scholarly journals A New Calibration Methodology for Thorax and Upper Limbs Motion Capture in Children Using Magneto and Inertial Sensors

Sensors ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 1057-1072 ◽  
Author(s):  
Luca Ricci ◽  
Domenico Formica ◽  
Laura Sparaci ◽  
Francesca Lasorsa ◽  
Fabrizio Taffoni ◽  
...  
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Upper Limbs

scholarly journals Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 282 ◽  
Author(s):  
Giuliana Grimaldi ◽  
Mario Manto ◽  
Yassin Jdaoudi
Keyword(s):  
Upper Limb ◽  
Functional Disability ◽  
Inertial Sensors ◽  
Brain Computer Interface ◽  
Quality Parameters ◽  
Computer Interface ◽  
Upper Limbs ◽  
Original Algorithm ◽  
Daily Life Activities ◽  
New Treatment

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project “Tremor” (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.

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.

scholarly journals Comparative Analysis of Inertial Sensor to Optical Motion Capture System Performance in Push-Pull Exertion Postures

2016 ◽  
Vol 60 (1) ◽  
pp. 970-974 ◽  
Author(s):  
Sol Lim ◽  
Andrea Case ◽  
Clive D’Souza
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Interactive Effects ◽  
Flexion Angle ◽  
Task Demand ◽  
Optical Motion ◽  
Physical Task ◽  
Target Force ◽  
Optical Motion Capture

This study examined interactions between inertial sensor (IS) performance and physical task demand on posture kinematics in a two-handed force exertion task. Fifteen male individuals participated in a laboratory experiment that involved exerting a two-handed isometric horizontal force on an instrumented height-adjustable handle. Physical task demand was operationalized by manipulating vertical handle height, target force magnitude, and force direction. These factors were hypothesized to influence average estimates of torso flexion angle measured using inertial sensors and an optical motion capture (MC) system, as well as the root mean squared errors (RMSE) between instrumentation computed over a 3s interval of the force exertion task. Results indicate that lower handle heights and higher target force levels were associated with increased torso and pelvic flexion in both, push and pull exertions. Torso flexion angle estimates obtained from IS and MC did not differ significantly. However, RMSE increased with target force intensity suggesting potential interactive effects between measurement error and physical task demand.

scholarly journals Semi-Automating Low Back Compression Force Estimations with an Inertial Sensor

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 37
Author(s):  
Sam Gleadhill ◽  
Daniel James ◽  
Raymond Leadbetter ◽  
Tomohito Wada ◽  
Ryu Nagahara ◽  
...  
Keyword(s):  
Motion Capture ◽  
Injury Risk ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Three Dimensional ◽  
Compressive Force ◽  
Low Back ◽  
Compression Force ◽  
Injury Risk Factor ◽  
Capture Method

There are currently no evidence-based practical automated injury risk factor estimation tools to monitor low back compressive force in ambulatory or sporting environments. For this purpose, inertial sensors may potentially replace laboratory-based systems with comparable results. The objective was to investigate inertial sensor validity to monitor low back compression force. Thirty participants completed a series of lifting tasks from the floor. Back compression force was estimated using a hand calculated method, an inertial sensor method and a three-dimensional motion capture method. Results demonstrated that semi-automation with a sensor had a higher agreement with motion capture compared to the hand calculated method, with angle errors of less than six degrees and back compression force errors of less than 200 Newtons. It was concluded that inertial sensors are valid to implement for static low back compression force estimations.

scholarly journals A Determination Method for Gait Event Based on Acceleration Sensors

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5499 ◽  
Author(s):  
Chang Mei ◽  
Farong Gao ◽  
Ying Li
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Gait Recognition ◽  
Three Dimensional ◽  
High Standard ◽  
Vertical Displacement ◽  
Human Gait ◽  
Vertical Acceleration ◽  
Acceleration Signal ◽  
Acceleration Sensors

A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

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.

Human Arm Motion Tracking by Orientation-Based Fusion of Inertial Sensors and Kinect Using Unscented Kalman Filter

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Arash Atrsaei ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Kalman Filter ◽  
Motion Capture ◽  
Motion Tracking ◽  
Inertial Sensors ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Human Motion ◽  
Home Based ◽  
Human Arm ◽  
Arm Motion

Due to various applications of human motion capture techniques, developing low-cost methods that would be applicable in nonlaboratory environments is under consideration. MEMS inertial sensors and Kinect are two low-cost devices that can be utilized in home-based motion capture systems, e.g., home-based rehabilitation. In this work, an unscented Kalman filter approach was developed based on the complementary properties of Kinect and the inertial sensors to fuse the orientation data of these two devices for human arm motion tracking during both stationary shoulder joint position and human body movement. A new measurement model of the fusion algorithm was obtained that can compensate for the inertial sensors drift problem in high dynamic motions and also joints occlusion in Kinect. The efficiency of the proposed algorithm was evaluated by an optical motion tracker system. The errors were reduced by almost 50% compared to cases when either inertial sensor or Kinect measurements were utilized.

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