scholarly journals Instrumented Ergonomic Risk Assessment Using Wearable Inertial Measurement Units: Impact of Joint Angle Convention

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Humadi ◽  
Milad Nazarahari ◽  
Rafiq Ahmad ◽  
Hossein Rouhani
Keyword(s):  
Risk Assessment ◽  
Joint Angle ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Ergonomic Risk Assessment ◽  
Ergonomic Risk

scholarly journals Allumo: Preprocessing and Calibration Software for Wearable Accelerometers Used in Posture Tracking

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 229 ◽  
Author(s):  
Alexis Fortin-Côté ◽  
Jean-Sébastien Roy ◽  
Laurent Bouyer ◽  
Philip Jackson ◽  
Alexandre Campeau-Lecours
Keyword(s):  
Joint Angle ◽  
Calibration Procedure ◽  
Battery Life ◽  
Inertial Measurement Units ◽  
Ecological Studies ◽  
Ecological Value ◽  
Inertial Measurement ◽  
Long Duration ◽  
Measurement Units ◽  
Wearable Accelerometers

Inertial measurement units have recently shown great potential for the accurate measurement of joint angle movements in replacement of motion capture systems. In the race towards long duration tracking, inertial measurement units increasingly aim to ensure portability and long battery life, allowing improved ecological studies. Their main advantage over laboratory grade equipment is their usability in a wider range of environment for greater ecological value. For accurate and useful measurements, these types of sensors require a robust orientation estimation that remains accurate over long periods of time. To this end, we developed the Allumo software for the preprocessing and calibration of the orientation estimate of triaxial accelerometers. This software has an automatic orientation calibration procedure, an automatic erroneous orientation-estimate detection and useful visualization to help process long and short measurement periods. These automatic procedures are detailed in this paper, and two case studies are presented to showcase the usefulness of the software. The Allumo software is open-source and available online.

scholarly journals A Comparative Study of Markerless Systems Based on Color-Depth Cameras, Polymer Optical Fiber Curvature Sensors, and Inertial Measurement Units: Towards Increasing the Accuracy in Joint Angle Estimation

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 173 ◽  
Author(s):  
Nicolas Valencia-Jimenez ◽  
Arnaldo Leal-Junior ◽  
Leticia Avellar ◽  
Laura Vargas-Valencia ◽  
Pablo Caicedo-Rodríguez ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Correlation Coefficient ◽  
Joint Angle ◽  
Vision System ◽  
Inertial Measurement Units ◽  
Polymer Optical Fiber ◽  
Angle Estimation ◽  
Inertial Measurement ◽  
Non Invasive ◽  
Measurement Units

This paper presents a comparison between a multiple red green blue-depth (RGB-D) vision system, an intensity variation-based polymer optical fiber (POF) sensor, and inertial measurement units (IMUs) for human joint angle estimation and movement analysis. This systematic comparison aims to study the trade-off between the non-invasive feature of a vision system and its accuracy with wearable technologies for joint angle measurements. The multiple RGB-D vision system is composed of two camera-based sensors, in which a sensor fusion algorithm is employed to mitigate occlusion and out-range issues commonly reported in such systems. Two wearable sensors were employed for the comparison of angle estimation: (i) a POF curvature sensor to measure 1-DOF angle; and (ii) a commercially available IMUs MTw Awinda from Xsens. A protocol to evaluate elbow joints of 11 healthy volunteers was implemented and the comparison of the three systems was presented using the correlation coefficient and the root mean squared error (RMSE). Moreover, a novel approach for angle correction of markerless camera-based systems is proposed here to minimize the errors on the sagittal plane. Results show a correlation coefficient up to 0.99 between the sensors with a RMSE of 4.90 ∘ , which represents a two-fold reduction when compared with the uncompensated results (10.42 ∘ ). Thus, the RGB-D system with the proposed technique is an attractive non-invasive and low-cost option for joint angle assessment. The authors envisage the proposed vision system as a valuable tool for the development of game-based interactive environments and for assistance of healthcare professionals on the generation of functional parameters during motion analysis in physical training and therapy.

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy (Preprint)

2021 ◽  
Author(s):  
Siavash Khaksar ◽  
Huizhu Pan ◽  
Iain Murray ◽  
Wanquan Liu ◽  
Himanshu Agrawal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Palsy ◽  
Joint Angle ◽  
Wrist Joint ◽  
Hand Movement ◽  
Machine Learning Algorithms ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Machine Learning Classification ◽  
Measurement Units

BACKGROUND Cerebral palsy (CP) is a physical disability that affects movement and posture. About 17 million people worldwide and 34000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and position in children with CP. OBJECTIVE This paper presents collaborative research between department of Electrical Engineering and Computing at Curtin University and the investigator team of a multi-centre randomised controlled trial involving children with CP. The main objective of this paper was to develop a digital solution for mass data collection and application of machine learning to classify the movement features associated with CP without the need to measure Euler, Quaternion, and joint measurement calculation and help determine the effectiveness of therapy. METHODS Custom, low-cost Inertial Measurement Units (IMUs) were developed to record the usual wrist movements of participants aged 5 to 15 years old with CP. The IMU data were used to calculate the joint angle of the wrist movement to determine the range of motion. Nine different machine learning algorithms were used to classify the movement features associated with CP. RESULTS Upon completion of the project, the wrist joint angle was successfully calculated, and CP movement was classified as a feature using machine learning on raw IMU data, with Random Forrest algorithm showing the highest accuracy at 85.75%. CONCLUSIONS Anecdotal feedback from MIT researchers were positive about the potential for IMUs to contribute accurate data about active ROM, especially in children for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movement throughout the day. CLINICALTRIAL The trial is registered with the ANZ Clinical Trials Registry (ACTRN12614001276640).

Sign in / Sign up

Export Citation Format

Share Document