A low-power opportunistic communication protocol for wearable applications

© 2015 IEEE.Recent trends in wearable applications demand flexible architectures being able to monitor people while they move in free-living environments. Current solutions use either store-download-offline processing or simple communication schemes with real-time streaming of sensor data. This limits the applicability of wearable applications to controlled environments (e.g, clinics, homes, or laboratories), because they need to maintain connectivity with the base station throughout the monitoring process. In this paper, we present the design and implementation of an opportunistic communication framework that simplifies the general use of wearable devices in free-living environments. It relies on a low-power data collection protocol that allows the end user to opportunistically, yet seamlessly manage the transmission of sensor data. We validate the feasibility of the framework by demonstrating its use for swimming, where the normal wireless communication is constantly interfered by the environment

Kinematics analysis of freestyle swimming athletes at the 2019 Indonesia Open Aquatic Championship (IOAC)

This study aims to determine the kinematics of the men's 50 and 100 meter freestyle swimming athletes. Research method uses quantitative descriptive with 50 meter swimmers and 100 meter freestyle men at the 2019 IOAC championship. Instrument used was a Sony Rx-10Mark IV camera placed in the highest stands at a distance of 25 m in a 50-meter pool. Video results analyzed using the Kinovea 0.8.27 software by calculating the SF,SV,SR, and SL. The results showed that the average number of a 50meter had an SF of 13.06,SV of 1.89 m.s-1,SR of 59.08 cycles.min-1, and SL of 1.92 m.cycle-1. The 100 meter number, the average SF value is 11.8 at a distance of 50-meter and 12.08 at 100 meter. In comparison, the SV average is 1.73 m.s-1 at a distance of 50 and 1.72 m.s-1 at a distance of 100-meter. SR the average is 46.35 cycles.min-1 distance of 50 meter and 50, 2 100 m distance. SL the average is 2.25 m.cycle-1 distance of 50-meter and 2.08 distance of 100-meter. Conclusion, are differences in kinematics of swimming between the men's 50 meter and 100 meter freestyle in SV and SR,while those in SF and SL tend to be the same

The Use of a Cap-mounted Tri-axial Accelerometer for Measurement of Distance, Lap Times and Stroke Rates in Swim Training

This paper will report some of the findings from a trial which recorded accelerometer data from six elite level swimmers (three female and three male, varying primary event stroke and distance) over the course of a regular 15 week training block. Measurements from a head-mounted accelerometer are used to determine when the athlete is swimming, marking of turning points (and therefore distance and lap-time measurements), and is processed by frequency analysis to determine stroke-rate. Comparison with video where available, and with training plans and literature where not, have proven this method to be accurate and reliable for determining these performance metrics. The primary objective of this project was to develop a low-cost, simple and highly usable system for use in swim coaching, feedback from elite coaches has indicated that development of this could be an extremely useful addition to their training regime

Smartpaddle® as a new tool for monitoring swimmers’ kinematic and kinetic variables in real time

Smart technology, such as wearables, applied to sports analysis is essential for performance enhancement. New technological equipment can promote the interaction between researchers, coaches, and athletes, facilitating information exchange in real time. Objective: The aim of this study was to present new wearable equipment (SmartPaddle®) to measure kinematic and kinetic variables in swimming and understand the agreement of the propulsive force variable with a pressure sensor system. Methods: Four male university swimmers (18.75±0.50 years old, 71.55±6.80 kg of body mass, and 175.00±5.94 cm of height) were analyzed. The SmartPaddle® and a pressure sensor system were used to collect the kinetic data (propulsive force). The comparison between the propulsive force methods was based on t-test paired samples, simple linear regression, and Bland-Altman plots. Results: SmartPaddle® is a system that consists of (i) a wearable device, (ii) the Trainesense Session Manager mobile application for recording, and; (iii) the Analysis Center for analysis and data storage. It records a set of kinematic and kinetic parameters useful for coaches daily. The comparison between the different methods revealed non-significant differences and a very-high relationship. Conclusion: SmartPaddle® is a feasible wearable device that swimmersswimmers can use can use to provide immediate data about their kinematic and kinetic profile. Coaches can easily monitor these parameters and give immediate suggestions to their swimmers on a daily basis.This work is supported by national funds (FCT - Portuguese Foundation for Science and Technology) under the project UIDB/DTP/04045/2020info:eu-repo/semantics/publishedVersio

Prediction of Kick Count in Triathletes during Freestyle Swimming Session Using Inertial Sensor Technology

Monitoring sports training performances with automatic, low cost, low power, and ergonomic solutions is a topic of increasing importance in the research of the last years. A parameter of particular interest, which has not been extensively dealt with in a state-of-the-art way, is the count of kicks during swimming training sessions. Coaches and athletes set the training sessions to optimize the kick count and swim stroke rate to acquire velocity and acceleration during swimming. In regard to race distances, counting kicks can influence the athlete’s performance. However, it is difficult to record the kick count without facing some issues about subjective interpretation. In this paper, a new method for kick count is proposed, based on only one triaxial accelerometer worn on the athlete’s ankle. The algorithm was validated on data recorded during freestyle training sessions. An accuracy of 97.5% with a sensitivity of 99.3% was achieved. The proposed method shows good linearity and a slope of 1.01. These results overcome other state-of-the-art methods, proving that this method is a good candidate for a reliable, embedded kick count

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

Measuring Kinematic Variables in Front Crawl Swimming Using Accelerometers: A Validation Study

Objective data on swimming performance is needed to meet the demands of the swimming coach and athlete. The purpose of this study is to use a multiple inertial measurement units to calculate Lap Time, Velocity, Stroke Count, Stroke Duration, Stroke Rate and Phases of the Stroke (Entry, Pull, Push, Recovery) in front crawl swimming. Using multiple units on the body, an algorithm was developed to calculate the phases of the stroke based on the relative position of the body roll. Twelve swimmers, equipped with these devices on the body, performed fatiguing trials. The calculated factors were compared to the same data derived to video data showing strong positive results for all factors. Four swimmers required individual adaptation to the stroke phase calculation method. The developed algorithm was developed using a search window relative to the body roll (peak/trough). This customization requirement demonstrates that single based devices will not be able to determine these phases of the stroke with sufficient accuracy

FRONTCRAWL PROPULSIVE PHASE DETECTION USING INERTIAL SENSORS

Front crawl is an alternating swimming stroke technique in which different phases of arm movement induce changes in acceleration of limbs and body. This study proposes a new approach to use inertial body worn sensors to estimate main temporal phases of front crawl. Distinctive features in kinematic signals are used to detect the temporal phases. These temporal phases are key information sources of qualitative and quantitative evaluation of swimming coordination, which have been assessed previously by video analysis. The present method has been evaluated upon a wide range of coordination and showed a difference of 4.9% with video based system. The results are in line with video analysis inter-operator variability yet offering an easy-to-use system for trainers

Exploring the role of wearable technology in sport kinematics and kinetics: a systematic review

The aim of this review was to understand the use of wearable technology in sport in order to enhance performance and prevent injury. Understanding sports biomechanics is important for injury prevention and performance enhancement and is traditionally assessed using optical motion capture. However, such approaches are limited by capture volume restricting assessment to a laboratory environment, a factor that can be overcome by wearable technology. A systematic search was carried out across seven databases where wearable technology was employed to assess kinetic and kinematic variables in sport. Articles were excluded if they focused on sensor design and did not measure kinetic or kinematic variables or apply the technology on targeted participants. A total of 33 articles were included for full-text analysis where participants took part in a sport and performed dynamic movements relating to performance monitored by wearable technologies. Inertial measurement units, flex sensors and magnetic field and angular rate sensors were among the devices used in over 15 sports to quantify motion. Wearable technology usage is still in an exploratory phase, but there is potential for this technology to positively influence coaching practice and athletes’ technique

Muscle Fatigue and Swimming Efficiency in Behind and Lateral Drafting

: Drafting in swimming is a tactic in which an athlete (drafter) swims in the wave of another athlete (leader). Our aim was to compare the effects of this tactic on the drafter, as far as muscle fatigue, muscle activity, and swimming efficiency are concerned. Fifteen drafters performed three 200\u2009m front crawl trials at a controlled submaximal pace in three configurations: Behind Drafting (BD), Lateral Drafting (LD), and Free Swimming (FS). Muscle fatigue, muscle activity, and swimming efficiency were obtained by surface electromyography (EMG) and video analysis from flexor carpi radialis, triceps brachii, latissimus dorsi, and rectus femoris muscles. The outcome measures were: time slope of Mean Frequency (MNF), for muscle fatigue; time slope of Root Mean Square (RMS), for muscle activity; and Stroke Index (SI) for swimming efficiency. Negative variations of MNF were 5.1\u2009\ub1\u20091.7%, 6.6\u2009\ub1\u20094.1%, and 11.1\u2009\ub1\u20092.7% in BD, LD, and FS, respectively. Statistical significance was found for all cases except for the rectus femoris. Positive variations of RMS were 3.4\u2009\ub1\u20091.2%, 4.7\u2009\ub1\u20092.7%, and 7.8\u2009\ub1\u20094.6% in BD, LD, and FS, respectively. Statistical significance was found only for the slopes of latissimus dorsi in FS and LD. The largest mean in SI was measured in the BD (2.01\u2009m2/s), while the smallest was measured in the FS (1.86\u2009m2/s). BD was found to be the best swimming configuration, in terms of lower muscle fatigue and higher swimming efficiency. Also, LD resulted to be advantageous with respect to FS