237 research outputs found
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
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
Assessment of feedback devices for performance monitoring in master’s swimmers
In recent years, new portable performance monitoring devices have appeared in swimming. The study aims to establish the current validity of the FORM Goggles, Finis Stopwatch, and the Garmin Swim 2 Watch, for the partial and total times and stroke count (experiment 1; n = 17) and to compare the effect of the devices considered as valid in monitoring the pace of master swimmers (experiment 2; n = 10). The FORM Goggles and the Finis Stopwatch showed good level of agreement and accuracy (Bland Altman plots showed homoscedasticity and in most cases Lin’s concordance correlation coefficient were>0.95, and the error magnitude<0.2 seconds). These systems allow better pace control compared to Garmin Swim 2, with a difference between target and actual time below 1.5 %. However, the results showed that the concurrent feedback provided by FORM Smart Swim Goggles could offer greater advantages than the traditional feedback provided via the Finis Stopwatch at the end of each series, as swimmers were closer to the target time (p < 0.05). In conclusion both the FORM Goggles and the Finis Stopwatch, showed a good validity and could serve for performance monitoring in swimming, allowing the Form Goggles better pace control.CTS-527Agencia de Innovación y Desarollo de Andalucía [B-
SEJ-164-UGR20 “SWIM FOR LIFE”
Research progress on wearable devices for daily human health management
As the public’s demand for portable access to personal health information continues to expand, wearable devices are not only widely used in clinical practice, but also gradually applied to the daily health management of ordinary families due to their intelligence, miniaturization, and portability. This paper searches the literature of wearable devices through PubMed and CNKI databases, classifies them according to the different functions realized by wearable devices, and briefly describes the algorithms and specific analysis methods of their applications and made a prospect of its development trend in the field of human health
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
以游泳感測波形演算法分析游泳動作之可行性
[[abstract]]目的:感測科技能精準且即時地呈現動作資訊,穿戴感測裝置更是受到研究與商業應用的關注,逐漸取代了影像與動力學的方法;感測技術應用在游泳領域中,不僅能優化技術提升運動表現,更可提供即時回饋資訊給教練,有助於提升訓練效果;本研究提出一個以配戴慣性感測器即可準確地計算划手次數及動作分類的演算法,期以能應用於游泳教學與訓練中。方法:以12名大專游泳選手為研究對象,分別配戴慣性感測器及G 牌游泳運動手錶進行四式游泳的實驗,演算感測裝置所記錄的資訊,以相對誤差百分比評定兩種感測裝置在游泳划手次數及動作分類之準確率。結果:本研究所提之感測演算法在游泳四式的划手次數準確率達93.64%,在動作判定的準確率為95.8%;而使用游泳運動手錶在划手次數及動作判定的準確率分別為90.24%及85.4%,由結果顯示本研究所提出之方法優於游泳運動手錶之準確率。結論:本研究所設計的方法實作於Android 平台上,所提之感測波形演算法能有效計算划手次數及動作分類,未來可應用於其他週期性的運動項目上。[[notice]]補正完
Influence Technique Training Data sensor (Triton Wear) To improve biomechanical variables for some stages Performance and achievement 50m freestyle youth
Swimming is one of the sports with great muscular effort and complex motor performance that requires coordination and harmony between the movements of the parts of the body contributing to performance that defies the limits of humans. The development of physical abilities and the improvement of technical performance can only be separated from the support of scientific theories and technology, in order to improve training methods and feedback system based on the fusion of information and data using modern sensors and information collection system. On the performance and mechanical conditions of the swimmer to help develop performance and achievement, the researchers used for this purpose a device (Triton Wear data sensor) to collect information and feedback on performance, to prepare special exercises and correct thereal-time performance of 50 m swimmers. The research was conducted on a sample of swimmers amounting to (6) young swimmers whose technical and mechanical data was extracted after wearing the sensor under the head cover of each swimmer, and the performance data was analyzed on this system, and the experiment lasted for six weeks, during which special exercises and feedback were given. Instantaneous based on the sensor data of each member of the sample, and after conducting post-tests, it was shown that the exercises and feedback based on the sensor technology, had a direct impact on the improvement of the average performance of the sample in the 50-meter freestyle race and the improvement of all biomechanical values (such as average speed, frequency and length, acceleration and instantaneous strength) This improvement indicated that data collection and nutrition Using the sensor's multi-information fusion can be applied more precisely and differently in the training of different swimming races, and can find many motor problems in sports swimmers to describe training solutions for them, both for specific parts of the body and the body as a whole. Techniques for accurately identifying this valuable information can therefore be used for quantitative biomechanical analysis. And to reach the training process
A novel approach to identify and quantify activity and performance in wheelchair rugby
Existing methods for performance and activity monitoring of court-based wheelchair sports such as wheelchair rugby during actual matches have their limitations. They either require too much manual efforts or they gather insufficient information. Inertia sensors have the ability to measure substantial amounts of movement data but there is no known method to decipher that huge amount of data for quantifying activity or performance. Based on literature, Fractal dimensions have been applied to signals of physical parameters measured as a time series in the field of sports, biomedical and manufacturing. In all these cases Fractal dimensions of the time-based signals were able to identify different states or conditions accurately. There are several methods of determining Fractal dimensions and for this study, two were narrowed down – one based on Renyi’s generalized dimension (S0) and the other based on Hausdorff dimension (DH). A feasibility study was first conducted to investigate the Fractal dimensions of forward accelerations during manual wheelchair pushing using the two methods. The outcome showed that generally higher Fractal dimension values were linked to higher push amplitudes and frequencies or a higher activeness level. It was identified that S0 related to energy released to the environment while DH showed a connection with activity level. This was then taken further by capturing forward/backward accelerations of wheelchairs during actual wheelchair rugby matches. S0 and DH were calculated from the acceleration data, and four methods were developed using S0 and DH values to identify and quantify activity and performance of the wheelchair rugby athletes. Those methods include cumulative plots of S0 and DH; a Decision template formed using a 2D plot of S0 against DH, and Activities Ranking that is also based on the 2D plot. After the basic process of the methods was developed, steps were taken to optimize the values of S0 and DH such that they are optimal for the identification and quantification outcome of wheelchair rugby activities. The factors that influence S0 and DH values include parameters of the inertia sensing device (sensor resolution and sampling rate), running average window width and amplitude multiplier for calculating DH. In the end, although the number of athletes that were tested was small, the analysis outcome supported results from previous studies where athletes of higher functional classifications showed higher performance. The analysis of activity ranking which had an accuracy of 95% also highlighted that difference in activities between the athletes related highly with their functional classifications and their role or position in the team. The results of the analysis proved to be relevant for coaching, planning matches and even for talent identification
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