Daniel Arthur James

An accelerometer based sensor platform for insitu elite athlete performance analysis

A sensor data acquisition platform has been developed for in-situ sporting applications encompassing stand alone high speed sampling and storage of multiple accelerometer data. Ambulatory monitoring of elite athletes in competition or training environments was then undertaken. The platform itself is divided into functional blocks and controlled by a small microcontroller using a custom OS. Hence it is easily customizable for a variety of sporting applications. The platform is packaged so as to be robust, hermetically sealed and biomechanically neutral to the athlete. Results and derivative data from rowing and swimming are presented as sample applications. In swimming applications, stroke characteristics for a variety of training strokes are analyzed and found to be better than other methods. The rowing application, when coupled with other monitoring techniques such as impeller velocity, enables recovery of intra and inter stroke phases as a means to assess performance over the entire course and has been used by competition rowers to improve performance at national and international competitions.

Investigating the translational and rotational motion of the swing using accelerometers for athlete skill assessment

In this paper, an accelerometer measurement system comprising three accelerometer nodes was used to identify the correlation between the skill level and the characteristics of the first serve swing in tennis. Three MEMS accelerometers were mounted on the knee, leg, and wrist of the tennis players. The kinematic model for the first serve was observed. Furthermore, this study revealed that side-forward motion of the hand along with the forward motion of the waist of an athlete can be used as indicator to assess the athletes skill level. It is envisaged that this application can provide feedback to tennis players.

Semi-automatic calibration technique using six inertial frames of reference

A triaxial accelerometer calibration technique that evades the problems of the conventional calibration method of aligning with gravity is proposed in this paper. It is based on the principle that the vector sum of acceleration from three sensing axes should be equal to the gravity vector. The method requires the accelerometer to be oriented and stationary in 6 different ways to solve for the 3 scale factors and 3 offsets. The Newton-Raphson method was employed to solve the non-linear equations in order to obtain the scale factors and offsets. The iterative process was fast, with an average of 5 iterations required to solve the system of equations. The accuracy of the derived scale factors and offsets were determined by using them to calculate the gravity vector magnitude using the triaxial accelerometer to measure gravity. The triaxial accelerometer was used to measure gravity 264 times to determine the accuracy of the 44 acceptable sets of scale factors and offsets derived from the calibrations (gravity was assumed to equal 9.8000 ms-2 during the calibration). It was found that the best calibration calculated the gravity vector magnitude to 9.8156 ± 0.4294 ms-2. This equates to a maximum of 4.5% error in terms of a constant acceleration measurement. Because of the principle behind this method, it has the disadvantage that noise/error in only one axis will cause an inaccurate determination of all the scale factors and offsets.

The Application of Inertial Sensors in Elite Sports Monitoring

Arguably the performance of elite athletes today has almost as much to do with science, as it does with training. Traditionally the measurement of elite athlete performance is commonly done in a laboratory environment where rigorous testing of biomechanics and physiology can take place. Laboratory testing however places limits on how the athlete performs, as the environment is sufficiently different to the training environment. In addition, performance characteristics are further augmented during competition when compared to regular training. By better understanding athlete performance during the competition and training environment coaches can more effectively work with athletes to improve their performance. The testing and monitoring of elite athletes in their natural training environment is a relatively new area of development that has been facilitated by advancements in microelectronics and other micro technologies. Whilst it is a logical progression to take laboratory equipment and miniaturize it for the training and competition environment, it introduces a number of considerations that need to be addressed. In this paper the use and application of inertial devices for elite and sub-elite sporting activities are discussed. The capacity of accelerometers and gyroscopes to measure human motion thousands of times per second in multiple axis and at multiple points on the body is well established. However interpretation of this data into well-known metrics suitable for use by sport scientists, coaches and athletes is something of a challenge. Traditional brute force techniques such as achieving dead reckoning position and velocity by multiple integration are generally regarded as an almost impossible task. However novel derivative measures of performance such as energy expenditure, pattern recognition of specific activities and characterisation of activities into specific phases of motion have achieved greater success interpreting sensor data.

An integrated swimming monitoring system for the biomechanical analysis of swimming strokes

This paper describes the development of a complete wearable swimming system for performance analysis, together with a sample application. The system comprises wearable nodes, data processing tools in MATLAB™ and integration with video. The swimming nodes are small in size and designed to be worn on body segments of interest, typically lower leg, lower arm, and the sacral or cervical regions. Each node contains inertial sensors, screen, data storage and RF communications for synchronisation and data download. The device is controlled using a microcontroller with a scheduler-based operating system to conserve power and is custom-packaged with a user interface and USB port that is fully waterproof. The cost of manufacture is a few hundred dollars in small-run quantities. The developed analysis software builds upon previously developed tools, can communicate with the nodes individually and can synchronise the recording of multiple units through a custom-developed protocol. Video is integrated into the developed tools as a method of presenting the sensor data alongside a more traditional analysis tool. A case study of the system analyses swim stroke phase with video and demonstrates the utility of the system as a tool for temporal stroke phase identification in the high-performance environment.

Accelerometers: An underutilized resource in sports monitoring

Play based sports monitoring techniques provide coaches and players with the tools to better measure the effects of training or live performance. This paper explores the advantages of using accelerometers units, in an effort to better analyse over ground running in professional athletes. A large portion of studies in player monitoring in the Australian Football League (AFL) utilize GPS to obtain time and distance measurements.

Identifying and reducing noise in psychophysiological recordings.

Psychophysiology continues to be a widely used methodology in the study of human behaviour, emotion and cognition. The new researcher is faced with a number of problems in the recording process since the desired physiological signal must be isolated from a variety of noise sources. Precautions and strategies that can be implemented in setting up the recording equipment and isolating the subject from interference are described. There are also a number of software techniques that can be applied to improve signal quality after the data have been acquired. An overview is provided of hardware and software methods used to maximise the signal quality.

Classification of Aerial Acrobatics in Elite Half-Pipe Snowboarding Using Body Mounted Inertial Sensors

We have previously presented data indicating that the two most important objective performance variables in elite half-pipe snowboarding competition are air-time and degree of rotation. Furthermore, we have documented that air-time can be accurately quantified by signal processing of tri-axial accelerometer data obtained from body mounted inertial sensors. This paper adds to our initial findings by describing how body mounted inertial sensors (specifically tri-axial rate gyroscopes) and basic signal processing can be used to automatically classify aerial acrobatic manoeuvres into four rotational groups (180, 360, 540 or 720 degree rotations). Classification of aerial acrobatics is achieved using integration by summation. Angular velocity (ω i, j, k ) quantified by tri-axial rate gyroscopes was integrated over time (t = 0.01s) to provide discrete angular displacements (θ i, j, k ). Absolute angular displacements for each orthogonal axes (i, j, k) were then accumulated over the duration of an aerial acrobatic manoeuvre to provide the total angular displacement achieved in each axis over that time period. The total angular displacements associated with each orthogonal axes were then summed to calculate a composite rotational parameter called Air Angle (AA). We observed a statistically significant difference between AA across four half-pipe snowboarding acrobatic groups which involved increasing levels of rotational complexity (P < 0.001, n = 216). The signal processing technique documented in this paper provides sensitive automatic classification of aerial acrobatics into terminology used by the snowboarding community and subsequently has the potential to allow coaches and judges to focus on the more subjective and stylistic aspects of half-pipe snowboarding during either training or elite-level competition.

An accelerometer-based system for elite athlete swimming performance analysis

The measurement of sport specific performance characteristics is an important part of an athletes training and preparation for competition. Thus automated measurement, extraction and analysis of performance measures is desired and addressed in this paper. A tri-axial accelerometer based system was located on the lower back or swimmers to record acceleration profiles. The accelerometer system contained two ADXL202 bi-axial accelerometers positioned perpendicular to one another, and can store over 6 hours of data at 150Hz per channel using internal flash memory. The simultaneous collection of video and electronics touch pad timing was used to validate the algorithm results. Using the tri-axial accelerometer data, algorithms have been developed to derive lap times and stroke count. Comparison against electronic touch pad timing against accelerometer lap times has produced results with a typical error of better than ±0.5 seconds. Video comparison of the stroke count algorithm for freestyle also produced results with an average error of ±1 stroke. The developed algorithms have a higher level of reliability compared to hand timed and counted date that is commonly used during training.

Feature Extraction of Performance Variables in Elite Half-Pipe Snowboarding Using Body Mounted Inertial Sensors

Recent analysis of elite-level half-pipe snowboard competition has revealed a number of sport specific key performance variables (KPVs) that correlate well to score. Information on these variables is difficult to acquire and analyse, relying on collection and labour intensive manual post processing of video data. This paper presents the use of inertial sensors as a user-friendly alternative and subsequently implements signal processing routines to ultimately provide automated, sport specific feedback to coaches and athletes. The author has recently shown that the key performance variables (KPVs) of total air-time (TAT) and average degree of rotation (ADR) achieved during elite half-pipe snowboarding competition show strong correlation with an athletes subjectively judged score. Utilising Micro-Electrochemical System (MEMS) sensors (tri-axial accelerometers) this paper demonstrates that air-time (AT) achieved during half-pipe snowboarding can be detected and calculated accurately using basic signal processing techniques. Characterisation of the variations in aerial acrobatic manoeuvres and the associated calculation of exact degree of rotation (DR) achieved is a likely extension of this research. The technique developed used a two-pass method to detect locations of half-pipe snowboard runs using power density in the frequency domain and subsequently utilises a threshold based search algorithm in the time domain to calculate air-times associated with individual aerial acrobatic manoeuvres. This technique correctly identified the air-times of 100 percent of aerial acrobatic manoeuvres within each half-pipe snowboarding run (n = 92 aerial acrobatic manoeuvres from 4 subjects) and displayed a very strong correlation with a video based reference standard for air-time calculation (r = 0.78 ± 0.08; p value < 0.0001; SEE = 0.08 ×/÷ 1.16; mean bias = -0.03 ± 0.02s) (value ± or ×/÷ 95% CL).