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Abstract
The estimate of a consistent and clinically meaningful joint kinematics using wearable inertial and magnetic sensors requires a sensor-to-segment coordinate system calibration. State-of-the-art calibration procedures for the upper limb are based on functional movements and/or pre-determined postures, which are difficult to implement in subjects that have impaired mobility or are bedridden in acute units. The aim of this study was to develop and validate an alternative calibration procedure based on the direct identification of palpable anatomical landmarks (ALs) for an inertial and magnetic sensor-based upper limb movement analysis protocol. The proposed calibration procedure provides an estimate of three-dimensional shoulder/elbow angular kinematics and the linear trajectory of the wrist according to the standards proposed by the International Society of Biomechanics. The validity of the method was assessed against a camera-based optoelectronic system during uniaxial joint rotations and a reach-to-grasp task. Joint angular kinematics was found as characterised by a low-biased range of motion (<−2.6°), a low root mean square deviation (RMSD) (<4.4°) and a high waveform similarity coefficient (R2 > 0.995) with respect to the gold standard. Except for the cranio–caudal direction, the linear trajectory of the wrist was characterised by a low-biased range of motion (<11 mm) together with a low RMSD (8 mm) and high waveform similarity (R2 > 0.968). The proposed method enabled the estimation of reliable joint kinematics without requiring any active involvement of the patient during the calibration procedure, complying with the metrological standards and requirements of clinical movement analysis.
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1 School of Sport and Exercise Sciences, “e-Campus” University, Novedrate, Italia
2 Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italia
3 IRCCS Fondazione Don Carlo Gnocchi, Milano, Italia
4 Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italia; Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genoa, Genova, Italia
5 Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italia
6 Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Roma, Italia
7 Dipartimento di Scienze dell’invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Università Cattolica del Sacro Cuore, Roma, Italia
8 IRCCS Fondazione Don Carlo Gnocchi, Milano, Italia; Dipartimento di Scienze dell’invecchiamento, Neurologiche, Ortopediche e della Testa-Collo, Università Cattolica del Sacro Cuore, Roma, Italia
9 University of Sassari, Biomedical Sciences Department, Sassari, Italia