Modeling and Error Analysis in Camera-Based Jump Height Measurement

Introduction: In this work, we use simulated data to quantify the different failure mechanisms of a previously presented low-cost jump height measurement system, based on widely available consumer smartphone technology. Methods: In order to assess the importance of the different preconditions of the jump height measurement algorithm, we generate a synthetic dataset of 2000 random jump parabolas for 2000 randomly generated persons without real-world artifacts. We then selectively add different perturbations to the parabolas and reconstruct the jump height using the evaluated algorithm. The degree to which the manipulations influence the reconstructed jump height gives us insights into how critical each precondition is for the method’s accuracy. Results: For a subject-to-camera distance of 2.5 meters, we found the most important influences to be tracking inaccuracies and distance changes (non-vertical jumps). These are also the most difficult factors to control. Camera angle and lens distortion are easier to handle in practice and have a very low impact on the reconstructed jump height. The intraclass correlation value ICC(3,1) between true jump height and the reconstruction from distorted data ranges between 0.999 for mild and 0.988 for more severe distortions. Conclusion: Our results support the design of future studies and tools for accurate and affordable jump height measurement, which can be used in individual fitness, sports medicine, and rehabilitation applications

Markerless camera-based vertical jump height measurement using OpenPose

Vertical jump height is an important tool to measure athletes’ lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement

Improved Calibration Procedure for Wireless Inertial Measurement Units without Precision Equipment

Inertial measurement units (IMUs) are used in medical applications for many different purposes. However, an IMU's measurement accuracy can degrade over time, entailing re-calibration. In their 2014 paper, Tedaldi et al. presented an IMU calibration method that does not require external precision equipment or complex procedures. This allows end-users or personnel without expert knowledge of inertial measurement to re-calibrate the sensors by placing them in several suitable but not precisely defined orientations. In this work, we present several improvements to Tedaldi's method, both on the algorithmic level and the calibration procedure: adaptions for low noise accelerometers, a calibration helper object, and packet loss compensation for wireless calibration. We applied the modified calibration procedure to our custom-built IMU platform and verified the consistency of results across multiple calibration runs. In order to minimize the time needed for re-calibration, we analyzed how the calibration result accuracy degrades when fewer calibration orientations are used. We found that N=12 different orientations are sufficient to achieve a very good calibration, and more orientations yielded only marginal improvements. This is a significant improvement compared to the 37 to 50 orientations recommended by Tedaldi. Thus, we were reduced the time required to calibrate a single IMU from ca. 5 minutes to less than 2 minutes without sacrificing any meaningful calibration accuracy

Measuring vertical jump height using a smartphone camera with simultaneous gravity-based calibration

Vertical jump height is an important tool to measure athletes' lower body power in sports science and medicine. Several different methods exist to measure jump height, but each has its own limitations. This work proposes a novel way to measure jump height directly, using optical tracking with a single smartphone camera. A parabolic fall trajectory is obtained from this video by tracking a single feature. The parabolic trajectory is then used to partially calibrate the camera and convert pixel measurements into real-world units, allowing the calculation of the achieved height. Comparison to an optical motion capture system yields promising results.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Menschliche Bewegungen drahtlos und genau messen : Sensoren und Algorithmen für die Heimanwendung sensomotorischer Tests

Balance, Sprungkraft und Körpergefühl sind im Sport, aber auch bei der Rehabilitation wichtige Indikatoren. Wissenschaftler*innen am Institut für Mikroelektronische Systeme entwickeln in Kooperation mit externen Partnern aus den Sportwissenschaften und der Industrie ein mobiles Diagnosesystem zur Beurteilung der sensomotorischen Regulationsfähigkeit mithilfe standardisierter Sporttests

An Integrated Heated Testbench for Characterizing High Temperature ICs

This paper presents a newly developed integrated heating system, which can keep the IC under test at a constant temperature of up to 250 ◦C. The heating system can be used while the IC under test is mounted on its custom-designed interface board, which controls the two supply voltages and provides connectivity to an FPGA. Using a testing framework on the FPGA, the test stimuli and operating clock can be provided with at least 100 MHz. Thus, it is possible to fully vary all three parameters—frequency, voltage, and temperature—during continuous operation of the IC. A case study is performed with a previously fabricated ASIC to test the proposed system

Verwendung von Intertialsensoren zur automatisierten Auswertung sensomotorischer Tests

Um die sensomotorische Leistungsfähigkeit von Sportlern zu evaluieren, gibt es verschiedene sportwissenschaftliche Standardtests, wie zum Beispiel den Y-Balance-Test (YBT). Allerdings ist bei vielen dieser Verfahren ein Tester nötig, der die Durchführung leitet und das Ergebnis bestimmt. In dieser Arbeit wird der Ansatz für ein System auf Basis von Inertialsensoren (IMUs) vorgestellt, durch das die Auswertung verschiedener im Bereich der Sportwissenschaften gängiger Tests automatisiert werden kann. Implementiert wurde der YBT und die aktive Winkelreproduktion. Durch die Automatisierung muss während des Tests kein Tester mehr anwesend sein, wodurch beispielsweise Freizeitsportler, Leistungssportler, Trainer, Vereine, aber auch Forschungseinrichtungen in die Lage versetzt werden, diese Tests jederzeit durchzuführen. Durch die Verwendung von IMUs, die ihre eigene Ausrichtung als Quaternion schätzen, wird im vorgestellten System die aktuelle Pose der Knochen im Skelett des Nutzers berechnet. Aus diesen werden dann die verschiedenen Testergebnisse, wie Gelenkwinkel oder die Position einzelner Extremitäten, bestimmt. Als Plattform kommt ein mobiles Gerät zum Einsatz, auf dem die Berechnung und die Visualisierung in Echtzeit erfolgen