Wearable System Applications in Performance Analysis of RaceRunning Athletes with Disabilities

Abstract

RaceRunning is a sport for disabled people and successful performance depends on reducing the amount of time spent travelling a specific distance. Performance analysis in RaceRunning athletes is based on traditional methods such as recording race time, distances travelled and frequency (sets and reps) that are not sufficient for monitoring training loads. The aims of this study were to monitor training loads in typical training sessions and evaluate technical adaptations in RaceRunning performance by acquiring sensor metrics. Five elite and competitive RaceRunning athletes (18.2 ± 2.3 yrs) at RR2 and RR3 levels were monitored for 8 weeks, performing in their usual training sessions while wearing unobtrusive motion sensors. The motion sensors were attached to the waist and lower leg in all training sessions, each lasting between 80 and 90 min. Performance metrics data collected from the motion sensors included player loads, race loads, work/rest ratio and impact shock directions, along with training factors (duration, frequency, distance, race time and rest time). Results showed that weekly training loads (player and race loads) followed acceptable threshold levels, according to assessment criteria (smallest worthwhile change, acute/chronic work ratio). The relationship between race velocity (performance index) and race load was non-linear and statistically significant, which led to different performance efficiency groups. Wearable motion sensor metrics revealed small to moderate technical adaptations following repeated sprint attempts in temporal running performance, variability and consistency. In conclusion, using a wearable-based system is an effective feedback tool to monitor training quality, revealing important insights into adaptations to training volumes in disabled athletes.

Keywords: RaceRunning; adaptations; feedback; performance efficiency; training loads; wearable-motion sensors.

Figure 1

Diagram of data management procedure in using the wearable system for monitoring RaceRunning performance. The main aims (bold) and tasks (italic) from session data collection to feedback provision are presented.

Figure 2

Examples of session training load monitoring procedures. Each athlete (A1,A2) ran multiple times as part of the training plan with enough rest periods. The main KPIs that were extracted in each session were related to training loads (external and internal) and sprint performance (time). We presented feedback based on the session performance individually and focused on the related KPIs. The performer load represents external load, whereas HR (beats/min) represents the internal load.

Figure 3

Monitoring weekly changes in training loads in one athlete (A). The descriptive changes in training load (A) were accompanied by two other criteria for checking the acceptable thresholds of training loads: SWC and ACWR. The results of AWC in different disability groups (B) and ACWR (mean ± SD) in different sessions (C) are presented in this Figure.

Figure 4

There was a non-linear relationship between race load and sprint performance in RaceRunners (A). This relationship was established as a criterion (significant association) to evaluate individual performance and for creating a classification system to evaluate training load (X), relative to sprint performance (Y). The vertical and horizontal lines are group median scores (B). The colours represent within class distributions.

Figure 5

Running gait profile of RaceRunning athletes in different training conditions. Temporal mean (A), variability (B) and consistency (C) of running cycle are different between normal and fatigue conditions.

Figure 6

Variations in training load during the whole study period (A) and in every session (B). The athletes shared the external loads in different directions, with slightly variations between sessions.