signal database is constructed using 18 users repeating 30 times each one of five gestures proposed. The patterns are composed by six features per sensor, including Spectral Flux, Spectral Centroid and Short Time Energy. Complete proposed patterns contains 18 features. Classifiers results shown 75.37%25 of accuracy using Bayesian classifier with Gaussian Kernel, 80%25 of accuracy using Knn classifier, and 85.56%25 of accuracy with Artificial Neural Networks. All this for a set of 18 users, supporting the hypothesis that classify IBAWS is independent from the user and could be generalized to use them in WD. © 2016 IEEE.
estado: published
related by: authorship_49067
related by: authorship_49064
related by: authorship_49066
related by: authorship_49069
most specific type: Document
artnum: 7830587
type: Entity
type: Conference Paper
related by: authorship_49071
type: Article
doi: 10.1109/ROPEC.2016.7830587
most specific type: Conference Paper
type: Continuant
origen: scopus
related by: authorship_49065
type: Information Content Entity
related by: authorship_49068
EI dscopus: 2-s2.0-85013847197
date time value: conference paper date 7705

Article
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Information Content Entity
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Conference Paper
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