scholarly journals Simulation of Gymnastics Performance Based on MEMS Sensor

2021 ◽  
Author(s):  
Bingxin Chen ◽  
Lifei Kuang ◽  
Wei He
Keyword(s):  
Measurement Errors ◽  
Inertial Sensor ◽  
Tracking System ◽  
Median Filter ◽  
Three Dimensional ◽  
Sensor Data ◽  
Human Model ◽  
Dynamic Link Library ◽  
Virtual Character ◽  
Mems Sensor

Abstract The development and progress of multi-sensor data fusion theory and method also lay the foundation for the research of human posture tracking system based on inertial sensor. This paper mainly studies the simulation of gymnastic performance based on MEMS sensors. In the preprocessing of reducing noise interference, this paper mainly uses median filter to remove signal burr. In this paper, the use of virtual character model for gymnastics performance. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation and coordinate offset of the limb where each sensor node is located, and realizes the real-time rendering of the virtual human model by using the driver of the human model. At the same time, it controls the storage of sensor data, the driving of model and the display of graphical interface. When the gesture action is about to happen, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of MEMS sensor. When the gesture action is completed, a signal to end the action is given to the system to mark the end of the action, so that the original signal data between the beginning and end of the gesture action can be captured. In order to ensure the normal communication between PS and PL, it is necessary to test the key interface. Because the data received by the SPI acquisition module is irregular, it is unable to verify whether the data is wrong. Therefore, the SPI acquisition module is replaced with an automatic incremental data module, and it is generated into an IP core to build a test platform for testing. The data show that the average measurement errors of x-axis displacement, Y-axis displacement, z-axis displacement and three-dimensional displacement are 8.17%, 7.51%, 9.72% and 8.7%, respectively. The results show that the MEMS sensor can accurately identify the action with high accuracy.

scholarly journals Simulation of gymnastics performance based on MEMS sensor

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Bingxin Chen ◽  
Lifei Kuang ◽  
Wei He
Keyword(s):  
Measurement Error ◽  
Dimensional Space ◽  
Tracking System ◽  
Three Dimensional ◽  
Sensor Data ◽  
Average Error ◽  
Dynamic Link Library ◽  
Virtual Character ◽  
Main Research ◽  
Mems Sensor

AbstractThe development and progress of multi-sensor data fusion theory and methods have also laid the foundation for the research of human body posture tracking system based on inertial sensing. The main research in this paper is the simulation of gymnastics performance based on MEMS sensors. In the preprocessing to reduce noise interference, this paper mainly uses median filtering to remove signal glitches. This article uses virtual character models for gymnastics performances. The computer receives sensor data from the sink node of the motion capture device through a Bluetooth communication module. The unit calculates the quaternion output from the dynamic link library of sensor data processing, calculates the rotation amount and coordinate offset of each sensor node’s limb, and uses the character model to realize the real-time rendering of the virtual character model. At the same time, it controls the storage of sensor data, the drive of the model, and the display of the graphical interface. When a gesture action is about to occur, a trigger signal is given to the system to mark the beginning of the action, so as to obtain the initial data of each axis signal of the MEMS sensor. When the gesture action is completed, give the system a signal to end the action. Mark the end of the action, so that you can capture the original signal data during the beginning and end of the gesture action. In order to ensure the normal communication between PS and PL, it is necessary to test the key interfaces involved. Because the data received by the SPI acquisition module is irregular, it is impossible to verify whether the data is wrong, so the SPI acquisition module is replaced with a module that automatically increments data, and the IP core is generated, and a test platform is built for testing. The data shows that the average measurement error of X-axis displacement of the space tracking system is 8.17%, the average measurement error of Y-axis displacement is 7.51%, the average measurement error of Z-axis displacement is 9.72%, and the average error of three-dimensional space measurement is 8.7%. The results show that the MEMS sensor can accurately recognize the action with high accuracy.

scholarly journals Are You for Real? Engineering a Virtual Lab for the Sports Sciences Using Wearables and IoT

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 110 ◽  
Author(s):  
Joel Benesha ◽  
Jim Lee ◽  
Daniel A. James ◽  
Barbara White
Keyword(s):  
Inertial Sensor ◽  
Tertiary Education ◽  
Three Dimensional ◽  
Sensor Data ◽  
Web Based ◽  
Sports Science ◽  
Virtual Lab ◽  
Prototype Software ◽  
Sports Sciences ◽  
3D Motion Capture

In tertiary education, disciplines such as sports science that require experimental components in their courses represent a significant challenge for online and distance education. This paper demonstrates the design and construction of an enriched experiment, together with the prototype software solution which can all be operated remotely using a web-based client. It presents research that investigated how to visualise data from internet of things (IoT) sensor devices (inertial sensor) used for tracking football sideline throw-ins. In this simple experiment, data was collected from one footballer, fitted with a single inertial sensor. A two-dimensional (2D) video, three-dimensional (3D) motion capture system and inertial sensor were all used to detect the release point of a sideline ball throw-in. In this project, inertial sensor data was used to create a 3D model using web graphical language and three.js.

HUMAN FALL DETECTION AND TRACKING SYSTEM USING MEMS SENSOR

2017 ◽  
Vol 6 (3) ◽  
pp. 20
Author(s):  
A. SAIPRIYA ◽  
V. MEENA ◽  
MAALIK M.ABDUL ◽  
D. PRAVINRAJ ◽  
P. JEGADEESHWARI ◽  
...  
Keyword(s):  
Tracking System ◽  
Fall Detection ◽  
Mems Sensor ◽  
Detection And Tracking

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals On Expressive Features for Gait Analysis using Lower Limb Inertial Sensor Data

2020 ◽  
Vol 53 (2) ◽  
pp. 15990-15997
Author(s):  
Felix Laufer ◽  
Michael Lorenz ◽  
Bertram Taetz ◽  
Gabriele Bleser
Keyword(s):  
Gait Analysis ◽  
Lower Limb ◽  
Inertial Sensor ◽  
Sensor Data

scholarly journals Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew P. Creagh ◽  
Florian Lipsmeier ◽  
Michael Lindemann ◽  
Maarten De Vos
Keyword(s):  
Multiple Sclerosis ◽  
Deep Learning ◽  
Inertial Sensor ◽  
Heterogeneous Data ◽  
Fine Tuning ◽  
Sensor Data ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Healthcare Applications ◽  
Feature Based

AbstractThe emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.

scholarly journals Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

2021 ◽  
Vol 185 ◽  
pp. 282-291
Author(s):  
Nizam U. Ahamed ◽  
Kellen T. Krajewski ◽  
Camille C. Johnson ◽  
Adam J. Sterczala ◽  
Julie P. Greeves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Sensor ◽  
Load Carriage ◽  
Sensor Data ◽  
Gait Patterns

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

Pointing to Remembered Targets in 3-D Space in Parkinson's Disease

Motor Control ◽  
1998 ◽  
Vol 2 (3) ◽  
pp. 251-277 ◽  
Author(s):  
Howard Poizner ◽  
Olga I. Fookson ◽  
Michail B. Berkinblit ◽  
Wayne Hening ◽  
Gregory Feldman ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Visual Feedback ◽  
Tracking System ◽  
Three Dimensional ◽  
Performance Deficits ◽  
Normal Speed ◽  
Task Conditions ◽  
Variable Errors ◽  
Target Locations

A three-dimensional tracking system was used to examine whether subjects with Parkinson's disease (PD) would show characteristic performance deficits in an unconstrained pointing task. Five targets were presented in a pyramidal array in space to 11 individuals with mild to moderate PD and 8 age-matched controls. After the target was indicated, subjects closed their eyes and pointed to the remembered target locations without vision. Despite the absence of visual feedback during movement, PD subjects were as accurate overall as controls. However, PD subjects showed greater variable errors, more irregular trajectories, and a vertical endpoint bias in which their endpoints were significantly lower than controls. They also showed deficiencies in the compensatory organization of joint rotations to ensure consistency in azimuthal (horizontal) positioning of the arm endpoint. We concluded that, under appropriate task conditions, PD subjects may not show overall deficits in accuracy even when making targeted movements at normal speed without visual feedback. Nevertheless, our findings indicate that there are certain dimensions of performance which are selectively altered in Parkinson's disease even when overall performance is normal.

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