Identification of Cross-Country Skiing Movement Patterns Using Micro-Sensors

This study investigated the potential of micro-sensors for use in the identification of the main movement patterns used in cross-country skiing. Data were collected from four elite international and four Australian athletes in Europe and in Australia using a MinimaxX™ unit containing accelerometer, gyroscope and GPS sensors. Athletes performed four skating techniques and three classical techniques on snow at moderate velocity. Data from a single micro-sensor unit positioned in the centre of the upper back was sufficient to visually identify cyclical movement patterns for each technique. The general patterns for each technique were identified clearly across all athletes while at the same time distinctive characteristics for individual athletes were observed. Differences in speed, snow condition and gradient of terrain were not controlled in this study and these factors could have an effect on the data patterns. Development of algorithms to process the micro-sensor data into kinematic measurements would provide coaches and scientists with a valuable performance analysis tool. Further research is needed to develop such algorithms and to determine whether the patterns are consistent across a range of different speeds, snow conditions and terrain, and for skiers of differing ability

From a module to an MSc that upskills workers and improves graduate employability: an example from Audio/Video Forensics

Developing an academic programme that upskills students and enhances employability can be a challenge, however getting that ‘spark’ of an idea can come from unexpected places. Programme design can be improved through collaboration with different subject areas and with employers.   Audio and Video forensics is a fast-moving area at the forefront of evidence in the criminal justice system. The need for improving knowledge, skills and abilities of audio & video forensic officers has been acknowledged by the Ministry of Justice, the UK Forensic Regulator and the National Police Chiefs Council, with plans for all audio & video forensic officers to be educated and trained to the highest level.   Having taught Audio Production for many years, the Audio Team introduced a popular and well received Audio Forensics module. Based on this, and through a working relationship with the Forensic Science team (School of PBS), the School of Engineering started discussions with Merseyside Police in relation to audio and video forensics. This collaboration led to creating the MSc in Audio and Video Forensics (validated in July 2021) delivered primarily online. Given this MSc in Audio and Video Forensics is the only such programme in the world, LJMU are ideally placed to be at the forefront of this educational upskilling.   Senior Officers within Merseyside Police generally and the Merseyside Police Forensic Unit in particular, have been closely involved in developing the MSc programme, with their support extending to sending a significant number of their staff to study on the programme. The talk will detail the collaboration between LJMU departments, students, and Police during the design process to produce a programme that meets the needs of Police Forensic Units, provides a route for Audio/Video Technology graduate to retrain into forensics, and provides graduates with the requisite transversal skills – an approach that other departments can follow. &nbsp

The mean velocities (± <i>s</i>) for each athlete for the various sub-techniques on Lap 1.

<p> 1–8 = fastest—slowest (entire race); Tuck = tucking; DP = double poling; KDP = kick double poling; DS = diagonal stride.</p

The mean cycle lengths and rates (± <i>s</i>) for each athlete and cyclical sub-technique on Lap 1 (■) and Lap 2 (●).

<p>1–8 = fastest—slowest (entire race); DP = double poling; KDP = kick double poling; DS = diagonal stride.</p

Sub-technique usage (mean ± <i>s</i>) by the 8 athletes.

<p>Sub-technique usage (mean ± <i>s</i>) by the 8 athletes.</p

The mean race velocities with which each athlete completed Lap 1 (■) and Lap 2 (●).

<p>1–8 = fastest—slowest (entire race).</p

The mean velocities, cycle lengths and cycle rates (± <i>s</i>) for the fastest four (▲) and slowest four (▼) athletes in each cyclical sub-technique.

<p>DP = double poling; KDP = kick double poling; DS = diagonal stride.</p

Characteristics of the participants (mean ± s, n = 8).

<p>Characteristics of the participants (mean ± s, n = 8).</p