measurement units
Recently Published Documents

Daily life activities often require humans to perform locomotion in challenging scenarios. In this context, this study aimed at investigating the effects induced by anterior-posterior (AP) and medio-lateral (ML) perturbations on walking. Through this aim, the experimental protocol involved 12 participants who performed three tasks on a treadmill consisting of one unperturbed and two perturbed walking tests. Inertial measurement units were used to gather lower limb kinematics. Parameters related to joint angles, as the range of motion (ROM) and its variability (CoV), as well as the inter-joint coordination in terms of continuous relative phase (CRP) were computed. The AP perturbation seemed to be more challenging causing differences with respect to normal walking in both the variability of the ROM and the CRP amplitude and variability. As ML, only the ankle showed different behavior in terms of joint angle and CRP variability. In both tasks, a shortening of the stance was found. The findings should be considered when implementing perturbed rehabilitative protocols for falling reduction.

Abstract Background Motor vehicle collisions are a common cause of death and serious injury. Many casualties will remain in their vehicle following a collision. Trapped patients have more injuries and are more likely to die than their untrapped counterparts. Current extrication methods are time consuming and have a focus on movement minimisation and mitigation. The optimal extrication strategy and the effect this extrication method has on spinal movement is unknown. The aim of this study was to evaluate the movement at the cervical and lumbar spine for four commonly utilised extrication techniques. Methods Biomechanical data was collected using inertial Measurement Units on 6 healthy volunteers. The extrication types examined were: roof removal, b-post rip, rapid removal and self-extrication. Measurements were recorded at the cervical and lumbar spine, and in the anteroposterior (AP) and lateral (LAT) planes. Total movement (travel), maximal movement, mean, standard deviation and confidence intervals are reported for each extrication type. Results Data from a total of 230 extrications were collected for analysis. The smallest maximal and total movement (travel) were seen when the volunteer self-extricated (AP max = 2.6 mm, travel 4.9 mm). The largest maximal movement and travel were seen in rapid extrication extricated (AP max = 6.21 mm, travel 20.51 mm). The differences between self-extrication and all other methods were significant (p < 0.001), small non-significant differences existed between roof removal, b-post rip and rapid removal. Self-extrication was significantly quicker than the other extrication methods (mean 6.4 s). Conclusions In healthy volunteers, self-extrication is associated with the smallest spinal movement and the fastest time to complete extrication. Rapid, B-post rip and roof off extrication types are all associated with similar movements and time to extrication in prepared vehicles.

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

Wearable technologies are emerging as a useful tool with many different applications. While these devices are worn on the human body and can capture numerous data types, this literature review focuses specifically on wearable use for performance enhancement and risk assessment in industrial- and sports-related biomechanical applications. Wearable devices such as exoskeletons, inertial measurement units (IMUs), force sensors, and surface electromyography (EMG) were identified as key technologies that can be used to aid health and safety professionals, ergonomists, and human factors practitioners improve user performance and monitor risk. IMU-based solutions were the most used wearable types in both sectors. Industry largely used biomechanical wearables to assess tasks and risks wholistically, which sports often considered the individual components of movement and performance. Availability, cost, and adoption remain common limitation issues across both sports and industrial applications.

This study developed and evaluated a novel concurrent biofeedback system for the sprint start. Previous studies have investigated sprint start biofeedback applications, but these have either not considered important kinematics, coaching implications or key motor learning principles. The biofeedback system was developed to convey rear knee angle information, obtained from 3D motion capture to novice participants as changes in the colour of an LED start line when they were in the “set” position. Based on initial user feedback, the system indicated whether the participants’ rear knee angles were within ± 2° of 130° (green) or not (red). A two-group experimental study was then employed to explore the acute responses of novices to the use of the biofeedback system during the sprint start. When exposed to biofeedback, the experimental group (EXP, n = 10) exhibited less deviation (4.0 ± 2.4°) from the target rear knee angle than they did in either a pre-test (11.9 ± 6.9°) or post-test (10.4 ± 4.4°) condition without biofeedback. The control group (CON, n = 10) with no biofeedback exhibited greater deviation from the target rear knee angle than the EXP group in all three condition blocks (pre-test = 21.8 ± 15.1°, no intervention = 15.6 ± 7.3°, post-test = 14.3 ± 6.5°) but the group × condition interaction effect was not significant (P = 0.210). The novel biofeedback system can be used to manipulate selected “set” position kinematics and has the potential to be incorporated with different input systems (e.g. inertial measurement units (IMUs)) or in longitudinal designs.

Common in-lab, marker-based gait analyses may not represent daily, real-world gait. Real-world gait analyses may be feasible using inertial measurement units (IMUs), especially with recent advancements in open-source methods (e.g., OpenSense). Before using OpenSense to study real-world gait, we must determine whether these methods: (1) estimate joint kinematics similarly to traditional marker-based motion capture (MoCap) and (2) differentiate groups with clinically different gait mechanics. Healthy young and older adults and older adults with knee osteoarthritis completed this study. We captured MoCap and IMU data during overground walking at self-selected and faster speeds. MoCap and IMU kinematics were computed with appropriate OpenSim workflows. We tested whether sagittal kinematics differed between MoCap- and IMU-derived data, whether tools detected between-group differences similarly, and whether kinematics differed between tools by speed. MoCap data showed more flexion than IMU data (hip: 0-47 and 65-100% stride, knee: 0-38 and 58-91% stride, ankle: 18-100% stride). Group kinematics differed at the hip (young extension > knee osteoarthritis at 30-47% stride) and ankle (young plantar flexion > older healthy at 62-65% stride). Group-by-tool interactions occurred at the hip (61-63% stride). Significant tool-by-speed interactions were found, with hip and knee flexion increasing more for MoCap than IMU data with speed (hip: 12-15% stride, knee: 60-63% stride). While MoCap- and IMU-derived kinematics differed, our results suggested that the tools similarly detected clinically meaningful differences in gait. Results of the current study suggest that IMU-derived kinematics with OpenSense may enable the valid and reliable evaluation of gait in real-world, unobserved settings.

Musculoskeletal disorders in the workplace are a growing problem in Europe. The measurement of these disorders in a working environment presents multiple limitations concerning equipment and measurement reliability. The aim of this study was to evaluate the use of inertial measurement units against a reference system for their use in the workplace. Ten healthy volunteers conducted three lifting methods (snatching, pushing, and pulling) for manhole cover using a custom-made tool weighting 20 and 30 kg. Participants’ back and dominant arm were equipped with IMU, EMG, and reflective markers for VICON analysis and perception of effort was estimated at each trial using a Visual Analog Scale (VAS). The Bland–Altman method was used and results showed good agreement between IMU and VICON systems for Yaw, Pitch and Roll angles (bias values < 1, −4.4 < LOA < 3.6°). EMG results were compared to VAS results and results showed that both are a valuable means to assess efforts during tasks. This study therefore validates the use of inertial measurement units (IMU) for motion capture and its combination with electromyography (EMG) and a Visual Analogic Scale (VAS) to assess effort for use in real work situations.

Inertial measurement units (IMUs) fixed to the lower limbs have been reported to provide accurate estimates of stride lengths (SLs) during walking. Due to technical challenges, validation of such estimates in running is generally limited to speeds (well) below 5 m·s−1. However, athletes sprinting at (sub)maximal effort already surpass 5 m·s−1 after a few strides. The present study aimed to develop and validate IMU-derived SLs during maximal linear overground sprints. Recreational athletes (n = 21) completed two sets of three 35 m sprints executed at 60, 80, and 100% of subjective effort, with an IMU on the instep of each shoe. Reference SLs from start to ~30 m were obtained with a series of video cameras. SLs from IMUs were obtained by double integration of horizontal acceleration with a zero-velocity update, corrected for acceleration artefacts at touch-down of the feet. Peak sprint speeds (mean ± SD) reached at the three levels of effort were 7.02 ± 0.80, 7.65 ± 0.77, and 8.42 ± 0.85 m·s−1, respectively. Biases (±Limits of Agreement) of SLs obtained from all participants during sprints at 60, 80, and 100% effort were 0.01% (±6.33%), −0.75% (±6.39%), and −2.51% (±8.54%), respectively. In conclusion, in recreational athletes wearing IMUs tightly fixed to their shoes, stride length can be estimated with reasonable accuracy during maximal linear sprint acceleration.