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Quantitative Approach Based on Wearable Inertial Sensors to Assess and Identify Motion and Errors in Techniques Used during Training of Transfers of Simulated c-Spine-Injured Patients

Patients with suspected spinal cord injuries undergo numerous transfers throughout treatment and care. Effective c-spine stabilization is crucial to minimize the impacts of the suspected injury. Healthcare professionals are trained to perform those transfers using simulation; however, the feedback o...

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Detalles Bibliográficos
Autores principales: Lebel, Karina, Chenel, Vanessa, Boulay, John, Boissy, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5859832/
https://www.ncbi.nlm.nih.gov/pubmed/29692881
http://dx.doi.org/10.1155/2018/5190693
Descripción
Sumario:Patients with suspected spinal cord injuries undergo numerous transfers throughout treatment and care. Effective c-spine stabilization is crucial to minimize the impacts of the suspected injury. Healthcare professionals are trained to perform those transfers using simulation; however, the feedback on the manoeuvre is subjective. This paper proposes a quantitative approach to measure the efficacy of the c-spine stabilization and provide objective feedback during training. Methods. 3D wearable motion sensors are positioned on a simulated patient to capture the motion of the head and trunk during a training scenario. Spatial and temporal indicators associated with the motion can then be derived from the signals. The approach was developed and tested on data obtained from 21 paramedics performing the log-roll, a transfer technique commonly performed during prehospital and hospital care. Results. In this scenario, 55% of the c-spine motion could be explained by the difficulty of rescuers to maintain head and trunk alignment during the rotation part of the log-roll and their difficulty to initiate specific phases of the motion synchronously. Conclusion. The proposed quantitative approach has the potential to be used for personalized feedback during training sessions and could even be embedded into simulation mannequins to provide an innovative training solution.