Differences in swimming stroke mechanics and kinematics derived from tri-axial accelerometers during a 200-IM event in South African national swimmers

Context: Swimming is a highly competitive sport, with elite swimmers and coaches constantly looking for ways to improve and challenge themselves to meet new performance goals. The implementation of technology in swimming has proven to be a vital tool in athlete monitoring and in providing coaches with additional information on the swimmer’s performance. Example of this technology is the use of inertial sensory devices such as tri-axial accelerometers. The accelerometers can be used to provide kinematic information with regards to the swimmer’s stroke rate, stroke length and stroke mechanics. In a typical training session, coaches would have to manually time and count their swimmer’s strokes to be able to gain the kinematic information they require. Hence, the use ofinertial sensory technology, such as accelerometers, would provide the necessary information coaches require, allowing them to concentrate on other performance aspects such as theirswimmer’s technique.Aim and objectives: The aim of this study was to determine the kinematic parameters and swimming stroke mechanics that could be derived from tri-axial accelerometers, during a 200-m individual medley (IM) event in South African national level swimmers. Three objectives were set to meet the aim of the study. The first was to identify and differentiate each of the stroking styles using tri-axial accelerometers. The second was to identify and differentiate the kinematic parametersand stroke mechanicsfor all four strokes using tri-axial accelerometers. The third objective was to implement machine learning to automate the identification and interpretation of the accelerometer data. Method:A quantitative, non-experimental descriptive one group post-test only design was used, in which 15 national level swimmers, of which seven male and eight female (mean ±SD: age: 20.9 ± 2.90 years; height: 173.28 ± 10.61 cm; weight: 67.81 ± 8.09 kg; arm span: 178.21 ± 12.15 cm) were tested. Three anthropometric measures were taken (height, weight and arm span) prior to testing, with two tri-axial accelerometers and Polar V800watch and heart rate belt attached to the swimmers left wrist, upper-back and chest, respectively. All swimmerswere required to perform three main swimming sets: 50-m IM, 100-m variation and 200-mIM. Variousdescriptivestatisticsincluding mean, standard deviation and confidence intervals (95%)were used to describe the data. with further inferential statistics including paired t-test, intra-class correlation and Bland Altman analysis wereused to describe the relationship ivbetween the accelerometer and the manually estimated parameters. Additionally, arepeated measures one-way ANOVA (with post-hoc Tukey HSD test) werealso used in an inter-comparison of the stroke parameters between each of the stroking styles. A confusion matrix wasused to measure the classification accuracy of the machine learning model implemented on the accelerometer data.Results:The accelerometers proved successful in identifyingand discerningthe stroke mechanics for each of the four stroking styles, with the use of video footage to validatethe findings. In the stroke kinematic differentiation, theBland Altman analysisresultsshowed an agreement between themanual method and accelerometer-derived estimates, although a discrepancy was evident for several of the kinematic parameters, with a significant difference found with the estimated lap time, average swimming velocity and stroke rate (paired t-test: p 0.05for all strokes)andbetween freestyle and backstroke for the average stroke rate and stroke length (Tukey:p = 0.0968 andp = 0.997, respectively).Lastly, the machine learning model found a classification accuracy of 96.6% in identifyingand labelling the stroking styles fromthe accelerometer data.Conclusion: It was shown that the tri-axial accelerometers were successful in the identification and differentiation of all the stroking styles, stroke mechanics and kinematics, although a discrepancy was found with the average swimming velocity, stroke rate and lap time estimations. The machine learning model implemented proved the benefits of using artificial intelligence to ease the data process and interpretation by automatically labelling the accelerometer data. Therefore, the use of tri-axial accelerometers as a coaching aid has major potential in the swimming community. However, further research is required to eliminate the time-consuming data processingand to increasetheaccuracy of the accelerometer in the measurement of all the stroke kinematics

The Real-Time Classification of Competency Swimming Activity Through Machine Learning

Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile device in real-time. This paper discusses the sensor placement, the hardware and app design, and the research process utilized to achieve activity recognition. For our studies, the data we have gathered comes from various swimming skill levels, from beginner to elite swimmers. Our wearable system uses angle-based novel features as inputs into optimal machine learning algorithms to classify flip turns, traditional competitive strokes, and survival swimming strokes. The machine-learning algorithm was able to classify all activities at .935 of an F-measure. Finally, we examined deep learning and created a CNN model to classify competitive and survival swimming strokes at 95% ac- curacy in real-time on a mobile device

Three-dimensional joint kinematics of swimming using body-worn inertial and magnetic sensors

Wearable inertial and magnetic measurements units (IMMU) are an important tool for underwater motion analysis because they are swimmer-centric, they require only simple measurement set-up and they provide the performance results very quickly. In order to estimate 3D joint kinematics during motion, protocols were developed to transpose the IMMU orientation estimation to a biomechanical model. The aim of the thesis was to validate a protocol originally propositioned to estimate the joint angles of the upper limbs during one-degree-of-freedom movements in dry settings and herein modified to perform 3D kinematics analysis of shoulders, elbows and wrists during swimming. Eight high-level swimmers were assessed in the laboratory by means of an IMMU while simulating the front crawl and breaststroke movements. A stereo-photogrammetric system (SPS) was used as reference. The joint angles (in degrees) of the shoulders (flexion-extension, abduction-adduction and internal-external rotation), the elbows (flexion-extension and pronation-supination), and the wrists (flexion-extension and radial-ulnar deviation) were estimated with the two systems and compared by means of root mean square errors (RMSE), relative RMSE, Pearson’s product-moment coefficient correlation (R) and coefficient of multiple correlation (CMC). Subsequently, the athletes were assessed during pool swimming trials through the IMMU. Considering both swim styles and all joint degrees of freedom modeled, the comparison between the IMMU and the SPS showed median values of RMSE lower than 8°, representing 10% of overall joint range of motion, high median values of CMC (0.97) and R (0.96). These findings suggest that the protocol accurately estimated the 3D orientation of the shoulders, elbows and wrists joint during swimming with accuracy adequate for the purposes of research. In conclusion, the proposed method to evaluate the 3D joint kinematics through IMMU was revealed to be a useful tool for both sport and clinical contexts

Quantification of performance analysis factors in front crawl swimming using micro electronics: a data rich system for swimming.

The aim of this study is to increase the depth of data available to swimming coaches in order to allow them to make more informed coaching decisions for their athletes in front crawl swimming. A coach’s job is to assist with various factors of an individual athlete to allow them to perform at an optimum level. The demands of the swimming coach require objective data on the swim performance in order to offer efficient solutions (Burkett and Mellifont, 2008). The main tools available to a coach are their observation and perceptions, however it is known that these used alone can often result in poor judgment. Technological progress has allowed video cameras to become an established technology for swim coaching and more recently when combined with software, for quantitative measurement of changes in technique. This has allowed assessment of swimming technique to be included in the more general discipline of sports performance analysis. Within swimming, coaches tend to observe from the pool edge, limiting vision of technique, but some employ underwater cameras to combat this limitation. Video cameras are a reliable and established technology for the measurement of kinematic parameters in sport, however, accelerometers are increasingly being employed due to their ease of use, performance, and comparatively low cost. Previous accelerometer based studies in swimming have tended to focus on easily observable factors such as stroke count, stroke rate and lap times. To create a coaching focused system, a solution to the problem of synchronising multiple accelerometers was developed using a maxima detection method. Results demonstrated the effectiveness of the method with 52 of 54 recorded data sets showing no time lag error and two tests showing an error of 0.04s. Inter-instrument and instrument-video correlations are all greater than r = .90 (p < .01), with inter-instrument precision (Root Mean Square Error; RMSE) ≈ .1ms−2, demonstrating the efficacy of the technique. To ensure the design was in line with coaches' expectations and with the ASA coaching guidelines, interviews were conducted with four ASA swim coaches. Results from this process identified the factors deemed important: lap time, velocity, stroke count, stroke rate, distance per stroke, body roll angle and the temporal aspects of the phases of the stroke. These factors generally agreed with the swimming literature but extended upon the general accelerometer system literature. Methods to measure these factors were then designed and recorded from swimmers. The data recorded from the multi-channel system was processed using software to extract and calculate temporal maxima and minima from the signal to calculate the factors deemed important to the coach. These factors were compared to video derived data to determine the validity and reliability of the system, all results were valid and reliable. From these validated factors additional factors were calculated, including, distance per stroke and index of coordination and the symmetry of these factors. The system was used to generate individual profiles for 12 front crawl swimmers. The system produced eight full profiles with no issues. Four profiles required individualisation in the processing algorithm for the phases of the stroke. This was found to be due to the way in which these particular swimmers varied in the way they fatigued. The outputs from previous systems have tended to be either too complicated for a coach to understand and interpret e.g. raw data (Ohgi et al. 2000), or quite basic in terms of output e.g. stroke rate and counts (Le Sage et al. 2011). This study has added to the current literature by developing a system capable of calculating and displaying a breadth of factors to a coach. The creation of this system has also created a biomechanical research tool for swimming, but the process and principles can be applied to other sports. The use of accelerometers was also shown to be particularly useful at recording temporal activities within sports activities. Using PC based processing allows for quick turnaround times in the processing of detailed results of performance. There has been substantial development of scientific knowledge in swimming, however, the exchange of knowledge between sport science and coaches still requires development (Reade et al. 2008; Williams and Kendall 2007). This system has started to help bridge the gap between science and coaching, however there is still substantial work needed. This includes a better understanding of the types of data needed, how these can be displayed and level of detail required by the coach to allow them to enact meaningful coaching programmes for their athletes

Inertial Sensors in Swimming: Detection of Stroke Phases through 3D Wrist Trajectory.

Monitoring the upper arm propulsion is a crucial task for swimmer performance. The swimmer indeed can produce displacement of the body by modulating the upper limb kinematics. The present study proposes an approach for automatically recognize all stroke phases through three-dimensional (3D) wrist\u2019s trajectory estimated using inertial devices. Inertial data of 14 national-level male swimmer were collected while they performed 25 m front-crawl trial at intensity range from 75% to 100% of their 25 m maximal velocity. The 3D coordinates of the wrist were computed using the inertial sensors orientation and considering the kinematic chain of the upper arm biomechanical model. An algorithm that automatically estimates the duration of entry, pull, push, and recovery phases result from the 3D wrist\u2019s trajectory was tested using the bi-dimensional (2D) video-based systems as temporal reference system. A very large correlation (r = 0.87), low bias (0.8%), and reasonable Root Mean Square error (2.9%) for the stroke phases duration were observed using inertial devices versus 2D video-based system methods. The 95% limits of agreement (LoA) for each stroke phase duration were always lower than 7.7% of cycle duration. The mean values of entry, pull, push and recovery phases duration in percentage of the complete cycle detected using 3D wrist\u2019s trajectory using inertial devices were 34.7 (\ub1 6.8)%, 22.4 (\ub1 5.8)%, 14.2 (\ub1 4.4)%, 28.4 (\ub1 4.5)%. The swimmer\u2019s velocity and arm coordination model do not affect the performance of the algorithm in stroke phases detection. The 3D wrist trajectory can be used for an accurate and complete identification of the stroke phases in front crawl using inertial sensors. Results indicated the inertial sensor device technology as a viable option for swimming arm-stroke phase assessment

Context Aware Computing or the Sense of Context

ITALIANO: I sistemi ubiquitous e pervasivi, speciali categorie di sistemi embedded (immersi), possono essere utilizzati per rilevare il contesto che li circonda. In particolare, i sistemi context-aware sono in grado di alterare il loro stato interno e il loro comportamento in base all’ambiente (context) che percepiscono. Per aiutare le persone nell’espletare le proprie attivitá, tali sistemi possono utilizzare le conoscenze raccolte attorno a loro. Un grande sforzo industriale e di ricerca, orientato all’innovazione dei sensori, processori, sistemi operativi, protocolli di comunicazione, e framework, offre molte tecnologie definibili abilitanti, come le reti di sensori wireless o gli Smartphone. Tuttavia, nonostante tale sforzo significativo, l’adozione di sistemi pervasivi che permettano di migliorare il monitoraggio dello sport, l’allenamento e le tecnologie assistive é ancora piuttosto limitato. Questa tesi individua due fattori determinanti per questo basso utilizzo delle tecnologie pervasive, principalmente relativi agli utenti. Da un lato il tentativo degli esperti e dei ricercatori dell’informatica di indurre l’adozione di soluzioni informatiche, trascurando parzialmente l’interazione con gli utenti finali, dall’altro lato una scarsa attenzione all’interazione tra uomo e computer. Il primo fattore puó essere tradotto nella mancanza di attenzione a ció che é rilevante nel contesto dei bisogni (speciali) dell’utente. Il secondo é rappresentato dall’utilizzo diffuso di interfacce grafiche di presentazione delle informazioni, che richiede un elevato livello di sforzo cognitivo da parte degli utenti. Mentre lo studio della letteratura puó fornire conoscenze sul contesto dell’utente, solo il contatto diretto con lui arricchisce la conoscenza di consapevolezza, fornendo una precisa identificazione dei fattori che sono piú rilevanti per il destinatario dell’applicazione. Per applicare con successo le tecnologie pervasive al campo dello sport e delle tecnologie assistive, l’identificazione dei fattori rilevanti é una premessa necessaria, Tale processo di identificazione rappresenta l’approccio metodologico principale utilizzato per questa tesi. Nella tesi si analizzano diversi sport (canottaggio, nuoto, corsa ) e una disabilitá (la cecitá), per mostrare come la metodologia di investigazione e di progettazione proposta venga messa in pratica. Infatti i fattori rilevanti sono stati identificati grazie alla stretta collaborazione con gli utenti e gli esperti nei rispettivi campi. Si descrive il processo di identificazione, insieme alle soluzioni elaborate su misura per il particolare campo d’uso. L’uso della sonificazione, cioé la trasmissione di informazioni attraverso il suono, si propone di affrontare il secondo problema presentato, riguardante le interfacce utente. L’uso della sonificazione puó facilitare la fruizione in tempo reale delle informazioni sulle prestazioni di attivitá sportive, e puó contribuire ad alleviare parzialmente la disabilitá degli utenti non vedenti. Nel canottaggio, si é identificato nel livello di sincronia del team uno dei fattori rilevanti per una propulsione efficace dell’imbarcazione. Il problema di rilevare il livello di sincronia viene analizzato mediante una rete di accelerometri wireless, proponendo due diverse soluzioni. La prima soluzione é basata sull’indice di correlazione di Pearson e la seconda su un approccio emergente chiamato stigmergia. Entrambi gli approcci sono stati testati con successo in laboratorio e sul campo. Inoltre sono state sviluppate due applicazioni, per smartphone e PC, per fornire la telemetria e la sonificazione del moto di una barca a remi. Nel campo del nuoto é stata condotta una ricerca in letteratura riguardo la convinzione diffusa di considerare la cinematica come il fattore rilevante della propulsione efficace dei nuotatori. Questa indagine ha richiamato l’attenzione sull’importanza di studiare il cosiddetto feel-for-water (sensazione-dell’-acqua) percepito dai nuotatori d’alto livello. É stato progettato un innovativo sistema, per rilevare e comunicare gli effetti fluidodinamici causati dallo spostamento delle masse d’acqua intorno alle mani dei nuotatori. Il sistema é in grado di trasformare la pressione dell’acqua, misurata con sonde Piezo intorno alle mani, in un bio-feedback auditivo, pensato per i nuotatori e gli allenatori, come base per un nuovo modo di comunicare la sensazione-dell’acqua. Il sistema é stato testato con successo nel campo e ha dimostrato di fornire informazioni in tempo reale per il nuotatore e il formatore. Nello sport della corsa sono stati individuati due parametri rilevanti: il tempo di volo e di contatto dei piedi. É stato progettato un sistema innovativo per ottenere questi parametri attraverso un unico accelerometro montato sul tronco del corridore ed é stato implementato su uno smartphone. Per ottenere il risultato voluto é stato necessario progettare e realizzare un sistema per riallineare virtualmente gli assi dell’accelerometro e per estrarre il tempo di volo e di contatto dal segnale dell’accelerometro riallineato. L’applicazione per smartphone completa é stata testata con successo sul campo, confrontando i valori con quelli di attrezzature specializzate, dimostrando la sua idoneitá come ausilio pervasivo all’allenamento di corridori. Per esplorare le possibilitá della sonificazione usata come una base per tecnologia assistiva, abbiamo iniziato una collaborazione con un gruppo di ricerca presso l’Universitá di Scienze Applicate, Ginevra, in Svizzera. Tale collaborazione si é concentrata su un progetto chiamato SeeColOr (See Color with an Orchestra - vedere i colori con un’orchestra). In particolare, abbiamo avuto l’opportunitá di implementare il sistema SeeColOr su smartphone, al fine di consentire agli utenti non vedenti di utilizzare tale tecnologia su dispositivi leggeri e a basso costo. Inoltre, la tesi esplora alcune questioni relative al campo del rilevamento ambientale in ambienti estremi, come i ghiacciai, utilizzando la tecnologia delle Wireless Sensor Networks. Considerando che la tecnologia é simile a quella usata in altri contesti presentati, le considerazioni possono facilmente essere riutilizzate. Si sottolinea infatti che i problemi principali sono legati alla elevata difficoltá e scarsa affidabilitá di questa tecnologia innovativa rispetto alle altre soluzioni disponibili in commercio , definite legacy, basate solitamente su dispositivi piú grandi e costosi, chiamati datalogger. La tesi presenta i problemi esposti e le soluzioni proposte per mostrare l’applicazione dell’approccio progettuale cercato e definito durante lo sviluppo delle attività sperimentali e la ricerca che le ha implementate. ---------------------------------------- ENGLISH: Ubiquitous and pervasive systems, special categories of embedded systems, can be used to sense the context in their surrounding. In particular, context-aware systems are able to alter their internal state and their behaviour based on the context they perceive. To help people in better performing their activities, such systems must use the knowledge gathered about the context. A big research and industrial effort, geared towards the innovation of sensors, processors, operating systems, communication protocols, and frameworks, provides many "enabling" technologies, such as Wireless Sensor Networks or Smartphones. However, despite that significant effort, the adoption of pervasive systems to enhance sports monitoring, training and assistive technologies is still rather small. This thesis identifies two main issues concerning this low usage of pervasive technologies, both mainly related to users. On one side the attempt of computer science experts and researchers to induce the adoption of information technology based solutions, partially neglecting interaction with end users; on the other side a scarce attention to the interaction between humans and computers. The first can be translated into the lack of attention at what is relevant in the context of the user’s (special) needs. The second is represented by the widespread usage of graphical user interfaces to present information, requiring a high level of cognitive effort. While literature studies can provide knowledge about the user’s context, only direct contact with users enriches knowledge with awareness, providing a precise identification of the factors that are more relevant to the user. To successfully apply pervasive technologies to the field of sports engineering and assistive technology, the identification of relevant factors is an obliged premise, and represents the main methodological approach used throughout this thesis. This thesis analyses different sports (rowing, swimming, running) and a disability (blindness), to show how the proposed design methodology is put in practice. Relevant factors were identified thanks to the tight collaboration with users and experts in the respective fields. The process of identification is described, together with the proposed application tailored for the special field. The use of sonification, i.e. conveying information as sound, is proposed to leverage the second presented issue, that regards the user interfaces. The usage of sonification can ease the exploitation of information about performance in real-time for sport activities and can help to partially leverage the disability of blind users. In rowing, the synchrony level of the team was identified as one of the relevant factors for effective propulsion. The problem of detecting the synchrony level is analysed by means of a network of wireless accelerometers, proposing two different solutions. The first solution is based on Pearson’s correlation index and the second on an emergent approach called stigmergy. Both approaches were successfully tested in laboratory and in the field. Moreover two applications, for smartphones and PCs, were developed to provide telemetry and sonification of a rowing boat’s motion. In the field of swimming, an investigation about the widespread belief considering kinematics as the relevant factor of effective propulsion of swimmers drew attention to the importance of studying the so called "feel-for-water" experienced by elite swimmers. An innovative system was designed to sense and communicate fluid-dynamic effects caused by moving water masses around swimmers hands. The system is able to transform water pressure, measured with Piezo-probes, around hands into an auditive biofeedback, to be used by swimmers and trainers, as the base for a new way of communication about the "feel-for-water". The system was successfully tested in the field and proved to provide real-time information for the swimmer and the trainer. In running sports two relevant parameters are time of flight and contact of feet. An innovative system was designed to obtain these parameters using a single trunk mounted accelerometer and was implemented on a smartphone. To achieve the intended result it was necessary to design and implement a system to virtually realign the axes of the accelerometer and to extract time of flight and time of contact phases from the realigned accelerometer signal. The complete smartphone application was successfully tested in the field with specialized equipment, proving its suitability in enhancing training of runners with a pervasive system. To explore possibilities of sonification applied as an assistive technology, we started a collaboration with research group from University of Applied Science, Geneva, Switzerland, focused on a project called SeeColOr (See Color with an Orchestra). In particular we had the opportunity to implement the SeeColOr system on smartphones, in order to enable blind users to use that technology on low cost and lightweight devices. Moreover, the thesis exposes some issues related to a field, environmental sensing in extreme environments, like glaciers, using the innovative Wireless Sensor Networks technology. Considering that the technology is similar to the one used in other presented contexts, learned lessons can easily be reused. It is emphasized that the main problems are related to the high difficulty and low reliability of that innovative technology with respect to other "legacy" commercially available solutions, based on expensive and bigger devices, called dataloggers. The thesis presents the exposed problems and proposed solutions to show the application of the design approach strived during the development and research

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

In the last few decades, a number of technological developments have advanced the spread of wearable sensors for the assessment of human motion. These sensors have been also developed to assess athletes’ performance, providing useful guidelines for coaching, as well as for injury prevention. The data from these sensors provides key performance outcomes as well as more detailed kinematic, kinetic, and electromyographic data that provides insight into how the performance was obtained. From this perspective, inertial sensors, force sensors, and electromyography appear to be the most appropriate wearable sensors to use. Several studies were conducted to verify the feasibility of using wearable sensors for sport applications by using both commercially available and customized sensors. The present study seeks to provide an overview of sport biomechanics applications found from recent literature using wearable sensors, highlighting some information related to the used sensors and analysis methods. From the literature review results, it appears that inertial sensors are the most widespread sensors for assessing athletes’ performance; however, there still exist applications for force sensors and electromyography in this context. The main sport assessed in the studies was running, even though the range of sports examined was quite high. The provided overview can be useful for researchers, athletes, and coaches to understand the technologies currently available for sport performance assessment

Effect of tumble turns on swimming performance in level 3 swimmers

Magister Artium (Sport, Recreation and Exercise Science) - MA(SRES)Swimming, as a sport, is constantly developing, both through the resources employed in training and assessment, and through the technological development of the fundamental aspects of swimming. In the freestyle events, swimmers spend between 38% and 50% of their competition time executing turns in short pool competitions over distances that vary from 50 m to 1500 m. The importance of the turn has been noted and analyzed for several decades, where it was found that the final turn velocity was second only to mid-pool swimming velocity for determining a medal finish in the men’s race. Due to the impact that the tumble turn has on swimming performance, the present study investigated the importance of the tuck index, foot-plant index and wall-contact time (WCT) on swimming performance. Therefore, the aim of this study was to determine the effect of the tuck index, foot-plant index, and WCT on the round trip time (RTT) in the tumble turn performance in level 3 swimmers in the freestyle swimming stroke. A quantitative cross-sectional and descriptive design was used in this study. A convenient sample of ten (10) swimmers were tested, five male and five female, all being level 3 swimmers affiliated to the high performance team of Swimming South Africa (SSA). Video analyses of the turns were recorded. Each subject performed thirty (30) trials, each consisting of a 50 m freestyle swim with flip turns at race pace. Descriptive statistics and multiple stepwise regression analyses were used to analyse the data. A p-value of below 0.05 indicated statistical significance. The mean tuck index was 0.57 ± 0.14°. The mean foot-plant index was 0.45 ± 0.10 cm. The mean WCT was 74.31 ± 11.57 %. The mean RTT was 2.47 ± 0.40 s. A significant negative correlation was found between tuck index and RTT (r = -0.41; p < 0.05). No significant relationship was found between foot-plant and WCT. Further regression analysis showed that the tuck index was a significant predictor of RTT (F = 21.745, p < 0.001). Following the freestyle tumble turn, the flutter kick technique remained the superior method of exiting the wall, based on the 5 m RTT. Therefore, the introduction of optimal turning practice for age-group swimmers is likely to result in significant reductions in turning times and should be noted by coaches and swimmers alike

Geometric data understanding : deriving case specific features

There exists a tradition using precise geometric modeling, where uncertainties in data can be considered noise. Another tradition relies on statistical nature of vast quantity of data, where geometric regularity is intrinsic to data and statistical models usually grasp this level only indirectly. This work focuses on point cloud data of natural resources and the silhouette recognition from video input as two real world examples of problems having geometric content which is intangible at the raw data presentation. This content could be discovered and modeled to some degree by such machine learning (ML) approaches like deep learning, but either a direct coverage of geometry in samples or addition of special geometry invariant layer is necessary. Geometric content is central when there is a need for direct observations of spatial variables, or one needs to gain a mapping to a geometrically consistent data representation, where e.g. outliers or noise can be easily discerned. In this thesis we consider transformation of original input data to a geometric feature space in two example problems. The first example is curvature of surfaces, which has met renewed interest since the introduction of ubiquitous point cloud data and the maturation of the discrete differential geometry. Curvature spectra can characterize a spatial sample rather well, and provide useful features for ML purposes. The second example involves projective methods used to video stereo-signal analysis in swimming analytics. The aim is to find meaningful local geometric representations for feature generation, which also facilitate additional analysis based on geometric understanding of the model. The features are associated directly to some geometric quantity, and this makes it easier to express the geometric constraints in a natural way, as shown in the thesis. Also, the visualization and further feature generation is much easier. Third, the approach provides sound baseline methods to more traditional ML approaches, e.g. neural network methods. Fourth, most of the ML methods can utilize the geometric features presented in this work as additional features.Geometriassa käytetään perinteisesti tarkkoja malleja, jolloin datassa esiintyvät epätarkkuudet edustavat melua. Toisessa perinteessä nojataan suuren datamäärän tilastolliseen luonteeseen, jolloin geometrinen säännönmukaisuus on datan sisäsyntyinen ominaisuus, joka hahmotetaan tilastollisilla malleilla ainoastaan epäsuorasti. Tämä työ keskittyy kahteen esimerkkiin: luonnonvaroja kuvaaviin pistepilviin ja videohahmontunnistukseen. Nämä ovat todellisia ongelmia, joissa geometrinen sisältö on tavoittamattomissa raakadatan tasolla. Tämä sisältö voitaisiin jossain määrin löytää ja mallintaa koneoppimisen keinoin, esim. syväoppimisen avulla, mutta joko geometria pitää kattaa suoraan näytteistämällä tai tarvitaan neuronien lisäkerros geometrisia invariansseja varten. Geometrinen sisältö on keskeinen, kun tarvitaan suoraa avaruudellisten suureiden havainnointia, tai kun tarvitaan kuvaus geometrisesti yhtenäiseen dataesitykseen, jossa poikkeavat näytteet tai melu voidaan helposti erottaa. Tässä työssä tarkastellaan datan muuntamista geometriseen piirreavaruuteen kahden esimerkkiohjelman suhteen. Ensimmäinen esimerkki on pintakaarevuus, joka on uudelleen virinneen kiinnostuksen kohde kaikkialle saatavissa olevan datan ja diskreetin geometrian kypsymisen takia. Kaarevuusspektrit voivat luonnehtia avaruudellista kohdetta melko hyvin ja tarjota koneoppimisessa hyödyllisiä piirteitä. Toinen esimerkki koskee projektiivisia menetelmiä käytettäessä stereovideosignaalia uinnin analytiikkaan. Tavoite on löytää merkityksellisiä paikallisen geometrian esityksiä, jotka samalla mahdollistavat muun geometrian ymmärrykseen perustuvan analyysin. Piirteet liittyvät suoraan johonkin geometriseen suureeseen, ja tämä helpottaa luonnollisella tavalla geometristen rajoitteiden käsittelyä, kuten väitöstyössä osoitetaan. Myös visualisointi ja lisäpiirteiden luonti muuttuu helpommaksi. Kolmanneksi, lähestymistapa suo selkeän vertailumenetelmän perinteisemmille koneoppimisen lähestymistavoille, esim. hermoverkkomenetelmille. Neljänneksi, useimmat koneoppimismenetelmät voivat hyödyntää tässä työssä esitettyjä geometrisia piirteitä lisäämällä ne muiden piirteiden joukkoon