hh.sePublikasjoner
Endre søk
Begrens søket
1 - 2 of 2
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Ražanskas, Petras
    et al.
    Department of Electric Power Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Electrical Power Systems, Kaunas University of Technology, Lithuania.
    Viberg, Per-Arne
    Swedish Adrenaline, Halmstad, Sweden.
    Olsson, Charlotte M.
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS).
    Predicting physiological parameters in fatiguing bicycling exercises using muscle activation timing2017Inngår i: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, s. 19-29Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article is concerned with a novel technique for prediction of blood lactate concentration level and oxygen uptake rate from multi-channel surface electromyography (sEMG) signals. The approach is built on predictive models exploiting a set of novel time-domain variables computed from sEMG signals. Signals from three muscles of each leg, namely, vastus lateralis, rectus femoris, and semitendinosus were used in this study. The feature set includes parameters reflecting asymmetry between legs, phase shifts between activation of different muscles, active time percentages, and sEMG amplitude. Prediction ability of both linear and non-linear (random forests-based) models was explored. The random forests models showed very good prediction accuracy and attained the coefficient of determination R2 = 0.962 for lactate concentration level and R2 = 0.980 for oxygen uptake rate. The linear models showed lower prediction accuracy. Comparable results were obtained also when sEMG amplitude data were removed from the training sets. A feature elimination algorithm allowed to build accurate random forests (R2 > 0.9) using just six (lactate concentration level) or four (oxygen uptake rate) time-domain variables. Models created to predict blood lactate concentration rate relied on variables reflecting interaction between front and back leg muscles, while parameters computed from front muscles and interactions between two legs were the most important variables for models created to predict oxygen uptake rate.© 2017 Elsevier Ltd.

  • 2.
    Viteckova, Slavka
    et al.
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Khandelwal, Siddhartha
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Kutilek, Patrik
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Krupicka, Radim
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Szabo, Zoltan
    Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
    Gait symmetry methods: Comparison of waveform-based Methods and recommendation for use2020Inngår i: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 55, artikkel-id 101643Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Gait symmetry has been shown to be a relevant measure for differentiating between normal and pathological gait. Although a number of symmetry methods exist, it is not clear which of these methods should be used as they have been developed using data collected from varying experimental protocols. This paper presents a comparison of state-of-the-art waveform-based symmetry methods and tests them on walking data collected from different environments. Acceleration signals collected from the ankle are used to analyse symmetry methods under different signal circumstances, such as phase shift, waveform shape difference, signal length (i.e. number of gait cycles) and gait initiation phase. The cyclogram based method is invariant to signal phase shifts, signal length and the gait initiation phase. The trend symmetry method is not affected by signal scaling and the gait initiation phase but is affected by signal length depending on the environment. Similar to the trend method, the cross-correlation symmetry method is not responsive to signal scaling and the gait initiation phase. The results of the symbolic method are not influenced by signal scaling, gait initiation and depending on the environment by the signal phase shift. From the results of the performed analysis, we recommend the trend method to gait symmetry assessment. The comparison of waveform-based symmetry methods brings new knowledge that will help in selecting an appropriate method for gait symmetry assessment under different experimental protocols. © 2019 Elsevier Ltd. All rights reserved.

1 - 2 of 2
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf