scholarly journals Motion Capture Technology in Industrial Applications: A Systematic Review

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5687
Author(s):  
Matteo Menolotto ◽  
Dimitrios-Sokratis Komaris ◽  
Salvatore Tedesco ◽  
Brendan O’Flynn ◽  
Michael Walsh
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Health And Safety ◽  
Industrial Applications ◽  
Machine Learning Algorithms ◽  
Industrial Processes ◽  
Application Study ◽  
First Choice ◽  
Study Characteristics ◽  
Measurement Units

The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

scholarly journals Wearables for Biomechanical Performance Optimization and Risk Assessment in Industrial and Sports Applications

2022 ◽  
Vol 9 (1) ◽  
pp. 33
Author(s):  
Sam McDevitt ◽  
Haley Hernandez ◽  
Jamison Hicks ◽  
Russell Lowell ◽  
Hamza Bentahaikt ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Performance Optimization ◽  
Performance Enhancement ◽  
Health And Safety ◽  
Industrial Applications ◽  
Data Types ◽  
Measurement Units ◽  
Numerous Data ◽  
And Performance ◽  
The Individual

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.

Systematic Calibration Method for FOG Inertial Measurement Units

2013 ◽  
Vol 662 ◽  
pp. 717-720 ◽  
Author(s):  
Zhen Yu Zheng ◽  
Yan Bin Gao ◽  
Kun Peng He
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Calibration Method ◽  
State Equation ◽  
Measurement Unit ◽  
Observation System ◽  
Body Frame ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Error Coefficients

As an inertial sensors assembly, the FOG inertial measurement unit (FIMU) must be calibrated before being used. The paper presents a one-time systematic IMU calibration method only using two-axis low precision turntable. First, the detail error model of inertial sensors using defined body frame is established. Then, only velocity taken as observation, system 33 state equation is established including the lever arm effects and nonlinear terms of scale factor error. The turntable experiments verify that the method can identify all the error coefficients of FIMU on low-precision two-axis turntable, after calibration the accuracy of navigation is improved.

Quantifying the Accuracy of a Motion Detecting Neural Prosthesis

2020 ◽  
Author(s):  
Martin L. Tanaka ◽  
Premkumar Subbukutti ◽  
David Hudson ◽  
Kimberly Hudson ◽  
Pablo Valenzuela ◽  
...  
Keyword(s):  
Motion Capture ◽  
Gait Cycle ◽  
Neural Prosthesis ◽  
Average Error ◽  
Camera Motion ◽  
Motion Capture System ◽  
General Shape ◽  
Neural Connections ◽  
Measurement Units ◽  
The Brain

Abstract The neural prosthesis under development is designed to improve gait in people with muscle weakness. The strategy is to augment impaired or damaged neural connections between the brain and the muscles that control walking. This third-generation neural prosthesis contains triaxial inertial measurement units (IMUs - accelerometers, gyroscopes, and processing chip) to measure body segment position and force sensitive resistors placed under the feet to detect ground contact. A study was conducted to compare the accuracy of the neural prosthesis using a traditional camera motion capture system as a reference. The IMUs were found to accurately represent the amplitude of the gait cycle components and generally track the motion. However, there are some differences in phase, with the IMUs lagging the actual motion. Phase lagged by about 10 degrees in the ankle and by about 5 degrees in the knee. Error of the neural prosthesis varied over the gait cycle. The average error for the ankle, knee and hip were 6°, 8°, and 9°, respectively. Testing showed that the neural prosthesis was able to capture the general shape of the joint angle curves when compared to a commercial camera motion capture system. In the future, measures will be taken to reduce lag in the gyroscope and reduce jitter in the accelerometer so that data from both sensors can be combination to obtain more accurate readings.

scholarly journals Nanotechnology: A Journey towards Finding Solutions

2015 ◽  
Vol 5 (1) ◽  
pp. 61
Author(s):  
Satya Pal Singh
Keyword(s):  
Health And Safety ◽  
Functional Materials ◽  
Simulation Method ◽  
Industrial Applications ◽  
Policy Makers ◽  
Advance Research ◽  
American Physical ◽  
Richard Feynman ◽  
And Control ◽  
Thin Film Magnetism

<p class="1Body">Nanotechnology is the understanding and control of matter at the diemnsions ranging between 1-100 nm. One nanometer is one billionth of a meter. Nanotechnology involves manipulation of atoms, imaging, measuring and modelling at nano scale. Its potentials were first highlighted by Richard Feynman in the American Physical Scociety meeting in 1959. Though, he did not coin the world nanotechnology himself but he explored the possiblities of functional materials at the bottom of the scale. In last two decades this technology has been commercialized to great extent and gaining importance day by day influencing the economies of different countries and henceforth enforcing the policy makers to address the issues like environment, health and safety. Governments are regularisaing and monitoring its research, uses, applications and technology transfer which includes intelluctaul property rights. This paper addresses the dimensions and trends of nanotechnology covering economic aspects. The paper is focussed on the changes in the functional properties of nanomaterials as physical, chemical, optical, electronic, electrical, magnetic etc. in comparision to those of the bulk of material. It has been discussed how the basic and advance research in nanoscience could be explotiedfor making technologies for its commercial and industrial applications for the benefit and safety of the soceity. Thin film magnetism is demonstrated using Monte Carlo simulation method. Experimental synthesisof some of thenanorods and qunatum dots are also discussed.</p>

Abnormalities Detection of IMU Based on PCA in Motion Monitoring

2012 ◽  
Vol 224 ◽  
pp. 533-538 ◽  
Author(s):  
Jing Zhou ◽  
Steven Su ◽  
Ai Huang Guo ◽  
Wei Dong Chen
Keyword(s):  
Measurement Method ◽  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Principal Component ◽  
Body Sensor Networks ◽  
Remote Measurement ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Multiple Degrees Of Freedom ◽  
Measurement Units

Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people’s daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling’s T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.

scholarly journals Screening For Yeast Phytase Leads to the Identification of a New Cell-Bound and Secreted Activity in Cyberlindnera jadinii CJ2

2021 ◽  
Vol 9 ◽  
Author(s):  
Claudia Capusoni ◽  
Immacolata Serra ◽  
Silvia Donzella ◽  
Concetta Compagno
Keyword(s):  
High Temperature ◽  
Phytic Acid ◽  
Convenient Method ◽  
Kluyveromyces Marxianus ◽  
Food Industry ◽  
Yeast Species ◽  
Industrial Applications ◽  
Industrial Processes ◽  
Acidic Ph ◽  
Initial Screening

Phytic acid is an anti-nutritional compound able to chelate proteins and ions. For this reason, the food industry is looking for a convenient method which allows its degradation. Phytases are a class of enzymes that catalyze the degradation of phytic acid and are used as additives in feed-related industrial processes. Due to their industrial importance, our goal was to identify new activities that exhibit best performances in terms of tolerance to high temperature and acidic pH. As a result of an initial screening on 21 yeast species, we focused our attention on phytases found in Cyberlindnera jadinii, Kluyveromyces marxianus, and Torulaspora delbrueckeii. In particular, C. jadinii showed the highest secreted and cell-bound activity, with optimum of temperature and pH at 50°C and 4.5, respectively. These characteristics suggest that this enzyme could be successfully used for feed as well as for food-related industrial applications.

A Mobile Motion Capture System Based on Inertial Sensors and Smart Shoes

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Pyeong-Gook Jung ◽  
Sehoon Oh ◽  
Gukchan Lim ◽  
Kyoungchul Kong
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Health Condition ◽  
Body Motion ◽  
Whole Body ◽  
Motion Capture System ◽  
Outdoor Sports ◽  
Motion Capture Systems

Motion capture systems play an important role in health-care and sport-training systems. In particular, there exists a great demand on a mobile motion capture system that enables people to monitor their health condition and to practice sport postures anywhere at any time. The motion capture systems with infrared or vision cameras, however, require a special setting, which hinders their application to a mobile system. In this paper, a mobile three-dimensional motion capture system is developed based on inertial sensors and smart shoes. Sensor signals are measured and processed by a mobile computer; thus, the proposed system enables the analysis and diagnosis of postures during outdoor sports, as well as indoor activities. The measured signals are transformed into quaternion to avoid the Gimbal lock effect. In order to improve the precision of the proposed motion capture system in an open and outdoor space, a frequency-adaptive sensor fusion method and a kinematic model are utilized to construct the whole body motion in real-time. The reference point is continuously updated by smart shoes that measure the ground reaction forces.

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