Rehabilitation posture correction using deep neural network

Cited 6 time in webofscience Cited 0 time in scopus
  • Hit : 253
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorHan, Seung-Hoko
dc.contributor.authorKim, Hangyuko
dc.contributor.authorChoi, Ho-Jinko
dc.date.accessioned2018-01-30T02:27:35Z-
dc.date.available2018-01-30T02:27:35Z-
dc.date.created2017-12-28-
dc.date.created2017-12-28-
dc.date.issued2017-02-13-
dc.identifier.citationIEEE International Conference on Big Data and Smart Computing (BigComp), pp.400 - 402-
dc.identifier.issn2375-933X-
dc.identifier.urihttp://hdl.handle.net/10203/238068-
dc.description.abstractThe rehabilitation treatment is important because it helps a patient restore physical sensory and mental capabilities. The patient whose symptoms are moderately relieved, or outpatient, usually rehabilitate the individual alone. Improper exercise or posture can slow the recovery of the patient or even worsen the patient's health status when doing rehabilitation exercise alone. The best way is to receive home visiting treatment from professional therapist until cured. However, such way is a burden on the patient in terms of cost. This paper proposes the novel model that corrects the improper postures of the patient when having rehabilitating exercise alone. We use Microsoft Kinect to recognize the posture of the patient by extracting the human skeleton. We will adopt deep neural network to analyze the extracted human skeleton, in order to determine whether the posture is correct or not. The data for training our model will be correct postures and incorrect postures and detailed data collection plan is provided in this paper. The implementation and experiment will be performed in the future work.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleRehabilitation posture correction using deep neural network-
dc.typeConference-
dc.identifier.wosid000403390900067-
dc.identifier.scopusid2-s2.0-85017586364-
dc.type.rimsCONF-
dc.citation.beginningpage400-
dc.citation.endingpage402-
dc.citation.publicationnameIEEE International Conference on Big Data and Smart Computing (BigComp)-
dc.identifier.conferencecountryKO-
dc.identifier.conferencelocationJeju Island-
dc.identifier.doi10.1109/BIGCOMP.2017.7881743-
dc.contributor.localauthorChoi, Ho-Jin-
Appears in Collection
CS-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 6 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0