The literature has yielded promising data over the past decade regarding the use of inertial sensors for the analysis of occupational ergonomics. However, despite their significant advantages (e.g., portability, lightness, low cost, etc.), their widespread implementation in the actual workplace has not yet been realized, possibly due to their discomfort or potential alteration of the worker's behaviour. This systematic review has two main objectives: (i) to synthesize and evaluate studies that have employed inertial sensors in ergonomic analysis based on the RULA method; and (ii) to propose an evaluation system for the transparency of this technology to the user as a potential factor that could influence the behaviour and/or movements of the worker. A search was conducted on the Web of Science and Scopus databases. The studies were summarized and categorized based on the type of industry, objective, type and number of sensors used, body parts analysed, combination (or not) with other technologies, real or controlled environment, and transparency. A total of 17 studies were included in this review. The Xsens MVN system was the most widely used in this review, and the majority of studies were classified with a moderate level of transparency. It is noteworthy, however, that there is a limited and worrisome number of studies conducted in uncontrolled real environments.
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http://dx.doi.org/10.3390/s24010298 | DOI Listing |
The inertial element of a solid block is commonly used as the proof mass in traditional accelerometers. However, it is challenging to accommodate both the high-density solid-state proof mass and the highly elastic component simultaneously in a miniature sensor, which makes it difficult for the sensors to maintain comparable sensing performance at a miniaturized size. Here, a novel, to the best of our knowledge, liquid metal-based fiber optic accelerometer (LMFOA) is proposed for the first time to meet this requirement.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Mechanical Engineering, Faculty of Engineering, Urmia University, Urmia, Iran.
This study investigates the nonlinear dynamics of a system with frequency-dependent stiffness using a MEMS-based capacitive inertial sensor as a case study. The sensor is positioned directly on a rotating component of a machine and consists of a microbeam clamped at both ends by fixed supports with a fixed central proof mass. The nonlinear behavior is determined by electrostatic forces, axial and bending motion coupling, and frequency-dependent stiffness.
View Article and Find Full Text PDFGait Posture
January 2025
Department of Neurology, Oregon Health & Science University, Portland, OR, United States. Electronic address:
Background: Gait impairments are common in individuals with mild traumatic brain injury (mTBI), presenting in the acute phase and often persisting in subtle ways over time. Despite the prominence of laboratory gait evaluations, a comprehensive understanding of gait deficits post-mTBI necessitates the examination of various gait domains in real-world environments. Assessing gait during a community ambulation task (CAT) may capture real-world challenges and influence focused interventions or rehabilitation in individuals with mTBI.
View Article and Find Full Text PDFWearable Technol
November 2024
Department of Kinesiology, Iowa State University, Ames, IA, USA.
Placing an inertial measurement unit (IMU) at the 5th lumbar vertebra (L5) is a frequently employed method to assess the whole-body center of mass (CoM) motion during walking. However, such a fixed position approach does not account for instantaneous changes in body segment positions that change the CoM. Therefore, this study aimed to assess the congruence between CoM accelerations obtained from these two methods.
View Article and Find Full Text PDFCommun Med (Lond)
January 2025
Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Leibniz ScienceCampus Primate Cognition and German Center for Child and Adolescent Health (DZKJ), Göttingen, Germany.
Background: To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent costs in the training of human assessors and further standardize classification of spontaneous motor patterns. Available deep learning tools, all of which are based on single sensor modalities, are however still considerably inferior to that of well-trained human assessors.
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