ACCURATE ESTIMATION OF UPPER LIMB ORTHOSIS WEAR TIME USING MINIATURE TEMPERATURE LOGGERS.
Objective: To propose and validate a new method for estimating upper limb orthosis wear time using miniature temperature loggers attached to locations on the upper body. Design: Observational study. Subjects: Fifteen healthy participants. Methods: Four temperature loggers were attached to the arm an...
| Publicado en: | Journal of Rehabilitation Medicine (Medical Journals Sweden AB) Vol. 54; pp. 1 - 11 |
|---|---|
| Autores principales: | , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Medical Journals Sweden AB
2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161431812&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161431812 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16501977 AWMH jtl: Journal of Rehabilitation Medicine (Medical Journals Sweden AB) issn: 16501977 maglogo: N pubinfo: dt: 2022 vid: 54 pid: 59195 pub: Medical Journals Sweden AB artinfo: ui: 161431812 161431812 161431812 10.2340/jrm.v54.43 161431812 ppf: 1 ppct: 10 formats: tig: atl: ACCURATE ESTIMATION OF UPPER LIMB ORTHOSIS WEAR TIME USING MINIATURE TEMPERATURE LOGGERS. aug: au: HAARMAN, Claudia J. W. HEKMAN, Edsko E. G. RIETMAN, Johan S. van der KOOIJ, Herman affil: Department of Biomechanical Engineering, University of Twente, Enschede, The Netherlands sug: subj: Upper Extremity Orthoses Utilization Wearable Sensors Temperature Patient Compliance Evaluation Human Female Male Adult Middle Age Aged Nonexperimental Studies Mobile Applications Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Objective: To propose and validate a new method for estimating upper limb orthosis wear time using miniature temperature loggers attached to locations on the upper body. Design: Observational study. Subjects: Fifteen healthy participants. Methods: Four temperature loggers were attached to the arm and chest with straps. Participants were asked to remove and re-attach the straps at specified time-points. The labelled temperature data obtained were used to train a decision tree classification algorithm to estimate wear time. The final performance (mean error and 95% confidence interval) of the trained classifier and the wear time estimation were assessed with a hold-out data-set. Results: The trained algorithm can correctly classify unseen temperature data with a mean classification error between 1.1% and 3.1% for the arm, and between 1.8% and 4.0% for the chest, depending on the sampling time of the temperature logger. This resulted in mean wear time errors between 0.5% and 8.3% for the arm, and 0.13% and 13.0% for the chest. Conclusion: The proposed method based on a classifier can accurately estimate upper limb orthosis wear time. This method could enable healthcare professionals to gain insight into the wear time of any upper limb orthosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|