journal article·2024-08-09Open access

Kinematic IMU-Based Assessment of Postural Transitions: A Preliminary Application in Clinical Context

Cinzia Amici, Joel Pollet, Giorgia Ranica, Roberto Bussola, Riccardo Buraschi

Applied Sciences· Volume 14· Issue 16

Abstract

This study aims to develop a new methodology for assessing postural transitions, such as sit-to-stand movements, and to preliminarily apply it in a clinical setting. These movements provide valuable information about the state of movement effector system components, whether musculoskeletal, nervous, or cognitive, and their evaluation is a key point in the functional assessment in the clinical setting of patients with complex rehabilitative needs. The objective of this study was developed by pursuing three goals: verifying the ability to discriminate between healthy and pathological subjects, defining a set of parameters for movement assessment, and thus designing a preliminary evaluation paradigm for future clinical applications. We investigated the signals from a single IMU sensor applied to subjects (20 healthy and 13 patients) performing five different postural transitions. A set of six kinematic variables that allowed a quantitative assessment of motion was identified, namely total time, smoothness, fluency, velocity, jerk root mean square, and maximum jerk variation. At the end of the study, the adopted methodology and set of parameters were shown to be able to quantitatively assess postural transitions in a clinical context and to be able to distinguish healthy subjects from pathological subjects. This, together with future studies, will provide researchers and clinicians with a valuable resource for evaluating the results of a rehabilitation program, as well as for keeping track of patients’ functional status in follow-up evaluations.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Musculoskeletal pain and rehabilitation · Fibromyalgia and Chronic Fatigue Syndrome Research · Occupational Health and Performance

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical and Industrial Engineering, University of Brescia, 25123 Brescia, Italy

IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy

Funding

Italian Ministry of Health—Ricerca Corrente 2023

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