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Clinical Features to Predict the Use of a sEMG Wearable Device (REMO ® ) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study

Author

Listed:
  • Giorgia Pregnolato

    (Laboratory of Healthcare Innovation Technology, IRCCS San Camillo Hospital, Via Alberoni 70, 30126 Venice, Italy)

  • Daniele Rimini

    (Medical Physics Department, Salford Care Organisation, Northern Care Alliance, Salford M6 8HD, UK
    Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University Of Manchester, Manchester M13 9PL, UK)

  • Francesca Baldan

    (FisioSPORT Terraglio s.r.l., 30174 Venezia, Italy)

  • Lorenza Maistrello

    (Laboratory of Healthcare Innovation Technology, IRCCS San Camillo Hospital, Via Alberoni 70, 30126 Venice, Italy)

  • Silvia Salvalaggio

    (Laboratory of Healthcare Innovation Technology, IRCCS San Camillo Hospital, Via Alberoni 70, 30126 Venice, Italy
    Padova Neuroscience Center, Università degli Studi di Padova, Via Orus 2/B, 35131 Padova, Italy)

  • Nicolò Celadon

    (Morecognition s.r.l., 10129 Turin, Italy)

  • Paolo Ariano

    (Morecognition s.r.l., 10129 Turin, Italy
    Artificial Physiology Group, Center for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Via Livorno 60, 10144 Torino, Italy)

  • Candido Fabrizio Pirri

    (Artificial Physiology Group, Center for Sustainable Future Technologies, Istituto Italiano di Tecnologia, Via Livorno 60, 10144 Torino, Italy
    Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy)

  • Andrea Turolla

    (Department of Biomedical and Neuromotor Sciences—DIBINEM, Alma Mater Studiorum Università di Bologna, Via Massarenti, 9, 40138 Bologna, Italy
    Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Via Pelagio Palagi, 9, 40138 Bologna, Italy)

Abstract

After stroke, upper limb motor impairment is one of the most common consequences that compromises the level of the autonomy of patients. In a neurorehabilitation setting, the implementation of wearable sensors provides new possibilities for enhancing hand motor recovery. In our study, we tested an innovative wearable (REMO ® ) that detected the residual surface-electromyography of forearm muscles to control a rehabilitative PC interface. The aim of this study was to define the clinical features of stroke survivors able to perform ten, five, or no hand movements for rehabilitation training. 117 stroke patients were tested: 65% of patients were able to control ten movements, 19% of patients could control nine to one movement, and 16% could control no movements. Results indicated that mild upper limb motor impairment (Fugl-Meyer Upper Extremity ≥ 18 points) predicted the control of ten movements and no flexor carpi muscle spasticity predicted the control of five movements. Finally, severe impairment of upper limb motor function (Fugl-Meyer Upper Extremity > 10 points) combined with no pain and no restrictions of upper limb joints predicted the control of at least one movement. In conclusion, the residual motor function, pain and joints restriction, and spasticity at the upper limb are the most important clinical features to use for a wearable REMO ® for hand rehabilitation training.

Suggested Citation

  • Giorgia Pregnolato & Daniele Rimini & Francesca Baldan & Lorenza Maistrello & Silvia Salvalaggio & Nicolò Celadon & Paolo Ariano & Candido Fabrizio Pirri & Andrea Turolla, 2023. "Clinical Features to Predict the Use of a sEMG Wearable Device (REMO ® ) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study," IJERPH, MDPI, vol. 20(6), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:6:p:5082-:d:1096395
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