TY - GEN
T1 - Preliminary Validation of an IMU-Based Physiotherapy Assessment System for the Lower Extremities
AU - Bonfiglio, Alessandro
AU - Petruccelli, Cecilia
AU - Villa, Giacomo
AU - Bongers, Raoul M.
AU - Farella, Elisabetta
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - Physiotherapy assessments traditionally rely on the clinician’s interpretation to evaluate musculoskeletal conditions. However, technology, such as Inertial Measurement Units (IMU), is increasingly used to assist medical professionals. This study evaluates the accuracy and reliability of the Euleria Lab (ELAB) rehabilitation system against an optical motion capture system (OPTO). 7 healthy volunteers were instrumented with 5 IMU and 22 retroreflective markers and performed lower limbs single-plane and multi-plane movements. Joint angles were used to compute Range of Motion (ROM), Root Mean Squared Error (RMSE), Bland-Altman plots and intraclass correlation coefficient (ICC). ROM and RMSE were analysed using two RM ANOVA. In multi-plane tasks, ankle, knee and hip angles were compared using Hotelling’s T2 statistical parametric mapping (SPM) test. No significant differences were found between the two systems for ROM and between tasks in terms of RMSE. However, hip rotation showed large RMSE and poor ICC reliability. Hip flexion and abduction showed good agreement and a systematic bias = 10°. Multi-joint tasks revealed significant differences only in hip flexion during lunges. Therefore, ELAB proved highly accurate and reliable for the assessment of the movements: ankle, knee, and trunk flexion, but demonstrated poor accuracy and agreement during the hip rotation movement. ELAB displayed significant biases but good agreement and minor errors during hip flexion and abduction.
AB - Physiotherapy assessments traditionally rely on the clinician’s interpretation to evaluate musculoskeletal conditions. However, technology, such as Inertial Measurement Units (IMU), is increasingly used to assist medical professionals. This study evaluates the accuracy and reliability of the Euleria Lab (ELAB) rehabilitation system against an optical motion capture system (OPTO). 7 healthy volunteers were instrumented with 5 IMU and 22 retroreflective markers and performed lower limbs single-plane and multi-plane movements. Joint angles were used to compute Range of Motion (ROM), Root Mean Squared Error (RMSE), Bland-Altman plots and intraclass correlation coefficient (ICC). ROM and RMSE were analysed using two RM ANOVA. In multi-plane tasks, ankle, knee and hip angles were compared using Hotelling’s T2 statistical parametric mapping (SPM) test. No significant differences were found between the two systems for ROM and between tasks in terms of RMSE. However, hip rotation showed large RMSE and poor ICC reliability. Hip flexion and abduction showed good agreement and a systematic bias = 10°. Multi-joint tasks revealed significant differences only in hip flexion during lunges. Therefore, ELAB proved highly accurate and reliable for the assessment of the movements: ankle, knee, and trunk flexion, but demonstrated poor accuracy and agreement during the hip rotation movement. ELAB displayed significant biases but good agreement and minor errors during hip flexion and abduction.
KW - Biomechanics
KW - IMU
KW - Physical assessments
KW - Physiotherapy
KW - Rehabilitation
UR - https://www.scopus.com/pages/publications/105003908550
U2 - 10.1007/978-3-031-85572-6_9
DO - 10.1007/978-3-031-85572-6_9
M3 - Conference contribution
AN - SCOPUS:105003908550
SN - 9783031855719
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 142
EP - 158
BT - Pervasive Computing Technologies for Healthcare - 18th EAI International Conference, PervasiveHealth 2024, Proceedings
A2 - Kondylakis, Haridimos
A2 - Triantafyllidis, Andreas
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024
Y2 - 17 September 2024 through 18 September 2024
ER -