Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics

Sensor node acceleration signatures and electromyography in synchronisation and sequencing analysis in sports: a rowing perspective

Following a review of the key determinants of successful rowing, a wireless body sensor network was developed to monitor boat and body segment acceleration and surface electromyography in major muscles recruited during the rowing stroke cycle. Its design was optimised to yield maximum information about the rowing stroke cycle from fewest sensors and minimise the power consumption of the nodes. The system was validated against the Qualisys motion capture and high-speed camera system with most Pearson correlation coefficients in excess of r = 0.8. On-land ergometer experimentation allowed muscle recruitment over the stroke cycle to be studied, with data from multiple experiments combined using correlation of the acceleration signatures of back and thigh nodes (r = 0.95). It was demonstrated that it was possible to identify one of the common rowing errors of ‘shooting-the-slide’ from the data collected, and that a marked decrease in correlation of good-to-bad technique over the drive phase of the stroke (0.95 reducing to 0.34 in the experiment undertaken) could be used to indicate the presence of this error. Extension of the wireless body sensor network to encompass boat and two oarsmen was demonstrated, allowing correlation of their rowing signatures to be studied, indicating their cohesion as a crew

Pedagogical Feedback for Computer-based Sport Training

Feedback in Computer-based Sport Training (CBST) may be synthetically designed to allow athletes to practise in a more effective way and enhance their skill acquisition. Little research has integrated pedagogic theory and instructional design with the design of feedback in CBST. To bridge this gap, the paper presents the design of pedagogically-informed feedback for the implementation of a CBST system. The heart of the design is to generate feedback based on the athletes’ achievement of their intended training outcome. The pedagogical feedback system measures athletes’ performance and compares it with the given training outcomes. The system then identifies the performance’s gap and generates feedback to reinforce better performance. A Counterbalanced experiment asked student rowers (N = 8) to explore the differences between the pedagogical feedback system and their current feedback system (Sean-Analysis). Pedagogical feedback was at least as good as Sean-Analysis with respect to the level of satisfaction of the athlete. Overall, it can be concluded that the pedagogical feedback appears to be a good model for generating feedback in CBST

REMOTE, a Wireless Sensor Network Based System to Monitor Rowing Performance

In this paper, we take a hard look at the performance of REMOTE, a sensor network based application that provides a detailed picture of a boat movement, individual rower performance, or his/her performance compared with other crew members. The application analyzes data gathered with a WSN strategically deployed over a boat to obtain information on the boat and oar movements. Functionalities of REMOTE are compared to those of RowX [1] outdoor instrument, a commercial wired sensor instrument designed for similar purposes. This study demonstrates that with smart geometrical configuration of the sensors, rotation and translation of the oars and boat can be obtained. Three different tests are performed: laboratory calibration allows us to become familiar with the accelerometer readings and validate the theory, ergometer tests which help us to set the acquisition parameters, and on boat tests shows the application potential of this technologies in sports

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

In this exploratory work the motion of rowers is analyzed while rowing on a rowing machine. This is performed using inertial sensors that measure the orientation at several positions on the body. Using these measurements, this work provides a preliminary analysis of the differences between experienced and novice rowers, or between a good and a bad technique. The analysis shows that the measured postural angles show no clear trend that would set apart experienced and novice rowers or a bad and a good technique. However, there are clear differences in absolute postural angle’s consistency and timing consistency of strokes between novice and experienced rowers. We also applied a machine learning technique to the data to find the similarities between different rowers and an experienced reference rower. The results can be used to compare the quality of the rowing technique with respect to a reference. In this paper, we present our initial results as well as the challenges that need to be further explored

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Stretch sensors for measuring knee kinematics in sports

The popularity of wearable technology in sport has increased, due to its ability to provide unobtrusive monitoring of athletes. This technology has been used to objectively measure kinetic and kinematic variables, with the aim of preventing injury, maximising athletic performance and classifying the skill level of athletes, all of which can influence training and coaching practices. Wearable technologies overcome the limitations of motion capture systems which are limited in their capture volume, enabling the collection of data in-field, during training and competition. Inertial sensors are a common form of technology used in these environments however, their high-cost and complex calibration due to multiple sensor integration can make them prohibitive for widespread use. This thesis focuses on the development of a strain sensor that can be used to measure knee range of motion in sports, specifically rowing and cycling, as a potential low-cost, lightweight alternative to inertial sensors which can also be integrated into clothing, making them more discreet. A systematic review highlighted the lack of alternate technologies to inertial sensors such as strain sensors, as well as the limited use of wearable technologies in both rowing and cycling. Strain sensors were fabricated from a carbon nanotube-natural rubber composite using solvent exchange techniques and employed a piezoresistive sensing mechanism. These were then characterised using mechanical testing, to determine their electrical properties under cyclical strain. The strain sensors displayed hysteretic behaviour, but were durable, withstanding over 4500 strain cycles. Statistical analysis indicated that over 60% of the tests conducted had good intra-test variability with regards to the resistance response range in each strain cycle and sensor response deviating by less than 10% at strain rates below 100 mm/min and less than 20% at a strain rate of 350 mm/min. These sensors were integrated into a wearable sensor system and tested on rowing and cycling cohorts consisting of ten athletes each, to assess the translational use of the strain sensor. This preliminary testing indicated that strain sensors were able to track the motion of the knee during the rowing stroke and cycling pedalling motion, when compared to the output of a motion capture system. Perspectives of participants on the wearable system were collected, which indicated their desire for a system that they could use in their sport, and they considered the translation of this system for real-life use with further development to improve comfort of the system and consistency of the sensor response. The strain sensors developed in this project, when integrated into a wearable sensor system, have the potential to provide an unobtrusive method of measuring knee kinematics, helping athletes, coaches and other support staff make technical changes that can reduce injury risk and improve performance.Open Acces