TOWARDS AN AUTOMATED FEEDBACK COACHING SUPPORT SYSTEM FOR SPRINT PERFORMANCE MONITORING

The purpose of this study was to investigate the feasibility of developing a cost-effective, automated performance feedback system to support sprint coaching. The proposed system is designed to deliver step length, step frequency, contact time and 10 m split time information of multiple athletes training on an indoor track. An integrated systems approach was chosen combining the novel Pisa Light-Gate (PLG) and Step Information Monitoring Systems (SIMS). Current results indicate data accuracy of RMS 1.662 cm for step length, RMS 0.977 ms for foot contact time and a split time detection accuracy of 8.45 ± 6.85 ms. These results suggest that the proposed integrated system, using off-the-shelf equipment, would go beyond currently available coaching tools by providing automated and highly accurate sprint performance information for multiple athletes

Estimation of temporal parameters during sprint running using a trunk-mounted inertial measurement unit

This research was supported by a grant of the Universit a Italo-Francese (Call Vinci) awarded to E. Bergamini.The purpose of this study was to identify consistent features in the signals supplied by a single inertial measurement unit (IMU), or thereof derived, for the identification of foot-strike and foot-off instants of time and for the estimation of stance and stride duration during the maintenance phase of sprint running. Maximal sprint runs were performed on tartan tracks by five amateur and six elite athletes, and durations derived from the IMU data were validated using force platforms and a high-speed video camera, respectively, for the two groups. The IMU was positioned on the lower back trunk (L1 level) of each athlete. The magnitudes of the acceleration and angular velocity vectors measured by the IMU, as well as their wavelet-mediated first and second derivatives were computed, and features related to foot-strike and foot-off events sought. No consistent features were found on the acceleration signal or on its first and second derivatives. Conversely, the foot-strike and foot-off events could be identified from features exhibited by the second derivative of the angular velocity magnitude. An average absolute difference of 0.005 s was found between IMU and reference estimates, for both stance and stride duration and for both amateur and elite athletes. The 95% limits of agreement of this difference were less than 0.025 s. The results proved that a single, trunk-mounted IMU is suitable to estimate stance and stride duration during sprint running, providing the opportunity to collect information in the field, without constraining or limiting athletes’ and coaches’ activities

PREDICTING ACTIVE FOOT CONTACT IN THE ACCELERATION PHASE OF ATHLETIC SPRINTING THROUGH ACCELEROMETER MEASUREMENTS

Active foot contact (absence of a braking impulse) during the acceleration phase of athletic sprinting is associated with the motion of the foot before touchdown (TD). Since the identification of braking impulses through force plate measurements is cost-expensive, the aim of this study was to develop a machine learning algorithm to predict active foot contact occurrences based on ankle-mounted accelerometer measurements. Ten recreationally active athletes (three females, seven males) performed 30 sprint-block-starts each, which were used as input to the machine learning model. Model performance was assessed by the AUC for both validation (AUC = 0.96) and testing (AUC = 0.94). It is therefore possible to predict active foot contact occurrence by a machine learning algorithm solely based on ankle-mounted accelerometer measurement data

The acute effect of whole-body vibration on cycling peak power output

The aim of the present study was to determine if an acute bout of whole-body vibration (WBV) prior to sprint cycling would increase peak power output. Ten male cyclists, all familiar with maximal sprint cycling exercise performed, on two separate occasions, a ten second standing sprint on a cycle ergometer. For one trial the sprint was preceded by a 2 minute WBV intervention, requiring the participant to stand on a vibrating platform that produced sinusoidal oscillations. The frequency and amplitude of the vibration was set at 26Hz and ‘high’ (approximately 2mm) respectively. For the other trial participants stood in the same position, however the platform did not vibrate (no-WBV; 0Hz and 0mm for frequency and amplitude respectively). No significant difference was recorded for peak power output between trials (1458.0 + 283.7 W versus 1506.3 + 232.5 W for WBV and no-WBV respectively, P = 0.17). The results suggest that WBV prior to maximal standing sprint cycling does not increase peak power output

Technologies to Aid Public Understanding in Running Performance

Measurement technologies and visualisation techniques are changing the way public audiences engage with televised coverage of sport. However, the adoption of measurement technologies for broadcast coverage of running—to engage audiences and improve public understanding of performance—has been limited. This might reflect measurement challenges of athletic competition environments; athlete-worn measurement devices can be impractical, and video-based analyses typically require well-defined input videos for analysis (e.g., calibration, etc.). Recently, single-camera and calibration-independent video processing has advanced practical analyses of running performance in sports environments. This paper presents (1) the application of a method to quantify temporal running parameters using broadcast footage of 100 m sprint and 1-mile endurance running, (2) the application of human posture detection to quantify spatial running parameters using hand-held action camera footage and (3) examples of co-developed data visualisations, aimed at improving public engagement and understanding of running performance

Non-invasive, spatio-temporal gait analysis for sprint running using a single camera

Sprint running velocity is the product of step length and step rate. A tool to measure these key metrics would aid sprint training. Athletes require fast and non-invasive analysis tools, to allow them to focus on performance. A non-invasive, single camera gait analysis system (Gait Analyser) was developed and installed at the Sheffield Hallam University City Athletics Stadium (SHUCAS). The Gait Analyser filmed athletes sprinting in lanes 1, 5 and 8 wearing different coloured shoes in varied lighting conditions (e.g. sunlight or overcast). The Gait Analyser automatically identified the position and time of athlete's foot contacts, allowing the calculation of step length, step time and step velocity. Output data were compared to corresponding, manually identified measurements. For optimised setups, 100% of foot contacts were identified. Resultant direction root-mean square error (RMSE) for foot contact position and time was 108.9 mm and 0.03 s respectively. RMSE for step length, step time and step velocity was 4.9 mm, 0.00 s and 0.07 m·s-1 respectively. The Gait Analyser measured spatio-temporal gait parameters of sprint running in situ without applying markers or sensors to the athlete or the running track: results were available 2-3 s after capture

Accuracy of PARTwear inertial sensor and Optojump optical measurement system for measuring ground contact time during running

The aim of this study was to validate the detection of ground contact time (GCT) during running in two differently working systems: a small inertial measurement sensor, PARTwear (PW), worn on the shoe laces, and the optical measurement system, Optojump (OJ), placed on the track. Twelve well-trained subjects performed 12 runs each on an indoor track at speeds ranging from 3.0 - 9.0 m[middle dot]s-1. GCT of one step per run (total 144) was simultaneously obtained by the PW, the OJ, and a high- speed video camera (HSC), whereby the latter served as reference system. The sampling rate was 1,000 Hz for all methods. Compared to the HSC, the PW and the OJ systems underestimated GCT by -1.3 +/-6.1% and -16.5 +/-6.7% (p- values < .05), respectively. The intraclass correlation coefficients between PW and HSC and between OJ and HSC were .984 and .853 (p-values < .001), respectively. Despite the constant systematic underestimation of GCT, analyses indicated that PW successfully recorded GCT over a wide range of speeds. However, results showed only moderate validity for the OJ system, with increasing errors when speed decreased. In conclusion, the PW proved to be a highly useful and valid application, and its use can be recommended not only for laboratory settings but also for field applications. In contrast, data on GCT obtained by OJ during running must be treated with caution, specifically when running speed changes or when comparisons are made with GCT data collected by other measurement systems

Wearable Sensors and Machine Learning based Human Movement Analysis – Applications in Sports and Medicine

Die Analyse menschlicher Bewegung außerhalb des Labors unter realen Bedingungen ist in den letzten Jahren sowohl in sportlichen als auch in medizinischen Anwendungen zunehmend bedeutender geworden. Mobile Sensoren, welche am Körper getragen werden, haben sich in diesem Zusammenhang als wertvolle Messinstrumente etabliert. Auf Grund des Umfangs, der Komplexität, der Heterogenität und der Störanfälligkeit der Daten werden vielseitige Analysemethoden eingesetzt, um die Daten zu verarbeiten und auszuwerten. Zudem sind häufig Modellierungsansätze notwendig, da die gemessenen Größen nicht auf direktem Weg aussagekräftige biomechanische Variablen liefern. Seit wenigen Jahren haben sich hierfür Methoden des maschinellen Lernens als vielversprechende Instrumente zur Ermittlung von Zielvariablen, wie beispielsweise der Gelenkwinkel, herausgestellt. Aktuell befindet sich die Forschung an der Schnittstelle aus Biomechanik, mobiler Sensoren und maschinellem Lernen noch am Anfang. Der Bereich birgt grundsätzlich ein erhebliches Potenzial, um einerseits das Spektrum an mobilen Anwendungen im Sport, insbesondere in Sportarten mit komplexen Bewegungsanforderungen, wie beispielsweise dem Eishockey, zu erweitern. Andererseits können Methoden des maschinellen Lernens zur Abschätzung von Belastungen auf Körperstrukturen mittels mobiler Sensordaten genutzt werden. Vor allem die Anwendung mobiler Sensoren in Kombination mit Prädiktionsmodellen zur Ermittlung der Kniegelenkbelastung, wie beispielsweise der Gelenkmomente, wurde bisher nur unzureichend erforscht. Gleichwohl kommt der mobilen Erfassung von Gelenkbelastungen in der Diagnostik und Rehabilitation von Verletzungen sowie Muskel-Skelett-Erkrankungen eine zentrale Bedeutung zu. Das übergeordnete Ziel dieser Dissertation ist es, festzustellen inwieweit tragbare Sensoren und Verfahren des maschinellen Lernens zur Quantifizierung sportlicher Bewegungsmerkmale sowie zur Ermittlung der Belastung von Körperstrukturen bei der Ausführung von Alltags- und Sportbewegungen eingesetzt werden können. Die Dissertation basiert auf vier Studien, welche in internationalen Fachzeitschriften mit Peer-Review-Prozess erschienen sind. Die ersten beiden Studien konzentrieren sich zum einen auf die automatisierte Erkennung von zeitlichen Events und zum anderen auf die mobile Leistungsanalyse während des Schlittschuhlaufens im Eishockey. Die beiden weiteren Studien präsentieren jeweils einen neuartigen Ansatz zur Schätzung von Belastungen im Kniegelenk mittels künstlich neuronalen Netzen. Zwei mobile Sensoren, welche in eine Kniebandage integriert sind, dienen hierbei als Datenbasis zur Ermittlung von Kniegelenkskräften während unterschiedlicher Sportbewegungen sowie von Kniegelenksmomenten während verschiedener Lokomotionsaufgaben. Studie I zeigt eine präzise, effiziente und einfache Methode zur zeitlichen Analyse des Schlittschuhlaufens im Eishockey mittels einem am Schlittschuh befestigten Beschleunigungssensor. Die Validierung des neuartigen Ansatzes erfolgt anhand synchroner Messungen des plantaren Fußdrucks. Der mittlere Unterschied zwischen den beiden Erfassungsmethoden liegt sowohl für die Standphasendauer als auch der Gangzyklusdauer unter einer Millisekunde. Studie II zeigt das Potenzial von Beschleunigungssensoren zur Technik- und Leistungsanalyse des Schlittschuhlaufens im Eishockey. Die Ergebnisse zeigen für die Standphasendauer und Schrittintensität sowohl Unterschiede zwischen beschleunigenden Schritten und Schritten bei konstanter Geschwindigkeit als auch zwischen Teilnehmern unterschiedlichen Leistungsniveaus. Eine Korrelationsanalyse offenbart, insbesondere für die Schrittintensität, einen starken Zusammenhang mit der sportlichen Leistung des Schlittschuhlaufens im Sinne einer verkürzten Sprintzeit. Studie III präsentiert ein tragbares System zur Erfassung von Belastungen im Kniegelenk bei verschiedenen sportlichen Bewegungen auf Basis zweier mobiler Sensoren. Im Speziellen werden unterschiedliche lineare Bewegungen, Richtungswechsel und Sprünge betrachtet. Die mittels künstlich neuronalem Netz ermittelten dreidimensionalen Kniegelenkskräfte zeigen, mit Ausnahme der mediolateralen Kraftkomponente, für die meisten analysierten Bewegungen eine gute Übereinstimmung mit invers-dynamisch berechneten Referenzdaten. Die abschließende Studie IV stellt eine Erweiterung des in Studie III entwickelten tragbaren Systems zur Ermittlung von Belastungen im Kniegelenk dar. Die ambulante Beurteilung der Gelenkbelastung bei Kniearthrose steht hierbei im Fokus. Die entwickelten Prädiktionsmodelle zeigen für das Knieflexionsmoment eine gute Übereinstimmung mit invers-dynamisch berechneten Referenzdaten für den Großteil der analysierten Bewegungen. Demgegenüber ist bei der Ermittlung des Knieadduktionsmoments mittels künstlichen neuronalen Netzen Vorsicht geboten. Je nach Bewegung, kommt es zu einer schwachen bis starken Übereinstimmung zwischen der mittels Prädiktionsmodell bestimmten Belastung und dem Referenzwert. Zusammenfassend tragen die Ergebnisse von Studie I und Studie II zur sportartspezifischen Leistungsanalyse im Eishockey bei. Zukünftig können sowohl die Trainingsqualität als auch die gezielte Verbesserung sportlicher Leistung durch den Einsatz von am Körper getragener Sensoren in hohem Maße profitieren. Die methodischen Neuerungen und Erkenntnisse aus Studie III und Studie IV ebnen den Weg für die Entwicklung neuartiger Technologien im Gesundheitsbereich. Mit Blick in die Zukunft können mobile Sensoren zur intelligenten Analyse menschlicher Bewegungen sinnvoll eingesetzt werden. Die vorliegende Dissertation zeigt, dass die mobile Bewegungsanalyse zur Erleichterung der sportartspezifischen Leistungsdiagnostik unter Feldbedingungen beiträgt. Zudem zeigt die Arbeit, dass die mobile Bewegungsanalyse einen wichtigen Beitrag zur Verbesserung der Gesundheitsdiagnostik und Rehabilitation nach akuten Verletzungen oder bei chronischen muskuloskelettalen Erkrankungen leistet

In-field assessment of change-of-direction ability with a single wearable sensor.

The Agility T-test is a standardized method to measure the change-of-direction (COD) ability of athletes in the field. It is traditionally scored based on the total completion time, which does not provide information on the different CODs. Augmenting the T-test with wearable sensors provides the opportunity to explore new metrics. Towards this, data of 23 professional soccer players were recorded with a trunk-worn GNSS-IMU (Global Navigation Satellite System-Inertial Measurement Unit) device. A method for detecting the four CODs based on the wavelet-denoised antero-posterior acceleration signal was developed and validated using video data (60 Hz). Following this, completion time was estimated using GNSS ground speed and validated with the photocell data. The proposed method yields an error (mean ± standard deviation) of 0 ± 66 ms for the COD detection, - 0.16 ± 0.22 s for completion time, and a relative error for each COD duration and each sequential movement durations of less than 3.5 ± 16% and 7 ± 7%, respectively. The presented algorithm can highlight the asymmetric performance between the phases and CODs in the right and left direction. By providing a more comprehensive analysis in the field, this work can enable coaches to develop more personalized training and rehabilitation programs

Relationship between physical capacity and match performance in semiprofessional Australian rules football

This study investigated the relationship between physical performance and match performance in Australian Rules Football (ARF). Thirty-six semiprofessional ARF players participated in this study. Physical capacity was measured using a 3-km time trial. Match performance was measured throughout the 2013 season through 2 methods: direct game involvements (DGIs) per minute and a recording of coaches\u27 vote after the game. The main finding of the study was that 3-km time trial performance was a significant predictor of DGI per minute (p ≤ 0.05). In addition, the number of senior games played was also significant in predicting DGI per minute (p ≤ 0.05). Furthermore, the number of senior games significantly correlated with coaches\u27 votes (p ≤ 0.05). There were no significant relationships between 3-km time trial and coaches\u27 vote. The results highlight the importance of developing physical capacity in the preseason period; the players who were better performers in the 3-km time trial had a greater number of DGIs per minute. This information is important to consider in preseason planning to ensure sufficient time is dedicated to developing physical capacity in the training program, as it is directly associated with performance. In addition, this research also highlights the importance of playing experience in relation to team selection. Playing experience, as measured by the number of senior games played, had a significant relationship with both measures of match performance