scholarly journals Instrumented shoulder functional assessment using inertial measurement units for frozen shoulder

2021 ◽  
Author(s):  
Ting-Yang Lu ◽  
Kai-Chun Liu ◽  
Chia-Yeh Hsieh ◽  
Chih-Ya Chang ◽  
Yu Tsao ◽  
...  
Keyword(s):  
Functional Assessment ◽  
Frozen Shoulder ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

scholarly journals Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 106
Author(s):  
Chih-Ya Chang ◽  
Chia-Yeh Hsieh ◽  
Hsiang-Yun Huang ◽  
Yung-Tsan Wu ◽  
Liang-Cheng Chen ◽  
...  
Keyword(s):  
Assessment Tool ◽  
Frozen Shoulder ◽  
Quantitative Information ◽  
Assessment Tools ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
And Performance ◽  
Task Segmentation ◽  
Task Identification

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

scholarly journals Inertial measurement units to estimate drag forces and power output during standardised wheelchair tennis coast-down and sprint tests

2021 ◽  
pp. 1-19
Author(s):  
Thomas Rietveld ◽  
Barry S. Mason ◽  
Victoria L. Goosey-Tolfrey ◽  
Lucas H. V. van der Woude ◽  
Sonja de Groot ◽  
...  
Keyword(s):  
Power Output ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Drag Forces ◽  
Measurement Units

scholarly journals Measurement of respiratory rate with inertial measurement units

2020 ◽  
Vol 6 (3) ◽  
pp. 237-240
Author(s):  
Simon Beck ◽  
Bernhard Laufer ◽  
Sabine Krueger-Ziolek ◽  
Knut Moeller
Keyword(s):  
Air Pollution ◽  
Respiratory Rate ◽  
Frequency Analysis ◽  
Clinical Settings ◽  
Breathing Rate ◽  
Demographic Changes ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Respiratory Parameters ◽  
Measurement Units

AbstractDemographic changes and increasing air pollution entail that monitoring of respiratory parameters is in the focus of research. In this study, two customary inertial measurement units (IMUs) are used to measure the breathing rate by using quaternions. One IMU was located ventral, and one was located dorsal on the thorax with a belt. The relative angle between the quaternion of each IMU was calculated and compared to the respiratory frequency obtained by a spirometer, which was used as a reference. A frequency analysis of both signals showed that the obtained respiratory rates vary slightly (less than 0.2/min) between the two systems. The introduced belt can analyse the respiratory rate and can be used for surveillance tasks in clinical settings.

scholarly journals Camera and inertial sensor fusion for the PnP problem: algorithms and experimental results

2021 ◽  
Vol 32 (4) ◽  
Author(s):  
Luigi D’Alfonso ◽  
Emanuele Garone ◽  
Pietro Muraca ◽  
Paolo Pugliese
Keyword(s):  
Pose Estimation ◽  
Good Accuracy ◽  
Inertial Sensor ◽  
Experimental Tests ◽  
Inertial Measurement Units ◽  
Magnetic Vector ◽  
Inertial Measurement ◽  
The Earth ◽  
Measurement Units ◽  
Position And Orientation

AbstractIn this work, we face the problem of estimating the relative position and orientation of a camera and an object, when they are both equipped with inertial measurement units (IMUs), and the object exhibits a set of n landmark points with known coordinates (the so-called Pose estimation or PnP Problem). We present two algorithms that, fusing the information provided by the camera and the IMUs, solve the PnP problem with good accuracy. These algorithms only use the measurements given by IMUs’ inclinometers, as the magnetometers usually give inaccurate estimates of the Earth magnetic vector. The effectiveness of the proposed methods is assessed by numerical simulations and experimental tests. The results of the tests are compared with the most recent methods proposed in the literature.

scholarly journals Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1804
Author(s):  
Jorge Posada-Ordax ◽  
Julia Cosin-Matamoros ◽  
Marta Elena Losa-Iglesias ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Laura Esteban-Gonzalo ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
High Speeds ◽  
Controlled Conditions ◽  
Gait Parameters ◽  
Measurement Units ◽  
Accuracy And Repeatability

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

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