TY - JOUR
T1 - Automated Multicohort Mobility Assessment with an Instrumented L-test (iL-test)
AU - Albites-Sanabria, Jose
AU - Palumbo, Pierpaolo
AU - D'ascanio, Ilaria
AU - Bonci, Tecla
AU - Caruso, Marco
AU - Salis, Francesca
AU - Cereatti, Andrea
AU - Din, Silvia Del
AU - Alcock, Lisa
AU - Kuederle, Arne
AU - Paraschiv-Ionescu, Anisoara
AU - Gazit, Eran
AU - Kluge, Felix
AU - Kirk, Cameron
AU - Encarna Mico-Amigo, M.
AU - Scott, Kirsty
AU - Hansen, Clint
AU - Klenk, Jochen
AU - Schwickert, Lars
AU - Megaritis, Dimitrios
AU - Vogiatzis, Ioannis
AU - Becker, Clemens
AU - Maetzler, Walter
AU - Hausdorff, Jeff
AU - Caulfield, Brian
AU - Vereijken, Beatrix
AU - Rochester, Lynn
AU - Muller, Arne
AU - Mazza, Claudia
AU - Carpinella, Ilaria
AU - Bowman, Thomas
AU - Ciechi, Roberta De
AU - Torchio, Alessandro
AU - Cattaneo, Davide
AU - Bianchi, Simona
AU - Ferrarin, Maurizio
AU - Randi, Pericle
AU - Piraccini, Lucrezia
AU - Davalli, Angelo
AU - Chiari, Lorenzo
AU - Palmerini, Luca
N1 - Publisher Copyright:
© 2001-2011 IEEE.
PY - 2025
Y1 - 2025
N2 - The L-test is a performance-based measure to assess balance and mobility. Currently, the primary outcome from this test is the time required to finish it. In this study we present the instrumented L-test (iL-test), an L-test wherein mobility is evaluated by means of a wearable inertial sensor worn at the lower back. We analyzed data from 113 people across seven cohorts: healthy adults, chronic obstructive pulmonary disease, multiple sclerosis, congestive heart failure, Parkinson's disease, proximal femoral fracture, and transfemoral amputation. The iL-test automatic segmentation was validated using stereophotogrammetry. Univariate and multivariate analyses were performed on 164 kinematic features derived from inertial signals to identify distinct patterns across different cohorts. The iL-test accurately recognized and segmented activities during the L-test for all cohorts (technical validity). A random forest classifier revealed that proximal femoral fracture and transfemoral amputation induced significantly different mobility patterns compared to healthy people with AUC values of 0.89 and 0.99, respectively. Strong correlations were found between kinematic features and clinical scores in multiple sclerosis, congestive heart failure, proximal femoral fracture, and transfemoral amputation, with consistent patterns of decreased movement ranges and smoothness with increasing disease severity. Furthermore, features derived from 90° and 180° turns were found to be important contributors to differentiation amongst cohorts, underscoring the need to evaluate different turn degrees and directions. This study emphasizes the iL-test potential to deliver automated mobility assessment across a wide range of clinical conditions, indicating a prospective avenue for improved mobility assessment and, eventually, more informed healthcare interventions.
AB - The L-test is a performance-based measure to assess balance and mobility. Currently, the primary outcome from this test is the time required to finish it. In this study we present the instrumented L-test (iL-test), an L-test wherein mobility is evaluated by means of a wearable inertial sensor worn at the lower back. We analyzed data from 113 people across seven cohorts: healthy adults, chronic obstructive pulmonary disease, multiple sclerosis, congestive heart failure, Parkinson's disease, proximal femoral fracture, and transfemoral amputation. The iL-test automatic segmentation was validated using stereophotogrammetry. Univariate and multivariate analyses were performed on 164 kinematic features derived from inertial signals to identify distinct patterns across different cohorts. The iL-test accurately recognized and segmented activities during the L-test for all cohorts (technical validity). A random forest classifier revealed that proximal femoral fracture and transfemoral amputation induced significantly different mobility patterns compared to healthy people with AUC values of 0.89 and 0.99, respectively. Strong correlations were found between kinematic features and clinical scores in multiple sclerosis, congestive heart failure, proximal femoral fracture, and transfemoral amputation, with consistent patterns of decreased movement ranges and smoothness with increasing disease severity. Furthermore, features derived from 90° and 180° turns were found to be important contributors to differentiation amongst cohorts, underscoring the need to evaluate different turn degrees and directions. This study emphasizes the iL-test potential to deliver automated mobility assessment across a wide range of clinical conditions, indicating a prospective avenue for improved mobility assessment and, eventually, more informed healthcare interventions.
KW - mobility
KW - objective measurements
KW - wearable sensors
UR - http://www.scopus.com/inward/record.url?scp=85215957455&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2025.3531723
DO - 10.1109/TNSRE.2025.3531723
M3 - Artículo
AN - SCOPUS:85215957455
SN - 1534-4320
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
ER -