Abstract
Performance analysis in cross-country skiing is constrained by the variability ofenvironmental conditions and terrain, and complicated by frequent changing between subtechniques
during competition. Snow conditions and skiing speed change constantly from day
to day and often during the day, and competition courses vary in the length, gradient and
distribution of hills from venue to venue. The aim of this body of work was to develop a new
performance analysis method, using a single micro-sensor, to continuously detect skiing subtechniques
and quantify the associated kinematic properties that describe a skier’s
performance during training and competition. Of particular interest was the relative use of
each sub-technique, together with velocity, cycle rate and cycle length characteristics
collectively defined as cross-country skiing macro-kinematics. Over five studies this thesis
explores proof of concept through detection of different sub-techniques, develops an
algorithm for the quantification of macro-kinematic parameters during training, demonstrates
the use of a refined algorithm to investigate performance demands and macro-kinematic
variability over an entire competition, compares macro-kinematics between different types of
event, and finally examines the implication for coaches arising from analysis throughout
rounds of a sprint event.
The first study (Chapter 3) in this research showed how the cycles of sub-techniques of both
classical and freestyle technique could be identified using a single micro-sensor unit,
containing an accelerometer, gyroscope and GPS sensors, mounted on the upper back. Data
was collected from eight skiers (six male and two female), of which four were World Cup
medallists, skiing at moderate velocity. Distinct movement patterns for four freestyle and
three classical cyclical sub-techniques were clearly identified, while at the same time
individual characteristics could be observed. The second study (Chapter 4) quantified macro-kinematics collected continuously from seven
skiers (four female and three male) during an on-snow training session in the classical
technique. Algorithms were developed to identify double poling (DP), diagonal striding (DS),
kick-double poling (KDP), tucking (Tuck), and turning (Turn) sub-techniques, and technique
duration, cycle rates (CR), and cycle counts were compared to video-derived data to assess
detection accuracy. There was good reliability between micro-sensor and video calculated
cycle rates for DP, DS, and KDP, while mean time spent performing each sub-technique was
under-reported. Incorrect Turn detection was a major factor in technique cycle
misclassification.
The third study (Chapter 5) used an algorithm with improved Turn detection to measure
macro-kinematics of eight male skiers continuously during a 10 km classical Distance
competition. Accuracy of sub-technique classification was further enhanced using manual
reclassification. DP was the predominant cyclical sub-technique utilised (43 ± 5% of total
distance), followed by DS (16 ± 4%) and KDP (5 ± 4%), with the non-propulsive Tuck
technique accounting for 24 ± 4% of the course. Large within-athlete variances in cycle length
(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. 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.
In the fourth study (Chapter 6) macro-kinematics were compared between six female skiers
competing in Sprint and Distance competitions in similar conditions on consecutive days,
over a 1.0 km section of track using terrain common to both competitions to eliminate the
influence of course topography. Mean race velocity, cyclical sub-technique velocities, and CR were higher during the Sprint race, while Tuck and Turn velocities were similar. Velocities
with KDP and DS were higher in the Sprint (KDP +12%, DS +23%) due to faster CR (KDP
+8%, DS +11%) and longer CL (KDP +5%, DS +10%), while the DP velocity was higher
(+8%) with faster CR (+16%) despite a shorter CL (-9%). During the Sprint the percentage of
total distance covered using DP was greater (+15%), with less use of Tuck (-19%). Across all
events and rounds, DP was the most used sub-technique in terms of distance, followed by
Tuck, DS, Turn and KDP. KDP was employed relatively little, and during the Sprint by only
half the participants.
The final case study (Chapter 7) focused on the insight coaches could gain from examining
variations in individual macro-kinematics for six female skiers across three rounds of a classic
Sprint competition. Individual macro-kinematic variations were influenced by personal
strengths and preferences, pacing strategies, and by interactions with other skiers in the headto-
head rounds. Potential coaching implications include using a range of CR and CL during
training, modifying these parameters during training to work on weaknesses, and altering
macro-kinematic race strategies depending on the course terrain, event round and on other
skiers’ tactics.
In conclusion this thesis outlines the development of a new cross-country skiing analysis
method that uses a single micro-sensor and a unique algorithm to effectively measure macrokinematic
parameters continuously during training and competition. This tool could be used
by researchers, coaches and athletes to better understand training and competition demands
and enhance performance. This research lays the ground-work for future research and
practical applications, which could include daily training monitoring, course profiling,
evaluation of sub-technique efficiency, and similar algorithm development for the Freestyle
technique.
| Date of Award | 2019 |
|---|---|
| Original language | English |
| Supervisor | Keith Lyons (Supervisor), Gordon WADDINGTON (Supervisor), Judith Anson (Supervisor) & Dale Chapman (Supervisor) |