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Lookup NU author(s): Dr Silvia Del DinORCiD, Dr Lisa AlcockORCiD, Dr Cameron KirkORCiD, Dr Encarna Mico Amigo, Dr Dimitris Megaritis, Professor Lynn RochesterORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Albites-Sanabria J, Palumbo P, D'Ascanio I, Bonci T, Caruso M, Salis F, Cereatti A, Del Din S, Alcock L, Kuederle A, Paraschiv-Ionescu A, Gazit E, Kluge F, Kirk C, Micó-Amigo ME, Scott K, Hansen C, Klenk J, Schwickert L, Megaritis D, Vogiatzis I, Becker C, Maetzler W, Hausdorff J, Caulfield B, Vereijken B, Rochester L, Muller A, Mazzà C, Carpinella I, Bowman T, De Ciechi R, Torchio A, Cattaneo D, Bianchi S, Ferrarin M, Randi P, Piraccini L, Davalli A, Chiari L, Palmerini L
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Systems & Rehabilitation Engineering
Year: 2025
Volume: 33
Pages: 717-727
Online publication date: 20/01/2025
Acceptance date: 20/01/2025
Date deposited: 23/01/2025
ISSN (print): 1534-4320
ISSN (electronic): 1558-0210
Publisher: IEEE
URL: https://doi.org/10.1109/TNSRE.2025.3531723
DOI: 10.1109/TNSRE.2025.3531723
