Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation.

This paper is focused on the multimodal analysis of patient performance, carried out by means of robotic technology and wearable sensors, and aims at providing quantitative measure of biomechanical and motion planning features of arm motor control following rehabilitation. Upper-limb robotic therapy...

Descripción completa

Detalles Bibliográficos
Publicado en:Medical & Biological Engineering & Computing Vol. 49; no. 10; pp. 1131 - 1145
Autores principales: Zollo L, Rossini L, Bravi M, Magrone G, Sterzi S, Guglielmelli E, Zollo, Loredana, Rossini, Luca, Bravi, Marco, Magrone, Giovanni, Sterzi, Silvia, Guglielmelli, Eugenio
Formato: clinical trial research Journal Article
Publicado: Springer Nature Oct2011
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=104588304&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 104588304
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        01400118
        PO0
      jtl: Medical & Biological Engineering & Computing
      issn: 01400118
      maglogo: N
    pubinfo:
      dt: Oct2011
      vid: 49
      iid: 10
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        104588304
        NLM21792622
        2011318959
        10.1007/s11517-011-0808-1
        NLM21792622
        104588304
      ppf: 1131
      ppct: 14
      formats:
        fmt:
          @attributes:
            type: P
      tig:
        atl: Quantitative evaluation of upper-limb motor control in robot-aided rehabilitation.
      aug:
        au:
          Zollo L
          Rossini L
          Bravi M
          Magrone G
          Sterzi S
          Guglielmelli E
          Zollo, Loredana
          Rossini, Luca
          Bravi, Marco
          Magrone, Giovanni
          Sterzi, Silvia
          Guglielmelli, Eugenio
        affil: Laboratory of Biomedical Robotics and Biomicrosystems, Università Campus Bio-Medico, Rome, Italy
      sug:
        subj:
          Robotics Methods
          Stroke Rehabilitation
          Upper Extremity Physiopathology
          Adult
          Aged
          Aged, 80 and Over
          Female
          Human
          Male
          Middle Age
          Hemiplegia Physiopathology
          Hemiplegia Rehabilitation
          Physical Therapy Equipment and Supplies
          Recovery
          Robotics Equipment and Supplies
          Stroke Physiopathology
          Treatment Outcomes
          Clinical Trials
          Adult: 19-44 years
          Aged: 65+ years
          Aged, 80 & over
          Middle Aged: 45-64 years
          Female
          Male
      ab: This paper is focused on the multimodal analysis of patient performance, carried out by means of robotic technology and wearable sensors, and aims at providing quantitative measure of biomechanical and motion planning features of arm motor control following rehabilitation. Upper-limb robotic therapy was administered to 24 community-dwelling persons with chronic stroke. Performance indices on patient motor performance were computed from data recorded with the InMotion2 robotic machine and a magneto-inertial sensor. Motor planning issues were investigated by means of techniques of motion decomposition into submovements. A linear regression analysis was carried out to study correlation with clinical scales. Robotic outcome measures showed a significant improvement of kinematic motor performance; improvement of dynamic components was more significant in resistive motion and highly correlated with MP. The analysis of motion decomposition into submovements showed an important change with recovery of submovement number, amplitude and order, tending to patterns measured in healthy subjects. Preliminary results showed that arm biomechanical functions can be objectively measured by means of the proposed set of performance indices. Correlation with MP is high, while correlation with FM is moderate. Features related to motion planning strategies can be extracted from submovement analysis.
      pubtype: Academic Journal
      doctype:
        clinical trial
        research
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N