Abstract
Many health professionals do not use correct person transfer techniques in their daily practice. This results in damage to the paraspinal musculature over time, resulting in lower back pain and injuries. In this work, we propose an approach for the accurate multimodal measurement of people lifting and related motion patterns for ergonomic education regarding the application of correct patient transfer techniques. Several examples of person lifting were recorded and processed through accurate instrumentation and the well-defined measurements of kinematics, kinetics, surface electromyography of muscles as well as multicamera video. This resulted in a complete measurement protocol and unique reference datasets of correct and incorrect lifting schemes for caregivers and patients. This understanding of multimodal motion patterns provides insights for further independent investigations.
Highlights
The problem of maintaining correct body posture while performing lifting activities is important in many professional fields
This task requires physical exertion for prolonged periods daily. It often results in musculoskeletal disorders (MSDs) or low-back spinal disorders (LBDs) [1]
(The SENIAM project (Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles) is a European concerted action in the Biomedical Health and Research Program (BIOMED II) of the European Union), recommendations were made for each specific movement phase
Summary
The problem of maintaining correct body posture while performing lifting activities is important in many professional fields It concerns healthcare personnel and other professionals who help people on a daily basis. Instead of using a human dummy when simulating lifting patients, full measurements of the real person being lifted were recorded for the analysis of the patient’s kinetic and kinematic data. Sensors 2021, 21, 3142 of Motion Tags [5], we present a simple equivalent of original data emphasizing the most important features of the phases of patient lifting in multiperson interaction. Motion Tags represent important information on a more abstract level and allow for efficient automation of the assessment between correct and incorrect movements. The main attention was focused on the work of the hips, back, knees, and selected muscle tensions
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