Change search
Refine search result
1 - 5 of 5
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Ji, Guangchao
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Björlin, Anders
    Kiwok AB.
    Östlund, Anders
    Kiwok AB.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    Lindecrantz, Kaj
    KTH-School of Technology and Health.
    Textile-Electronic Integration in Wearable Measurement Garments for Pervasive Healthcare Monitoring2015Conference paper (Other academic)
  • 2.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Ji, Guangchao
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    A knitted garment using intarsia technique for Heart Rate Variability biofeedback: Evaluation of initial prototype2015In: Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015, p. 3121-3124Conference paper (Refereed)
  • 3.
    Abtahi, Farhad
    et al.
    KTH-School of Technology and Health.
    Lu, Ke
    KTH-School of Technology and Health.
    Guangchao, Li
    KTH-School of Technology and Health.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Seoane, Fernando
    University of Borås, Faculty of Caring Science, Work Life and Social Welfare. KTH-School of Technology and Health.
    A Knitted Garment using Intarsia Technique for Heart Rate Variability Biofeedback: Evaluation of Initial Prototype.2015Conference paper (Other academic)
  • 4. Brown, Shannon
    et al.
    Ortiz-Catalan, Max
    Chalmers University of Technology.
    Petersson, Joel
    University of Borås, Faculty of Textiles, Engineering and Business.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Seoane, Fernando
    University of Borås, Faculty of Textiles, Engineering and Business. KTH-School of Technology and Health.
    Intarsia-sensorized band and textrodes for real-time myoelectric pattern recognition2016In: Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, Institute of Electrical and Electronics Engineers (IEEE) , 2016, p. 6074-6077Conference paper (Refereed)
    Abstract [en]

    Surface Electromyography (sEMG) has applications in prosthetics, diagnostics and neuromuscular rehabilitation. Self-adhesive Ag/AgCl are the electrodes preferentially used to capture sEMG in short-term studies, however their long-term application is limited. In this study we designed and evaluated a fully integrated smart textile band with electrical connecting tracks knitted with intarsia techniques and knitted textile electrodes. Real-time myoelectric pattern recognition for motor volition and signal-to-noise ratio (SNR) were used to compare its sensing performance versus the conventional Ag-AgCl electrodes. After a comprehending measurement and performance comparison of the sEMG recordings, no significant differences were found between the textile and the Ag-AgCl electrodes in SNR and prediction accuracy obtained from pattern recognition classifiers.

  • 5. Brown, Shannon
    et al.
    Ortiz-Catalan, Max
    Chalmers University of Technology.
    Petersson, Joel
    University of Borås, Faculty of Textiles, Engineering and Business. Högskolan i Borås.
    Rödby, Kristian
    University of Borås, Faculty of Textiles, Engineering and Business.
    Seoane, Fernando
    University of Borås, Faculty of Textiles, Engineering and Business. KTH-School of Technology and Health.
    Intarsia-Sensorized Band and Textrodes for the Acquisition of Myoelectric Signals2016In: The Second International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, International Academy, Research and Industry Association (IARIA) , 2016, p. 14-19, article id 2_10_80013Conference paper (Refereed)
    Abstract [en]

    Surface Electromyography (sEMG) has applications in prosthetics, diagnostics and neuromuscular rehabilitation, and has been an increasing area of study. This study attempts to use a fully integrated smart textile band with electrical connecting tracks knitted with intarsia techniques to evaluate the quality of sEMG acquired by knitted textile electrodes. Myoelectric pattern recognition for motor volition and signal-to-noise ratio (SNR) were used to compare its sensing performance versus the conventional Ag-AgCl electrodes. Overall no significant differences were found between the textile and the Ag-AgCl electrodes in SNR and prediction accuracy obtained from pattern recognition classifiers. On average the textile electrodes produced a high prediction accuracy, >97% across all movements, which is equivalent to the accuracy obtained with conventional gel electrodes (Ag-AgCl). Furthermore the SNR for the Maximum Voluntary Contraction did not differ considerably between the textile and the Ag-AgCl electrodes.

1 - 5 of 5
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf