Vi Do Tran1, Paolo Dario2, Stefano Mazzoleni3. 1. The BioRobotics Institute, Scuola Superiore Sant'Anna, Polo Sant'Anna Valdera, V.le R. Piaggio 34-56025 Pontedera, Italy; Rehabilitation Bioengineering Laboratory, Volterra, Italy. 2. The BioRobotics Institute, Scuola Superiore Sant'Anna, Polo Sant'Anna Valdera, V.le R. Piaggio 34-56025 Pontedera, Italy. 3. The BioRobotics Institute, Scuola Superiore Sant'Anna, Polo Sant'Anna Valdera, V.le R. Piaggio 34-56025 Pontedera, Italy; Rehabilitation Bioengineering Laboratory, Volterra, Italy. Electronic address: stefano.mazzoleni@santannapisa.it.
Abstract
AIM OF THE STUDY: This review classifies the kinematic measures used to evaluate post-stroke motor impairment following upper limb robot-assisted rehabilitation and investigates their correlations with clinical outcome measures. METHODS: An online literature search was carried out in PubMed, MEDLINE, Scopus and IEEE-Xplore databases. Kinematic parameters mentioned in the studies included were categorized into the International Classification of Functioning, Disability and Health (ICF) domains. The correlations between these parameters and the clinical scales were summarized. RESULTS: Forty-nine kinematic parameters were identified from 67 articles involving 1750 patients. The most frequently used parameters were: movement speed, movement accuracy, peak speed, number of speed peaks, and movement distance and duration. According to the ICF domains, 44 kinematic parameters were categorized into Body Functions and Structure, 5 into Activities and no parameters were categorized into Participation and Personal and Environmental Factors. Thirteen articles investigated the correlations between kinematic parameters and clinical outcome measures. Some kinematic measures showed a significant correlation coefficient with clinical scores, but most were weak or moderate. CONCLUSIONS: The proposed classification of kinematic measures into ICF domains and their correlations with clinical scales could contribute to identifying the most relevant ones for an integrated assessment of upper limb robot-assisted rehabilitation treatments following stroke. Increasing the assessment frequency by means of kinematic parameters could optimize clinical assessment procedures and enhance the effectiveness of rehabilitation treatments.
AIM OF THE STUDY: This review classifies the kinematic measures used to evaluate post-stroke motor impairment following upper limb robot-assisted rehabilitation and investigates their correlations with clinical outcome measures. METHODS: An online literature search was carried out in PubMed, MEDLINE, Scopus and IEEE-Xplore databases. Kinematic parameters mentioned in the studies included were categorized into the International Classification of Functioning, Disability and Health (ICF) domains. The correlations between these parameters and the clinical scales were summarized. RESULTS: Forty-nine kinematic parameters were identified from 67 articles involving 1750 patients. The most frequently used parameters were: movement speed, movement accuracy, peak speed, number of speed peaks, and movement distance and duration. According to the ICF domains, 44 kinematic parameters were categorized into Body Functions and Structure, 5 into Activities and no parameters were categorized into Participation and Personal and Environmental Factors. Thirteen articles investigated the correlations between kinematic parameters and clinical outcome measures. Some kinematic measures showed a significant correlation coefficient with clinical scores, but most were weak or moderate. CONCLUSIONS: The proposed classification of kinematic measures into ICF domains and their correlations with clinical scales could contribute to identifying the most relevant ones for an integrated assessment of upper limb robot-assisted rehabilitation treatments following stroke. Increasing the assessment frequency by means of kinematic parameters could optimize clinical assessment procedures and enhance the effectiveness of rehabilitation treatments.
Authors: Christoph M Kanzler; Mike D Rinderknecht; Anne Schwarz; Ilse Lamers; Cynthia Gagnon; Jeremia P O Held; Peter Feys; Andreas R Luft; Roger Gassert; Olivier Lambercy Journal: NPJ Digit Med Date: 2020-05-29
Authors: Camila L A Gomes; Roberta O Cacho; Viviane T B Nobrega; Ellen Marjorie de A Confessor; Eyshila Emanuelle M de Farias; José Leôncio F Neto; Denise S de Araújo; Ana Loyse de S Medeiros; Rodrigo L Barreto; Enio W A Cacho Journal: Biomed Eng Online Date: 2020-03-04 Impact factor: 2.819