Sensors

Editorial

Jump to: Research

3 pages, 170 KiB

Open AccessEditorial

Sensor Technology for Sports Monitoring

by Vesa Linnamo

Sensors 2023, 23(2), 572; https://doi.org/10.3390/s23020572 - 4 Jan 2023

Cited by 6 | Viewed by 2898

Abstract

Over the past decades, huge steps have been made in the development of sensor technology related to sports monitoring [...] Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

Research

Jump to: Editorial

17 pages, 5586 KiB

Open AccessArticle

Validation of a Sensor-Based Dynamic Ski Deflection Measurement in the Lab and Proof-of-Concept Field Investigation

by Christoph Thorwartl, Josef Kröll, Andreas Tschepp, Helmut Holzer, Wolfgang Teufl and Thomas Stöggl

Sensors 2022, 22(15), 5768; https://doi.org/10.3390/s22155768 - 2 Aug 2022

Cited by 6 | Viewed by 2383

Abstract

Introduction: Ski deflection is a performance-relevant factor in alpine skiing and the segmental and temporal curvature characteristics (m⁻¹) along the ski have lately received particular attention. Recently, we introduced a PyzoFlex^® ski deflection measurement prototype that demonstrated high reliability and [...] Read more.

Introduction: Ski deflection is a performance-relevant factor in alpine skiing and the segmental and temporal curvature characteristics (m⁻¹) along the ski have lately received particular attention. Recently, we introduced a PyzoFlex^® ski deflection measurement prototype that demonstrated high reliability and validity in a quasi-static setting. The aim of the present work is to test the performance of an enhanced version of the prototype in a dynamic setting both in a skiing-like bending simulation as well as in a field proof-of-concept measurement. Material and methods: A total of twelve sensor foils were implemented on the upper surface of the ski. The ski sensors were calibrated with an empirical curvature model and then deformed on a programmable bending robot with the following program: 20 times at three different deformation velocities (v_slow, v_medium, v_fast) with (1) central bending, (2) front bending, (3) back bending, (4) edging left, and (5) edging right. For reliability assessment, pairs of bending cycles (cycle 1 vs. cycle 10 and cycle 10 vs. cycle 20) at v_slow, v_medium, and v_fast and between pairs of velocity (v_slow vs. v_medium and v_slow vs. v_fast) were evaluated by calculating the change in the mean (CIM), coefficient of variation (CV) and intraclass correlation coefficient (ICC 3.1) with a 95% confidence interval. For validity assessment, the calculated segment-wise mean signals were compared with the values that were determined by 36 infrared markers that were attached to the ski using an optoelectrical measuring system (Qualisys). Results: High reliability was found for pairs of bending cycles (CIM −0.69–0.24%, max CV 0.28%, ICC 3.1 > 0.999) and pairs of velocities (max CIM = 3.03%, max CV = 3.05%, ICC 3.1 = 0.997). The criterion validity based on the Pearson correlation coefficient was r = 0.98. The accuracy (systematic bias) and precision (standard deviation), were −0.003 m⁻¹ and 0.047 m⁻¹, respectively. Conclusions: The proof-of-concept field measurement has shown that the prototype is stable, robust, and waterproof and provides characteristic curvature progressions with plausible values. Combined with the high laboratory-based reliability and validity of the PyzoFlex^® prototype, this is a potential candidate for smart ski equipment. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

16 pages, 32986 KiB

Open AccessArticle

Detecting and Visualizing Stops in Dance Training by Neural Network Based on Velocity and Acceleration

by Yuuki Jin, Genki Suzuki and Hiroyuki Shioya

Sensors 2022, 22(14), 5402; https://doi.org/10.3390/s22145402 - 20 Jul 2022

Cited by 4 | Viewed by 2278

Abstract

Various genres of dance, such as Yosakoi Soran, have contributed to the health of many people and contributed to their sense of belonging to a community. However, due to the effects of COVID-19, various face-to-face activities have been restricted and group dance practice [...] Read more.

Various genres of dance, such as Yosakoi Soran, have contributed to the health of many people and contributed to their sense of belonging to a community. However, due to the effects of COVID-19, various face-to-face activities have been restricted and group dance practice has become difficult. Hence, there is a need to facilitate remote dance practice. In this paper, we propose a system for detecting and visualizing the very important dance motions known as stops. We measure dance movements by motion capture and calculate the features of each movement based on velocity and acceleration. Using a neural network to learn motion features, the system detects stops and visualizes them using a human-like 3D model. In an experiment using dance data, the proposed method obtained highly accurate stop detection results and demonstrated its effectiveness as an information and communication technology support for remote group dance practice. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

15 pages, 24308 KiB

Open AccessArticle

Proposal of an Alpine Skiing Kinematic Analysis with the Aid of Miniaturized Monitoring Sensors, a Pilot Study

by Caterina Russo, Elena Puppo, Stefania Roati and Aurelio Somà

Sensors 2022, 22(11), 4286; https://doi.org/10.3390/s22114286 - 4 Jun 2022

Cited by 7 | Viewed by 3312

Abstract

The recent growth and spread of smart sensor technologies make these connected devices suitable for diagnostic and monitoring in different fields. In particular, these sensors are useful in diagnostics for control of diseases or during rehabilitation. They are also extensively used in the [...] Read more.

The recent growth and spread of smart sensor technologies make these connected devices suitable for diagnostic and monitoring in different fields. In particular, these sensors are useful in diagnostics for control of diseases or during rehabilitation. They are also extensively used in the monitoring field, both by non-expert and expert users, to monitor health status and progress during a sports activity. For athletes, these devices could be used to control and enhance their performance. This development has led to the realization of miniaturized sensors that are wearable during different sporting activities without interfering with the movements of the athlete. The use of these sensors, during training or racing, opens new frontiers for the understanding of motions and causes of injuries. This pilot study introduced a motion analysis system to monitor Alpine ski activities during training sessions. Through five inertial measurement units (IMUs), placed on five points of the athletes, it is possible to compute the angle of each joint and evaluate the ski run. Comparing the IMU data, firstly, with a video and then proposing them to an expert coach, it is possible to observe from the data the same mistakes visible in the camera. The aim of this work is to find a tool to support ski coaches during training sessions. Since the evaluation of athletes is now mainly developed with the support of video, we evaluate the use of IMUs to support the evaluation of the coach with more precise data. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

9 pages, 549 KiB

Open AccessArticle

Physical Demands during the Game and Compensatory Training Session (MD + 1) in Elite Football Players Using Global Positioning System Device

by Gabriel Calderón-Pellegrino, Leonor Gallardo, Jorge Garcia-Unanue, Jose Luis Felipe, Antonio Hernandez-Martin, Víctor Paredes-Hernández and Javier Sánchez-Sánchez

Sensors 2022, 22(10), 3872; https://doi.org/10.3390/s22103872 - 19 May 2022

Cited by 8 | Viewed by 3140

Abstract

The aims of this study were to analyze the differences of physical demands of non-starter players regarding the playing time during the competition and to evaluate the physical demands of the compensatory training (MD + 1C) for substitute players in elite football. The [...] Read more.

The aims of this study were to analyze the differences of physical demands of non-starter players regarding the playing time during the competition and to evaluate the physical demands of the compensatory training (MD + 1C) for substitute players in elite football. The match statistics and MD + 1C of substitute players from a professional Spanish LaLiga football club were analyzed using a 10-Hz global positioning system (GPS) Apex GPS system device, which has been validated as a reliable and valid method to analyze performance in team sports, during all games of the 2016/2017, 2017/2018 and 2018/2019 seasons. The starting players showed both lower total distances covered and high-intensity actions compared to the substitutes. Regarding the minutes played by the substitutes, greater physical performance was found for the players with fewer minutes (5–15 min). Furthermore, no differences were found between first and second divisions regarding physical performance of substitutes (p > 0.05). This study highlights the importance of individualizing the workload of training sessions for substitutes and starters. Furthermore, the complementary session should be individualized according to the minutes played by the substitutes. These players are potentially under-loaded compared to starters, especially in terms of high-intensity actions, therefore additional session-specific training for each substitute would be useful to reach the optimal training load according to the minutes played during the game. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

14 pages, 17736 KiB

Open AccessArticle

Propulsion Calculated by Force and Displacement of Center of Mass in Treadmill Cross-Country Skiing

by Shuang Zhao, Olli Ohtonen, Keijo Ruotsalainen, Lauri Kettunen, Stefan Lindinger, Caroline Göpfert and Vesa Linnamo

Sensors 2022, 22(7), 2777; https://doi.org/10.3390/s22072777 - 5 Apr 2022

Cited by 4 | Viewed by 2979

Abstract

This study evaluated two approaches for estimating the total propulsive force on a skier’s center of mass (COM) with double-poling (DP) and V2-skating (V2) skiing techniques. We also assessed the accuracy and the stability of each approach by changing the speed and the [...] Read more.

This study evaluated two approaches for estimating the total propulsive force on a skier’s center of mass (COM) with double-poling (DP) and V2-skating (V2) skiing techniques. We also assessed the accuracy and the stability of each approach by changing the speed and the incline of the treadmill. A total of 10 cross-country skiers participated in this study. Force measurement bindings, pole force sensors, and an eight-camera Vicon system were used for data collection. The coefficient of multiple correlation (CMC) was calculated to evaluate the similarity between the force curves. Mean absolute force differences between the estimated values and the reference value were computed to evaluate the accuracy of each approach. In both DP and V2 techniques, the force–time curves of the forward component of the translational force were similar to the reference value (CMC: 0.832–0.936). The similarity between the force and time curves of the forward component of the ground reaction force (GRF) and the reference value was, however, greater (CMC: 0.879–0.955). Both approaches can estimate the trend of the force–time curve of the propulsive force properly. An approach by calculating the forward component of GRF is a more appropriate method due to a better accuracy. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

22 pages, 3914 KiB

Open AccessArticle

A Wearable System for Jump Detection in Inline Figure Skating

by Antonio Panfili, Alvise Spanò and Agostino Cortesi

Sensors 2022, 22(4), 1650; https://doi.org/10.3390/s22041650 - 20 Feb 2022

Cited by 4 | Viewed by 3845

Abstract

This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes’ performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to [...] Read more.

This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes’ performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to detect when jumps are performed and provides a live view of the data (e.g., the number and height of jumps) through a graphical user interface. The main novelty of our approach lies in the way in which the optical sensors are orientated. Typically, the sensors are orientated horizontally and positioned in pairs on the ground, where they measure the time interval between the moment the athlete leaves the ground and the moment they land. In our system, an optical sensor is placed under each foot and is vertically orientated so as to constantly measure the distance from the ground. In addition, a gyroscope sensor is placed on the athlete’s back, which provides information on the direction and angular momentum of the movement. By combining this data, the system provides the accurate detection of various jumps and technical elements without any constraints on the training ground. In this paper, the system is also compared to similar platforms in the literature, although there are no other specific systems that are available for inline figure skating. The results of the experimental evaluation, which was performed by high profile athletes, confirm its effectiveness in correctly detecting jumps, especially considering its compromise between precision and the overall cost of the equipment. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

14 pages, 4239 KiB

Open AccessArticle

Magnitude and Shape of the Forces Applied on the Foot Rest and Paddle by Elite Kayakers

by Pedro Bonito, Miguel Sousa, Fernando José Ferreira, Jorge Fonseca Justo and Beatriz Branquinho Gomes

Sensors 2022, 22(4), 1612; https://doi.org/10.3390/s22041612 - 18 Feb 2022

Cited by 4 | Viewed by 3013

Abstract

The study aimed to investigate the magnitude and shape of the forces applied on the foot rest, foot strap, and paddle. Thirteen elite male kayakers participated in this study and performed a 2-min test simulating 500 m race pace in a kayak ergometer. [...] Read more.

The study aimed to investigate the magnitude and shape of the forces applied on the foot rest, foot strap, and paddle. Thirteen elite male kayakers participated in this study and performed a 2-min test simulating 500 m race pace in a kayak ergometer. Forces applied by the kayakers on the paddle, foot rest, and foot strap were measured with load cells and recorded by an electronic measuring system. The magnitude of the peak forces applied on the foot rest (left: 543.27 ± 85.93; right: 524.39 ± 88.36) approximately doubled the ones applied on the paddle (left: 236.37 ± 19.32; right: 243.92 ± 28.89). The forces on the foot strap were similar in magnitude to the paddle forces (left: 240.09 ± 74.92; right: 231.05 ± 52.01). A positive correlation was found between the peak forces applied on the foot rest and paddle on the same side (p < 0.001). When comparing the best and worst kayakers’ performance, the best showed greater forces magnitudes and synchronization of the peak forces. Analyses of the force–time curves, including not only the forces applied by the kayaker on the paddle but also the ones applied on the foot rest and strap, should be considered relevant in terms of technique analyses. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

12 pages, 1392 KiB

Open AccessArticle

The Effect of Paddle Stroke Variables Measured by Trainesense SmartPaddle^® on the Velocity of the Kayak

by Antti Löppönen, Tomi Vänttinen, Marko Haverinen and Vesa Linnamo

Sensors 2022, 22(3), 938; https://doi.org/10.3390/s22030938 - 26 Jan 2022

Cited by 8 | Viewed by 5186

Abstract

(1) Background: This study aimed to compare key variables of paddle stroke measured by a commercial Trainesense SmartPaddle^® against the strain-gauge shaft and investigate how these variables are associated with the velocity of the boat among national-level canoe polo players. (2) Methods: [...] Read more.

(1) Background: This study aimed to compare key variables of paddle stroke measured by a commercial Trainesense SmartPaddle^® against the strain-gauge shaft and investigate how these variables are associated with the velocity of the boat among national-level canoe polo players. (2) Methods: This study involved 14 Finnish national-level canoe polo players. The measurement protocol consisted of three different paddling velocities, which were performed in indoor swimming pools. The velocity of the boat was calculated based on the performance time measured with the laser photocell gate. Canoe polo equipment was used in the study and a SmartPaddle sensor was attached to the paddle blade. A strain-gauge paddle shaft was used as a reference method to examine the validity of SmartPaddle. (3) Results: The stroke rate, force production time, mean and maximal force measured with the strain-gauge paddle shaft correlated strongly (r = 0.84–0.95, p < 0.01) with SmartPaddle. However, the SmartPaddle overestimated the maximum force compared to the strain-gauge shaft. Stroke rate (r = 0.86, p < 0.01), mean force (r = 0.79, p < 0.01), maximal force (r = 0.78, p < 0.01) and total absolute impulse (r = 0.70, p < 0.01) correlated positively and force production time negatively (r = −0.76, p < 0.01) with the velocity of the boat. (4) Conclusions: We conclude that the SmartPaddle provides promising information on stroke key variables when compared to the strain-gauge paddle shaft. The SmartPaddle is a new and interesting tool for biomechanical research and daily kayaking coaching in real open water conditions. However, more research and algorithm development are needed before the SmartPaddle can be used in everyday coaching sessions in kayaking. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

12 pages, 2654 KiB

Open AccessArticle

Framework for In-Field Analyses of Performance and Sub-Technique Selection in Standing Para Cross-Country Skiers

by Camilla H. Carlsen, Julia Kathrin Baumgart, Jan Kocbach, Pål Haugnes, Evy M. B. Paulussen and Øyvind Sandbakk

Sensors 2021, 21(14), 4876; https://doi.org/10.3390/s21144876 - 17 Jul 2021

Cited by 2 | Viewed by 2269

Abstract

Our aims were to evaluate the feasibility of a framework based on micro-sensor technology for in-field analyses of performance and sub-technique selection in Para cross-country (XC) skiing by using it to compare these parameters between elite standing Para (two men; one woman) and [...] Read more.

Our aims were to evaluate the feasibility of a framework based on micro-sensor technology for in-field analyses of performance and sub-technique selection in Para cross-country (XC) skiing by using it to compare these parameters between elite standing Para (two men; one woman) and able-bodied (AB) (three men; four women) XC skiers during a classical skiing race. The data from a global navigation satellite system and inertial measurement unit were integrated to compare time loss and selected sub-techniques as a function of speed. Compared to male/female AB skiers, male/female Para skiers displayed 19/14% slower average speed with the largest time loss (65 ± 36/35 ± 6 s/lap) found in uphill terrain. Female Para/AB skiers utilized DP, DK, and DIA, 61/43%, 15/10%, and 25/47% of the distance at low speeds, respectively, while the corresponding numbers for male Para/AB skiers were 58/18%, 1/13%, and 40/69%. At higher speeds, female Para/AB skiers utilized DP and OTHER, 26/52% and 74/48% of the distance, respectively, while corresponding numbers for male Para/AB skiers were 29/66% and 71/34%. This indicates different speed thresholds of the classical sub-techniques for Para than AB skiers. The framework provides a point of departure for large-scale international investigations of performance and related factors in Para XC skiing. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

Figure 1
The 2.5 km XC skiing race course divided into the 10 segments according to the elevation difference, including three uphill, three flat, and four downhill segments. Six turns were distributed over the 2.5 km lap (red arrow). With different placement of the start and finish, there is a gap in the 2.5 km course, which was removed from the analyses. (S) Segment (length (meter), incline range (%)); S1: 131 m, −2.6–1.6%; S2: 543 m, 1.7–12.4%; S3: 509 m, −11.7–0.2%; S4 100 m, 0.7–2.8%; S5: 156 m, −6.0–0.0%; S6: 166 m, 1.3–12.7%; S7: 339 m, −8.5–−0.4%; S8: 200 m, 1.2–16%; S9: 183 m, −10.1–−1.0%; S10: 138 m, 0.0–1.6%. Full article ">Figure 2
Comparison of average male AB XC skiers (dark red; standard deviation light pink), male B3a XC skier (yellow), and male B3b XC skier (blue) for the six laps during the race with respect to average speed, absolute speed difference, relative speed difference, accumulated time difference, and tuck. Course details are visualized in the lower part of the figure; turns (red dashes) and altitude profile of the 2.5 km race course, with uphill (black), flat (dark gray), and downhill (light gray) segments. Full article ">Figure 3
Comparison of average female AB XC skiers (dark red; standard deviation light pink) and female LW4 XC skier (green) for the four laps during the race with respect to average speed, absolute speed difference, relative speed difference, accumulated time difference, and tuck. Course details are visualized in the lower part of the figure; turns (red dashes) and altitude profile of the 2.5 km race course, with uphill (black), flat (dark gray), and downhill (light gray) segments. Full article ">Figure 4
Distribution of sub-techniques over different speed-intervals for male AB, the B3a, and the B3b XC skiers per distance and time. Diagonal stride (DIA; red); Kick double poling (DK; green); Double poling (DP; blue); Tuck position and turn technique (OTHER; gray); White sections illustrate that the skier did not use these speeds. Full article ">Figure 5
Distribution of sub-techniques over different speed-intervals for female AB and the LW4 XC skiers per distance and time. Diagonal stride (DIA; red); Kick double poling (DK; green); Double poling (DP; blue); Tuck position and turn technique (OTHER; gray). Blank sections for AB XC skiers and the LW4 XC skier illustrate that one of the skiers didn’t used these speeds. Full article ">

15 pages, 3324 KiB

Open AccessArticle

A Novel Sensor Foil to Measure Ski Deflections: Development and Validation of a Curvature Model

by Christoph Thorwartl, Josef Kröll, Andreas Tschepp, Philipp Schäffner, Helmut Holzer and Thomas Stöggl

Sensors 2021, 21(14), 4848; https://doi.org/10.3390/s21144848 - 16 Jul 2021

Cited by 8 | Viewed by 3188

Abstract

The ski deflection with the associated temporal and segmental curvature variation can be considered as a performance-relevant factor in alpine skiing. Although some work on recording ski deflection is available, the segmental curvature among the ski and temporal aspects have not yet been [...] Read more.

The ski deflection with the associated temporal and segmental curvature variation can be considered as a performance-relevant factor in alpine skiing. Although some work on recording ski deflection is available, the segmental curvature among the ski and temporal aspects have not yet been made an object of observation. Therefore, the goal of this study was to develop a novel ski demonstrator and to conceptualize and validate an empirical curvature model. Twenty-four PyzoFlex^® technology-based sensor foils were attached to the upper surface of an alpine ski. A self-developed instrument simultaneously measuring sixteen sensors was used as a data acquisition device. After calibration with a standardized bending test, using an empirical curvature model, the sensors were applied to analyze the segmental curvature characteristic (m⁻¹) of the ski in a quasi-static bending situation at five different load levels between 100 N and 230 N. The derived curvature data were compared with values obtained from a high-precision laser measurement system. For the reliability assessment, successive pairs of trials were evaluated at different load levels by calculating the change in mean (CIM), the coefficient of variation (CV) and the intraclass correlation coefficient (ICC 3.1) with a 95% confidence interval. A high reliability of CIM −1.41–0.50%, max CV 1.45%, and ICC 3.1 > 0.961 was found for the different load levels. Additionally, the criterion validity based on the Pearson correlation coefficient was R² = 0.993 and the limits of agreement, expressed by the accuracy (systematic bias) and the precision (SD), was between +9.45 × 10⁻³ m⁻¹ and −6.78 × 10⁻³ m⁻¹ for all load levels. The new measuring system offers both good accuracy (1.33 × 10⁻³ m⁻¹) and high precision (4.14 × 10⁻³ m⁻¹). However, the results are based on quasi-static ski deformations, which means that a transfer into the field is only allowed to a limited extent since the scope of the curvature model has not yet been definitely determined. The high laboratory-related reliability and validity of our novel ski prototype featuring PyzoFlex^® technology make it a potential candidate for on-snow application such as smart skiing equipment. Full article

(This article belongs to the Special Issue Sensor Technology for Sports Monitoring)

► Show Figures

Figure 1

Figure 1
(a) Ski prototype with 24 single connected sensors. One foil element was implemented at the rear (nine sensors) and two foil elements at the front (fifteen sensors). (b) Detailed view of the sensors in the front ski segment. The sensors were laminated to the ski with a black high-performance adhesive tape (very temperature and UV stable). Full article ">Figure 2
(a) The bending scheme of a PyzoFlex® sensor film mounted on top of the ski at the bending radius <math display="inline"><semantics> <mrow> <mi mathvariant="normal">R</mi> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mrow> <msup> <mi mathvariant="normal">w</mi> <mo>″</mo> </msup> </mrow> <mrow> <mo>(</mo> <mi mathvariant="normal">x</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (see main text) in cross-section view (left) and top view (right). The bending of the sensor causes a lateral, in-plane strain <math display="inline"><semantics> <mrow> <msub> <mi>s</mi> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> in the piezoelectric layer. The sensor element has length L, width b and thickness t, and the sensitive, piezoelectric layer is located at a radial distance <math display="inline"><semantics> <mi>ζ</mi> </semantics></math> off the neutral axis. (b) The sensor generates a charge <math display="inline"><semantics> <mrow> <mi>Q</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>, which is converted into a proportional output voltage <math display="inline"><semantics> <mrow> <msub> <mi>u</mi> <mi>a</mi> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>.</mo> </mrow> </semantics></math> <math display="inline"><semantics> <mrow> <msub> <mi>C</mi> <mi>f</mi> </msub> </mrow> </semantics></math> corresponds to the capacitance in the feedback loop. Full article ">Figure 3
Three-point bending test with an integrated laser measurement system (Atomic GmbH); (a) picture with the corresponding experimental components; (b) schematic drawing of the experimental setup. N = 83 data points were captured over a length of 1650 mm (78 points directly by the laser measurement system (<math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mn>4</mn> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>x</mi> <mn>4</mn> </msub> <mo>,</mo> <msub> <mi>z</mi> <mn>4</mn> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mn>81</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <msub> <mi>z</mi> <mrow> <mn>81</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>z</mi> <mrow> <mn>81</mn> </mrow> </msub> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> ) and 5 points extrapolated). Full article ">Figure 4
The ski instrumented with PyzoFlex® technology-based sensors. Full article ">Figure 5
Raw data from the laser measurement system. Vertical displacement (mean ±1.96 SD) vs. horizontal position (mean ±1.96 SD) over three repetitions at different load levels (100 N, 110 N, 160 N, 220 N and 230 N). (a) All 83 measuring points; (b) Detailed view over 10 measuring points. Full article ">Figure 5 Cont.
Raw data from the laser measurement system. Vertical displacement (mean ±1.96 SD) vs. horizontal position (mean ±1.96 SD) over three repetitions at different load levels (100 N, 110 N, 160 N, 220 N and 230 N). (a) All 83 measuring points; (b) Detailed view over 10 measuring points. Full article ">Figure 6
The orange and blue data points represent the calculated mean curvature from the PyzoFlex® data (mean ± SD) of the middle sensor row for the corresponding segments (S1 to S7). The large gray data points represent the curvature progression calculated from the laser data at different load levels (100 N, 110 N, 160 N, 220 N and 230 N). The interpolated points (small gray data dots) were used for the numerical calculation of the mean segmental curvature. Note: There are no sensors in the binding area, and for the sake of completeness, the data from the laser measurement system are displayed. Full article ">Figure 7
Left (a): The correlation between curvature (m−1) measured by laser measurement system (criterion instrument) and PyzoFlex® sensor system. Right (b): Bland–Altman plot showing the difference against the average of the laser measurement system and PyzoFlex® sensor system with limits of agreement (LoA) (dotted lines). SD: Standard deviation. Full article ">

Journal Menu

Journal Browser

Sensor Technology for Sports Monitoring

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI