scholarly journals Enhancing the Performance of Elite Speed Skaters Using SkateView: A New Device to Measure Performance in Speed Skating

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 133
Author(s):  
Jeroen van der Eb ◽  
Sjoerd Gereats ◽  
Arno Knobbe
Keyword(s):  
Inertial Measurement Unit ◽  
Training Session ◽  
Measurement Unit ◽  
Stroke Frequency ◽  
New Device ◽  
Technical Performance ◽  
Speed Skating ◽  
Basic Parameters ◽  
Light Force ◽  
Insight Into

In speed skating, environmental circumstances and the near-frictionless movement of the skate in a fore–aft direction over the ice make it difficult to measure technical performance parameters on a regular basis while training in an indoor speed skating rink. SkateView has been developed to overcome these challenges, comprising of two IMU’s (Inertial Measurement Unit), ultra-light force sensors, a mobile phone and an app providing feedback to coach and skater. The feedback, directly on the ice or shortly after a training session, consists of basic parameters like ice contact time, stroke frequency and lap times, and more parameters can be added. Stroke frequency is an important performance parameter, which is presented on a stroke–by–stroke basis and provides a direct insight into the activity.

scholarly journals Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5553
Author(s):  
Mohsen Sharifi Renani ◽  
Casey A. Myers ◽  
Rohola Zandie ◽  
Mohammad H. Mahoor ◽  
Bradley S. Davidson ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Gait Patterns ◽  
Movement Data ◽  
Gait Parameters ◽  
Activity Types ◽  
Spatial Parameters ◽  
Total Knee ◽  
Insight Into ◽  
Sensor Locations

Quantitative assessments of patient movement quality in osteoarthritis (OA), specifically spatiotemporal gait parameters (STGPs), can provide in-depth insight into gait patterns, activity types, and changes in mobility after total knee arthroplasty (TKA). A study was conducted to benchmark the ability of multiple deep neural network (DNN) architectures to predict 12 STGPs from inertial measurement unit (IMU) data and to identify an optimal sensor combination, which has yet to be studied for OA and TKA subjects. DNNs were trained using movement data from 29 subjects, walking at slow, normal, and fast paces and evaluated with cross-fold validation over the subjects. Optimal sensor locations were determined by comparing prediction accuracy with 15 IMU configurations (pelvis, thigh, shank, and feet). Percent error across the 12 STGPs ranged from 2.1% (stride time) to 73.7% (toe-out angle) and overall was more accurate in temporal parameters than spatial parameters. The most and least accurate sensor combinations were feet-thighs and singular pelvis, respectively. DNNs showed promising results in predicting STGPs for OA and TKA subjects based on signals from IMU sensors and overcomes the dependency on sensor locations that can hinder the design of patient monitoring systems for clinical application.

New method to evaluate three-dimensional push-off angle during short-track speed skating using wearable inertial measurement unit sensors

2019 ◽  
Vol 233 (4) ◽  
pp. 476-480 ◽  
Author(s):  
Kyungsoo Kim ◽  
Jun Seok Kim ◽  
Tserenchimed Purevsuren ◽  
Batbayar Khuyagbaatar ◽  
SuKyoung Lee ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Inertial Measurement Unit ◽  
High Speed ◽  
Three Dimensional ◽  
Analysis Data ◽  
New Method ◽  
Measurement Unit ◽  
Angle Estimation ◽  
Inertial Measurement ◽  
Speed Skating

The push-off mechanism to generate forward movement in skating has been analyzed by using high-speed cameras and specially designed skates because it is closely related to skater performance. However, using high-speed cameras for such an investigation, it is hard to measure the three-dimensional push-off force, and a skate with strain gauges is difficult to implement in the real competitions. In this study, we provided a new method to evaluate the three-dimensional push-off angle in short-track speed skating based on motion analysis using a wearable motion analysis system with inertial measurement unit sensors to avoid using a special skate or specific equipment insert into the skate for measurement of push-off force. The estimated push-off angle based on motion analysis data was very close to that based on push-off force with a small root mean square difference less than 6% when using the lateral marker in the left leg and the medial marker in the right leg regardless of skating phase. These results indicated that the push-off angle estimation based on motion analysis data using a wearable motion capture system of inertial measurement unit sensors could be acceptable for realistic situations. The proposed method was shown to be feasible during short-track speed skating. This study is meaningful because it can provide a more acceptable push-off angle estimation in real competitive situations.

scholarly journals Insole Gait Acquisition System Based on Wearable Sensors

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Niharika Gogoi ◽  
Zixuan Yu ◽  
Yichun Qin ◽  
Jens Kirchner ◽  
Georg Fischer
Keyword(s):  
Health Monitoring ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Wearable Sensors ◽  
Human Gait ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution ◽  
Human Gait Analysis ◽  
Insight Into

Human gait analysis is a growing field of research interest in medical treatment, sports training and structural health monitoring. In our study, we propose a low-cost insole design with wearable sensors based on piezoelectric discs (PZT) and an inertial measurement unit (IMU) to acquire the human gait. The sensors are placed at three points of a shoe sole: toe, metatarsal and heel. The human gait obtained from such an insole layout is significantly affected by plantar pressure distribution and alignment of the feet. The PZT sensors give an insight into the pressure map under the feet, and the IMUs record projection and orientation of the feet.

scholarly journals Design of mobile holonomic robot with wireless inertial measurement control system

Mechanik ◽  
2017 ◽  
Vol 90 (7) ◽  
pp. 634-636
Author(s):  
Piotr Rychlik ◽  
Wojciech Kaczmarek
Keyword(s):  
Control System ◽  
Inertial Measurement Unit ◽  
Control Method ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Measurement Control ◽  
Holonomic Robot ◽  
Basic Parameters ◽  
The Way

The article presents the design of mobile holonomic robot, where special attention was given to the robot’s control method. It discusses the way of processing values from an inertial measurement unit to actuator signals. In order to determine the correct functioning of the robot, a number of tests were carried out covering its software, and followed by the determining of basic parameters characterizing robot’s mobility.

Validity and Reliability of a New Device to Measure Type of Actions in Indoor Sports

2020 ◽  
Author(s):  
Carlos Lago-Fuentes ◽  
Paolo Aiello ◽  
Mauro Testa ◽  
Iker Muñoz ◽  
Marcos Mecías Calvo
Keyword(s):  
Coefficient Of Variation ◽  
Inertial Measurement Unit ◽  
High Reliability ◽  
Intraclass Correlation ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
New Device ◽  
Standing Up ◽  
Indoor Sports

AbstractThe purpose of this study was to analyze the validity and the reliability of the intensity ranges, number of actions and changes of direction measured by a commercial inertial measurement unit. Eleven elite youth futsal players performed a circuit with different type of displacements as sprinting, running at low-medium intensity, standing up and changes of direction. Data recorded by the Overtraq system were compared with video-analyzer during the six trials of each player. Standard error mean, Intraclass Correlation Coeficient and Coefficient of variation, were calculated to analyze the reliability of the device, as well as the Root Mean Square Error and Confidence Interval with correlation of Pearson for its validity. The results reported good validity for three intensity ranges (R2>0.7) with high reliability (Intraclass Correlation Coeficient: 0.8–0.9), especially for high intensity actions (Intraclass Correlation Coeficient: 0.95, Coefficient of Variation: 3.06%). Furthermore, the validity for the number of different actions was almost perfect (96.3–100%), with only small differences regarding changes of activity (mean error: 2.0%). The Overtraq system can be considered as a valid and reliable technology for measuring and monitoring actions at different intensities and changes of direction in futsal, likewise common actions for other indoor sports.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals The Accelerations of a Wave Measurement Buoy Impacted by Breaking Waves in the Surf Zone

2021 ◽  
Vol 9 (2) ◽  
pp. 214
Author(s):  
Adam C. Brown ◽  
Robert K. Paasch
Keyword(s):  
Inertial Measurement Unit ◽  
Surf Zone ◽  
Spherical Wave ◽  
Breaking Waves ◽  
Measurement Unit ◽  
High Fidelity ◽  
Wave Measurement ◽  
Near Shore ◽  
Shoaling Waves ◽  
Multiple Deployments

A spherical wave measurement buoy capable of detecting breaking waves has been designed and built. The buoy is 16 inches in diameter and houses a 9 degree of freedom inertial measurement unit (IMU). The orientation and acceleration of the buoy is continuously logged at frequencies up to 200 Hz providing a high fidelity description of the motion of the buoy as it is impacted by breaking waves. The buoy was deployed several times throughout the winter of 2013–2014. Both moored and free-drifting data were acquired in near-shore shoaling waves off the coast of Newport, OR. Almost 200 breaking waves of varying type and intensity were measured over the course of multiple deployments. The characteristic signature of spilling and plunging breakers was identified in the IMU data.

Sign in / Sign up

Export Citation Format

Share Document