Trunk Flexion-Extension in Healthy Subjects: Preliminary Analysis of Movement Profiles
Contributo in Atti di convegno
Data di Pubblicazione:
2022
Citazione:
Trunk Flexion-Extension in Healthy Subjects: Preliminary Analysis of Movement Profiles / C. Amici, V. Cappellini, F. Ragni, R. Formicola, A. Borboni, B. Piovanelli, S. Negrini, G. Candiani (MECHANISMS AND MACHINE SCIENCE). - In: New Trends in Medical and Service Robotics / [a cura di] G. Rauter, G. Carbone, P.C. Cattin, A. Zam, D. Pisla, R. Riener. - [s.l] : Springer, 2022. - ISBN 978-3-030-76146-2. - pp. 155-163 (( Intervento presentato al 7. convegno International Workshop on New Trends in Medical and Service Robotics (MESROB) tenutosi a on line nel 2021 [10.1007/978-3-030-76147-9_17].
Abstract:
The importance of investigating trunk flexion-extension movements to assess the subject's health state is well established in clinical practice. Several diagnostic tools are currently available, which mainly ground on subjective evaluations, or quantitative but invasive methods (e.g. radiography). Because of technological constraints, non-invasive instruments, like optoelectronic acquisition systems with passive skin optical markers, still provide data affected by not-negligible artefacts. Besides, an effective analysis should involve movements performed by the subject at a self-imposed velocity, introducing potential inter-and intra-subject variability in data. This paper presents the preliminary analysis of the movement profile of passive skin optical markers during a trunk flexion-extension task, based on a parametric identification process. According to the Asymmetric Gaussian Functions (AGFs) optimization strategy, an optimization procedure for the fitting of markers' spatial displacement with different Gaussian functions is proposed and applied to a dataset of 29 healthy subjects, for 59 exercises. With the primary aim of investigating strength points and limits of single-and multi-components AGFs models as fitting functions, a descriptive statistical analysis is performed for both the methods on the fitting performance in different conditions, and for the single-component AGF case, on the estimated parametric coefficients as well.
Tipologia IRIS:
03 - Contributo in volume
Elenco autori:
C. Amici, V. Cappellini, F. Ragni, R. Formicola, A. Borboni, B. Piovanelli, S. Negrini, G. Candiani
Link alla scheda completa:
Titolo del libro:
New Trends in Medical and Service Robotics