Academic literature on the topic 'Swimming monitoring device'

A prototype swimming tachometer is described which consists of a waterproof box housing a battery-powered electronic system linked externally to an opto-electronic velocity transducer. The device is strapped to the hips, where it monitors water flow to produce continuous measurements of two critical variables of swimming performance, namely, velocity and acceleration. These measurements are converted in real time to auditory feedback signals to the subject via an ear plug. Permanent records may be taken simultaneously as an option using a switched external line.

We report the development of an acoustic telemetry tag used to monitor electromyograms (EMGs) remotely from free-swimming marine fish. The device described amplifies and filters the EMG and then converts the electrical waveform into a frequency-modulated acoustic signal that is transmitted through water. The signal is then received, demodulated and recorded by the receiving system. The EMG tag described has been tested on a range of species, including toadfish Opsanus τ, spiny dogfish Squalus acanthias, yellowfin tuna Thunnus albacares and eastern Pacific bonito Sarda chiliensis, in different tank environments. In certain tanks the fidelity with which the system replicates the EMG is sufficient to quantify accurately the onset, offset, duration, the integrated area under the absolute value of the signal and the number of signal zero crossings. This EMG tag will expand the scope of questions that can be addressed about the behavior and physiology of free-swimming fish.

AbstractWith the goal of automatic monitoring of environments along natural coastal areas and tidal flats, researchers designed and developed an amphibious robot equipped with fin actuators called “RT-I” that mimics the locomotion of both a tortoise and a sea turtle. Experiments were carried out using a forearm with 4 degrees of freedom, which can reproduce the walking motions of tortoises and sea turtles on sand, to evaluate the walking performances of a robotic tortoise and a robotic sea turtle. It was clarified that the arm for a robotic tortoise is more suitable for use on soil compared with the arm for a robotic sea turtle. The advantages of both sea turtles and tortoises were adopted in a robotic turtle, namely, the lift-based swimming mode sea turtles use and the quadrupedal locomotion tortoises use. The present amphibious robot consists of four main components: (i) leg units, (ii) a control unit pressure hull, (iii) a buoyancy adjusting device, and (iv) a fairing cover. To realize not only swimming motion with the combination of flapping, rowing, and feathering, but also tortoise-like walking motion, three motors were set up at the acromioclavicular joint using a differential gear mechanism to independently produce the three types of motion, and one motor was set up to produce elbow joint motion. A buoyancy-adjusting device was installed to realize walking on land and in water as well as swimming in shallow water. The swimming and walking performances of the amphibious robot in water were evaluated by measuring the forward swimming speed, backward swimming speed, speed of turning, and speed of descending vertically as the indexes of the maneuverability of the robotic turtle, and the walking speed and propulsive efficiency with the crawl gait for various walking patterns in still water and in waves.

Recently, integrating the Internet of Things (IoT) and computer vision has been utilized in swimming pool automated surveillance systems. Several studies have been proposed to overcome off-time surveillance drowning incidents based on using a sequence of videos to track human motion and position. This paper proposes an efficient and reliable detection system that utilizes a single image to detect and classify drowning objects, to prevent drowning incidents. The proposed system utilizes the IoT and transfer learning to provide an intelligent and automated solution for off-time monitoring swimming pool safety. In addition, a specialized transfer-learning-based model utilizing a model pretrained on “ImageNet”, which can extract the most useful and complex features of the captured image to differentiate between humans, animals, and other objects, has been proposed. The proposed system aims to reduce human intervention by processing and sending the classification results to the owner’s mobile device. The performance of the specialized model is evaluated by using a prototype experiment that achieves higher accuracy, sensitivity, and precision, as compared to other deep learning algorithms.

The most common cause of diseases in swimming pools is the lack of sanitary control of water quality; water may contain microbiological and chemical contaminants. Among the people most at risk of infection are children, pregnant women, and immunocompromised people. The origin of the problem is a need to develop a system that can predict the formation of chlorine water disinfection by-products, such as trihalomethanes (THMs). THMs are volatile organic compounds from the group of alkyl halides, carcinogenic, mutagenic, teratogenic, and bioaccumulating. Long-term exposure, even to low concentrations of THM in water and air, may result in damage to the liver, kidneys, thyroid gland, or nervous system. This article focuses on analysis of the kinetics of swimming pool water reaction in analytical device reproducing its circulation on a small scale. The designed and constructed analytical device is based on the SIMATIC S7-1200 PLC driver of SIEMENS Company. The HMI KPT panel of SIEMENS Company enables monitoring the process and control individual elements of device. Value of the reaction rate constant of free chlorine decomposition gives us qualitative information about water quality, it is also strictly connected to the kinetics of the reaction. Based on the experiment results, the value of reaction rate constant was determined as a linear change of the natural logarithm of free chlorine concentration over time. The experimental value of activation energy based on the directional coefficient is equal to 76.0 [kJ×mol−1]. These results indicate that changing water temperature does not cause any changes in the reaction rate, while it still affects the value of the reaction rate constant. Using the analytical device, it is possible to constantly monitor the values of reaction rate constant and activation energy, which can be used to develop a new way to assess pool water quality.

Background and aim: Monitoring bioelectric phase angle (PhA) provides important information on the health and the condition of the athlete. Together with the vector length, PhA constitutes the bioimpedance vector analysis (BIVA) patterns, and their joint interpretation exceeds the limits of the evaluation of the PhA alone. The present investigation aimed to monitor changes in the BIVA patterns during a training macrocycle in swimmers, trying to ascertain if these parameters are sensitive to training load changes across a 13-week training period. Methods: Twelve national and international level swimmers (four females; eight males; 20.9 ± 1.9 years; with a competitive swimming background of 11.3 ± 1.8 years; undertaking 16–20 h of pool training and 4–5 h of dry-land training per week and 822.0 ± 59.0 International Swimming Federation (FINA) points) were evaluated for resistance (R) and reactance (Xc) using a single frequency phase sensitive bioimpedance device at the beginning of the macrocycle (M1), just before the beginning of the taper period (M2), and just before the main competition of the macrocycle (M3). At the three-time assessment points, swimmers also performed a 50 m all-out first stroke sprint with track start (T50 m) while time was recorded. Results: The results of the Hotelling T2 test showed a significant vector displacement due to simultaneous R and Xc changes (p < 0.001), where shifting from top to bottom along the major axis of the R-Xc graph from M1 to M2 was observed. From M2 to M3, a vector displacement up and left along the minor axis of the tolerance ellipses resulted in an increase in PhA (p < 0.01). The results suggest a gain in fluid with a decrease in cellular density from M1 to M2 due to decrements in R and Xc. Nevertheless, the reduced training load characterizing taper seemed to allow for an increase in PhA and, most importantly, an increase of Xc, thus demonstrating improved cellular health and physical condition, which was concomitant with a significant increase in the T50 m performance (p < 0.01). Conclusions: PhA, obtained by bioelectrical R and Xc, can be useful in monitoring the condition of swimmers preparing for competition. Monitoring BIVA patterns allows for an ecological approach to the swimmers’ health and condition assessment without resorting to equations to predict the related body composition variables.

Heart rate monitoring in free-ranging cetaceans to understand their behavioural ecology and diving physiology is challenging. Here, we developed a simple, non-invasive method to monitor the heart rate of cetaceans in the field using an electrocardiogram-measuring device and a single suction cup equipped with an electrode. The unipolar suction cup was placed on the left lateral body surface behind the pectoral fin of Risso's dolphins ( Grampus griseus ) and a false killer whale ( Pseudorca crassidens ) in captivity; their heart rate was successfully monitored. We observed large heart rate oscillations corresponding to respiration in the motionless whales during surfacing (a false killer whale, mean 47 bpm, range 20–75 bpm; Risso's dolphins, mean ± s.d. 61 ± 15 bpm, range 28–120 bpm, n = 4 individuals), which was consistent with the sinus arrhythmia pattern (eupneic tachycardia and apneic bradycardia) observed in other cetaceans. Immediately after respiration, the heart rate rapidly increased to approximately twice that observed prior to the breath. Heart rate then gradually decreased at around 20–50 s and remained relatively constant until the next breath. Furthermore, we successfully monitored the heart rate of a free-swimming Risso's dolphin. The all-in-one suction cup device is feasible for field use without restraining animals and is helpful in further understanding the diving physiology of free-ranging cetaceans. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.

This thesis examined the benefits of physiological and performance testing of elite swimmers. The study considered the following research questions: the degree to which physiological and performance measures in training contribute to swimming performance; sources and magnitude of variability in testing, training and competition performance; the magnitudes of changes in test measures during routine training; and the reliability, validity and utility of miniaturised and automated smart sensor technology to monitor the stroke and performance times of swimmers in training. The experimental approach involved the retrospective analysis of five years of physiological and performance testing of elite level swimmers, the development of a new accelerometry-based smart sensor device to monitor swimmers in the pool, a cross-sectional study comparing the physiological and performance responses of swimmers of different levels, and the effects of an intensive 14-day training program on submaximal physiological and performance measures. Collectively, the outcomes of these studies provide a strong justification for the physiological and performance testing of elite swimmers, a quantitative framework for interpreting the magnitude of changes and differences in test scores and sources of variation, and highlight the potential utility of new smart sensor technology to automate the monitoring of a swimmer�s training performance. The first study (Chapter 2) characterises the changes and variability in test performance, physiological and anthropometric measures, and stroke mechanics of swimmers within and between seasons over their elite competitive career. Forty elite swimmers (24 male, 16 female) performed a 7 x 200-m incremental swimming step test several times each 6-month season (10 � 5 tests, spanning 0.5 to 6.0 y). Mixed linear modeling provided estimates of change in the mean and individual responses for measures based on submaximal performance (fixed 4-mM lactate), maximal performance (the seventh step), and lean mass (from skinfolds and body mass). Submaximal and maximal swim speed increased within each season from the pre to taper phase by ~2.2% for females and ~1.5% for males (95% confidence limits �1.0%), with variable contributions from stroke rate and stroke length. Most of the gains in speed were lost in the off-season, leaving a net average annual improvement of ~1.0% for females and ~0.6% for males (�1.0%). For submaximal and maximal speed, individual variation between phases was �2.2% and the typical measurement error was �0.8%. In conclusion, step test and anthropometric measures can be used to confidently monitor progressions in swimmers in an elite training program within and between seasons. The second study (Chapter 3) quantified the relationship between changes in test measures and changes in competition performance for individual elite swimmers. The primary question addressed was whether test measures could predict a swimmers performance at the major end-of-season competition. The same sample group as in Study 1 was examined. A 7 x 200-m incremental swimming step-test and anthropometry were conducted in up to four training phases each season. Correlations of changes in step-test and anthropometric measures between training phases between and within seasons, with changes in competition performance between seasons, were derived by repeated-measures mixed modeling and linear regression. Changes in competition performance were best tracked by changes in test measures between taper phases. The best single predictor of competition performance was skinfolds for females (r = -0.53). The best predictor from the step-test was stroke rate at 4-mM lactate (females, r = 0.46; males, r = 0.41); inclusion of the second-best step-test predictor in a multiple linear regression improved the correlations marginally (females, r =0.52 with speed in the seventh step included; males, r = 0.58 with peak lactate concentration included). Changes in test measures involving phases other than the taper provided weak and inconclusive correlations with changes in performance, possibly because the coaches and swimmers took corrective action when tests produced poor results. In conclusion, a combination of fitness and techniques factors are important for competitive performance. The step test is apparently a useful adjunct in a swimmer�s training preparation for tracking large changes in performance. These initial studies identified stroke mechanics as a major determinant of a swimmer�s performance. Chapter 4 details the development of a small tri-axial accelerometry-based smart sensor device (the Traqua) that enables continual monitoring of various performance/stroke characteristics in swimming. The initial focus was to develop a device that automated the detection of a swimmer�s movements, specifically lap times, stroke rate and stroke count. The Traqua consists of a tri-axial accelerometer packaged with a microprocessor, which attaches to the swimmer at the pelvis to monitor their whole body movements while swimming. This study established the failure/error rate in the first generation algorithms developed to detect the swimming-specific movements of stroke identification, laps (start, turn and finish), and strokes (stroke count and stroke rate) in a cohort of 21 elite and sub-elite swimmers. Movements were analysed across a range of swimming speeds for both freestyle and breaststroke. These initial algorithms were reasonably successful in correctly identifying the markers representing specific segments of a swimming lap in a range of swimmers across a spectrum of swimming speeds. The first iteration of the freestyle algorithm produced error-rates of 13% in detection of lap times, 5% for stroke rate, and 11% for stroke count. Subsequent improvements of the software reduced the error rate in lap and stroke detection. This improved software was used in the following two studies. The next study (Chapter 5) evaluated the reliability and validity of the Traqua against contemporary methods used for timing, stroke rate and stroke count determination. The subjects were 14 elite and 10 sub-elite club-level swimmers. Each swimmer was required to swim seven evenly paced 200-m efforts on a 5-min cycle, graded from easy to maximal. Swimmers completed the test using their main competitive stroke (21 freestyle, 3 breaststroke). Timing was compared for each 50-m lap and total 200-m time by electronic touch pads, video coding, a hand-held manual stopwatch, and the Traqua. Stroke count was compared for video coding, self-reported counting, and the Traqua, while the stroke rate was compared via video coding, hand-held stopwatch, and the Traqua. Retest trials were conducted under the same conditions 7 d following the first test. All data from the Traqua presented in this and the subsequent studies were visually inspected for errors in the automated algorithms, where the algorithms had either failed to correctly identify the start, turn, finish or individual strokes and corrected prior to analysis. The standard error of the estimate for each of the timing methods for total 200 m was compared with the criterion electronic timing. These standard errors were as follows: Traqua (0.64 s; 90% confidence limits 0.60 � 0.69 s), Video (0.52 s; 0.49 � 0.55 s); Manual (0.63 s; 0.59 � 0.67 s). Broken down by 50-m laps, the standard error of the estimate for the Traqua compared with the electronic timing for freestyle only was: 1st 50-m 0.35 s; 2nd and 3rd 50-m 0.13 s; 4th 50-m 0.65 s. When compared with the criterion video-coding determination, the error for the stroke count was substantially lower for the Traqua (0.6 strokes.50 m-1; 0.5 � 0.6 strokes.50 m-1) compared to the self-reported measure (2.3 strokes.50 m-1; 2.5 � 2.9 strokes.50 m-1). However, the error for stroke rate was similar between the Traqua (1.5 strokes.min-1; 1.4 � 1.6 strokes.min-1) and the manual stopwatch (1.8 strokes.min-1; 1.7 � 1.9 strokes.min-1). The typical error of measurement of the Traqua was 1.99 s for 200-m time, 1.1 strokes.min-1 for stroke rate, and 1.1 strokes.50 m-1 for stroke count. In conclusion, the Traqua is comparable in accuracy to current methods for determining time and stroke rate, and better than current methods for stroke count. A substantial source of error in the Traqua timing was additional noise in the detection of the start and finish. The Traqua is probably useful for monitoring of routine training but electronic timing and video are preferred for racing and time trials. Having established the reliability and validity of the Traqua, Chapter 6 addressed the ability to discriminate the pattern of pacing between different levels of swimmers in the 7 x 200-m incremental step test. This study also sought to quantify the differences in pacing between senior and junior swimmers. Eleven senior elite swimmers (5 female, 6 male) and 10 competitive junior swimmers (3 female, 7 male) participated in this study. Each swimmer was required to swim seven evenly paced 200-m freestyle efforts on a 5-min cycle, graded from easy to maximal. The Traqua was used to measure time, stroke rate and stroke count. The senior swimmers were better able to descend in each of the 200-m efforts. Overall the senior swimmers were ~2-3 s per 50 m faster than the junior swimmers. Both groups were fastest in the first 50-m lap with the push start. The senior swimmers then descended the 50- m time for each of the subsequent laps, getting ~0.5 s faster per lap, with the final lap the fastest. In contrast, the junior swimmers swam a similar time for each of the subsequent laps. The junior swimmers were marginally more variable in their times (coefficient of variation: ~2%) compared with the senior swimmers (~1.8%). In comparison to junior swimmers, the senior swimmers in this study were faster, adopted a more uniform negative split strategy to pacing within a 200-m effort, and were more consistent in reproducing submaximal and maximal swimming speeds. The final study (Chapter 7) analysed the effect of 14-d of intensive training on the reproducibility of submaximal swimming performance in elite swimmers. Submaximal physiological and performance testing is widely used in swimming and other individual sports but the variability in test measures, and the effects of fatigue, during intensive training have surprisingly not been quantified systematically. Seven elite swimmers (3 male and 4 female) participated in an intensive 14-d training camp one month prior to the National championships. The aim of the study was to characterise the intra-session, daily and training block variability of submaximal swimming time, physiological and stroke characteristics in elite swimmers. The swimmers performed a specified submaximal 200-m effort in most sessions, after the warm-up and at the end of the session for both morning and afternoon sessions. During the efforts, swimming time and stroke mechanics were measured and physiological measures were recorded immediately on completion. The Traqua was worn by all swimmers in every training session. Mixed linear modeling was used to provide estimates of changes in the mean and individual responses (within-athlete variation as a coefficient of variation) for all measures. The swimmers were moderately slower (1.4%; �1.4%) over the 14-d training camp. The mean submaximal 200-m effort was very likely to be faster (0.7%; confidence limits �0.7%) in the afternoon compared with the morning session. The females were more variable in their submaximal performance times (CV=2.6%) than the male swimmers (1.7%). Blood lactate concentration was almost certainly lower (-23%; �10%) following higher volume in the previous session; however a higher intensity workout the previous session almost certainly leads to higher lactate (21%; �15%) in the current session. Considered together, these results indicate that the 200-m submaximal test is useful in monitoring submaximal physiological and performance measures and the negative effects of cumulative fatigue. In conclusion, changes in the physiological and performance measures derived from the poolbased progressive incremental step test are moderately correlated with changes in end-ofviii. season competition performance. The magnitudes of changes and differences in test measures between phases within a season, from season to season, and between males and females, established in this study can be applied to similar elite level swimmers preparing for major competition. The quantification of typical error of the same measures demonstrates that coaches and scientists can distinguish real and worthwhile improvements using the 7 x 200-m step test. Continual pool-based monitoring with the automated smart sensor Traqua device may provide more accurate and detailed information about a swimmer�s training adaptation than current fitness tests and monitoring methods. Finally, submaximal testing in trained swimmers is useful in monitoring progress in physiological and performance measures, and the impact of cumulative fatigue during an intensive period of training. Collectively, the outcomes of these studies indicate that routine physiological and performance testing can provide measurable benefits for elite swimmers and their coaches.

Underwater robots with buoyancy control capability are highly desirable in deep ocean exploration for underwater environment monitoring and intelligent collection. In this paper, a prototype of buoyancy control device powered by ionic polymer metal composite (IPMC) is developed. An IPMC is used for enhancing the water electrolysis of tap water and separating the gases produced. The produced hydrogen and oxygen gases are stored in two separate chambers. Collection of these gases increase the volume of water displaced by the device, hence, increases its buoyancy. Two solenoid valves are used to control the release of gases to decrease the device’s buoyancy. Using a dynamic model developed in our previous work, the parameters of the model are identified through an open-loop test. A PID controller is then designed for close-loop depth control. The PID controller uses the error in depth to estimate the desired gas generation/releasing rate. It then calculates the duty cycle of the pulse-width modulation (PWM) signal used for driving the solenoid valves. The closed-loop depth control is verified both through simulation and real-time experiment, showing satisfactory results.

Early detection of leaks in hazardous materials pipelines is essential to reduce product loss and damage to the environment. Small undetected leaks can result in very high clean-up costs and have the potential to grow to more serious failures. There are a variety of methods that can detect leaks in pipelines, ranging from manual inspection to advanced satellite based imaging. Typically, most operators opt for a combination of CPM where available, and direct observation methodologies including aerial patrols, ground patrols and public awareness programs that are designed to encourage and facilitate the reporting of suspected leaks. Permanent monitoring sensors based on acoustic or other technologies are also available. These methods can be costly, and none can reliably detect small leaks regardless of their location in the line. SmartBall is a radical new approach that combines the sensitivity of acoustic leak detection with the 100% coverage capability of in-line inspection. The free-swimming device is spherical and smaller than the pipe bore allowing it to roll silently through the line and achieve the highest responsiveness to small leaks. It can be launched and retrieved using conventional pig traps, but its size and shape allow it to negotiate obstacles that could otherwise render a pipeline unpiggable. The SmartBall technology was originally developed and successfully implemented for the water industry, and now refined for oil and gas pipelines over 4-inches in diameter. SmartBall has been proven capable of detecting leaks in liquid lines of less than 0.1 gallons per minute where conventional CPM methods can detect leaks no smaller than 1% of throughput. Development work is continuing to reduce the detection threshold still further. Whereas traditional acoustic monitoring techniques have focused on longitudinal deployment and spacing of acoustic sensors, the SmartBall uses only a single acoustic sensor that is deployed inside the pipeline. Propelled by the flow of product in the pipeline, the device will record all noise events as it traverses the length of the pipeline. This allows the acoustic sensor to pass in very close proximity to any leak whereby the sensor can detect very small leaks, whose noise signature can be clearly distinguished from any background noise.