Colin Mackintosh

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.

Using micro-sensor data to quantify macro kinematics of classical cross-country skiing during on-snow training

Abstract Micro-sensors were used to quantify macro kinematics of classical cross-country skiing techniques and measure cycle rates and cycle lengths during on-snow training. Data were collected from seven national level participants skiing at two submaximal intensities while wearing a micro-sensor unit (MinimaxX™). Algorithms were developed identifying double poling (DP), diagonal striding (DS), kick-double poling (KDP), tucking (Tuck), and turning (Turn). Technique duration (T-time), cycle rates, and cycle counts were compared to video-derived data to assess system accuracy. There was good reliability between micro-sensor and video calculated cycle rates for DP, DS, and KDP, with small mean differences (Mdiff% = −0.2 ± 3.2, −1.5 ± 2.2 and −1.4 ± 6.2) and trivial to small effect sizes (ES = 0.20, 0.30 and 0.13). Very strong correlations were observed for DP, DS, and KDP for T-time (r = 0.87–0.99) and cycle count (r = 0.87–0.99), while mean values were under-reported by the micro-sensor. Incorrect Turn detection was a major factor in technique cycle misclassification. Data presented highlight the potential of automated ski technique classification in cross-country skiing research. With further refinement, this approach will allow many applied questions associated with pacing, fatigue, technique selection and power output during training and competition to be answered.

Full course macro-kinematic analysis of a 10 km classical cross-country skiing competition

In this study micro-sensors were employed to analyse macro-kinematic parameters during a classical cross-country skiing competition (10 km, 2-lap). Data were collected from eight male participants during the Australian championship competition wearing a single micro-sensor unit (MinimaxX™, S4) positioned on their upper back. Algorithms and visual classification were used to identify skiing sub-techniques and calculate velocities, cycle lengths (CL) and cycle rates (CR) over the entire course. Double poling (DP) was the predominant cyclical sub-technique utilised (43 ± 5% of total distance), followed by diagonal stride (DS, 16 ± 4%) and kick double poling (KDP, 5 ± 4%), with the non-propulsive Tuck technique accounting for 24 ± 4% of the course. Large within-athlete variances in CL and CR occurred, particularly for DS (CV% = 25 ± 2% and CV% = 15 ± 2%, respectively). For all sub-techniques the mean CR on both laps and for the slower and faster skiers were similar, while there was a trend for the mean velocities in all sub-techniques by the faster athletes to be higher. Overall velocity and mean DP-CL were significantly higher on Lap 1, with no significant change in KDP-CL or DS-CL between laps. Distinct individual velocity thresholds for transitions between sub-techniques were observed. Clearly, valuable insights into cross-country skiing performance can be gained through continuous macro-kinematic monitoring during competition.

Accurate tracking using TOA in sensor networks

Localisation and tracking of nodes in wireless sensor networks is valuable in many applications, and is the primary purpose of the network in applications such as for monitoring emergency service personnel and athletes for performance monitoring. Performing high accuracy tracking using low-cost hardware in difficult radio propagation environments is extremely challenging, and the CSIRSO WASP system was designed to meet this challenge. In this paper results are presented of trials of the WASP system for monitoring cyclists that were conducted with the Australian Institute of Sport. The results show that the system has an absolute and relative error less than 0.33 m for 95% of the measurements.