PhD Pinboard: Monitoring Neuromuscular Fatigue from Wearable Technology

This PhD Pinboard article by Michael Gerhardy explores how wearable technology can be used to monitor neuromuscular fatigue in-situ in team sport athletes, helping sports science practitioners with training load management.

My name is Michael Gerhardy, and I am a PhD Candidate in the School of Exercise and Nutrition Sciences at Deakin University in Melbourne, Australia. My PhD research focuses on the role of neuromuscular fatigue (NMF) and how it impacts adaptations to concurrent training in team sport athletes.

Specifically, I research how altering the neuromuscular demands of running-based interval sessions within concurrent training can subsequently impact on strength training performance and adaptations.

NMF is a common occurrence in athletes, and we see accumulations in athlete fatigue levels from factors including high training/match demands, and limited recovery between sessions. My aim is to research and help practitioners become more proficient in quantifying NMF during interval sessions, and to assist in limiting the risks of excess fatigue and/or injury.

My Story

As a long-distance runner who has completed several marathons, NMF is no stranger to me throughout a training block. Fatigue can be constant, and adding in strength sessions to improve overall performance can increase overall NMF throughout these training blocks.

So I’ve always been interested to understand exactly how much NMF an athlete is experiencing, and how we can use that information to inform loading decisions. From my own personal experience, at times, I would feel quite tired and sore, but my GPS watch would tell me my pace, distance, and splits are consistent, indicating that I’m maintaining similar external load despite my perceptual responses, and may therefore not be that fatigued.

However, as I’ve progressed through my research, I’ve learnt that it is often a matter of not just WHAT we do, but instead HOW we do it. Through understanding changes in running gait and stride variables under fatigue (e.g., ground contact time, stride length, vertical stiffness), we gain so much more information about NMF status in athletes in-situ, as opposed to traditional measures collected outside of the training environment.

It’s been surprising to observe how participants elicit similar external GPS loads, but the way they compensated their running strategy under fatigue blew my mind. These experiences have been a key turning point for me in wanting to help other practitioners improve their NMF monitoring practices to assist in their loading and training decisions. 

My PhD was structured into the following sections, each of which I’ll detail the key findings of below, before summarising the practical relevance and conclusions of my thesis:

• Assessing NMF using Wearable Technology

• An Alternate Approach to Quantifying NMF

• Quantifying NMF over an Eight-Week Micro Cycle using GPS-embedded Accelerometers

Key Findings

Assessing NMF using Wearable Technology

Practitioners may assign certain interval sessions to athletes to elicit a targeted load, but how much NMF occurs during the subsequent session remains unknown. To further understand this problem, recreationally active male participants (22.4 ± 4.1 years) in one study completed a series of three different running interval sessions (short intervals, long intervals, and sprint intervals) in a randomised order, with each session differing in their estimated neuromuscular demands.

Figure 1. Outline of SRP testing on a field to assess NMF using wearable technology.

To quantify NMF, we set up a standardised running protocol (SRP) where participants completed a set of four submaximal runs at a standardised pace (e.g., 5 m.s-1) pre- and post-session. The figure below outlines an example of an SRP protocol with 60-m runs completed in ~12 seconds (i.e., 5 m.s-1).

PlayerMaker™ foot-mounted inertial measurement units (IMU) were used to determine changes in variables including ground contact time, flight time, and leg and vertical stiffness to estimate the presence of NMF. 

Figure 2. Diagram of PlayerMaker™ unit positioning on lateral malleolus of each foot. Each unit was housed tightly in custom-made silicone straps to prevent excess movement.

We found significant changes in all these variables following all three running-based interval sessions, including increases in ground contact time, and decreases in flight time, stride length, and leg and vertical stiffness, which all indicate subtle changes in running strategy to maintain the same physical output. 

Interestingly, we also observed greater between-trial differences in stride length and leg stiffness in sprint intervals versus short and long intervals, confirming that sprint intervals are likely to elicit higher neuromuscular demands.

Figure 3. Between-trial differences in leg stiffness (kN/m2) measured between interval sessions with varying estimated neuromuscular demands. * p < 0.05 versus short and long intervals.

An Alternate Approach to Quantifying NMF

Throughout the study, I also decided to dive into the work of Garrett et al. (1) to determine whether an alternative approach to SRP analysis created better insight into athlete NMF status.

The theory was that if you divided an SRP interval into two distinct phases (acceleration and constant velocity), you could obtain greater insights into athlete fatigue and avoid any true effects being diluted when analysing the whole interval. To extract these phases, a 6th order polynomial was fitted to the 10 Hz GPS acceleration data to obtain the timestamps of when a participant was accelerating and at a constant velocity.

Figure 4. Example of 6th-order polynomial fitted to 10Hz acceleration data during each interval of the SRP. Fitted acceleration above the threshold (black dashed line) was defined as acceleration, while fitted acceleration below the threshold was defined as constant velocity.

We found isolating the analysis to the acceleration phase showed significantly lower ground contact times (p < 0.001, d = -1.17), and significantly greater vertical stiffness (p < 0.001, d = 1.12) vs. the whole SRP interval.

We also noted decreases in mean velocity (-0.189 m.s-1, p = 0.044), distance (-1.19 m, p = 0.004), and near decreases in duration during the acceleration phase (-0.165 s, p = 0.065), while constant velocity duration simultaneously increased (+0.529 s, p = 0.016), along with increased distance (+1.85 m, p = 0.036), and increased Player Load (+0.303 AU, p < 0.001).

These findings show that athletes may exhibit different running strategies at different times of an SRP, which might be made invisible when we analyse a whole SRP interval. For example, if practitioners wish to investigate how a player may run, the acceleration phase can be isolated to see how the player generates movement. However, as most of an SRP is run at constant velocity, we will not get this same information by analysing the whole interval and may not fully understand how the player is generating movement. These findings create a more holistic and individualised understanding of athlete movement under fatigue and can then inform practitioners if players are ready for matches, or if more recovery time is needed.

The findings from this project were presented at the Sports Medicine Australia Conference in Melbourne in 2024 (2).

Figure 5. Cohen’s d effect sizes of differences in stride variables at isolated phase of SRP vs. whole sprint. Effects outside the green dotted line indicate a large effect (d = 0.80).

Quantifying NMF over an Eight-Week Micro Cycle using GPS-embedded Accelerometers

During my final PhD study in 2024, we recruited participants to complete an eight-week concurrent training program, which was divided into two training groups:

• higher neuromuscular demand interval sessions + resistance training (H-HIIT + RT)

• lower neuromuscular demand interval sessions + resistance training (L-HIIT + RT)

Pre-and-post interval sessions, participants again completed an SRP involving 4 x 70-m runs at ~5 m.s-1, but this time only wore a GPS unit (Catapult Vector) located at the thoracic spine to quantify changes in stride variables (and therefore NMF) using an internally validated MATLAB algorithm developed during my PhD. This was done to assess the possibilities of quantifying key metrics associated with NMF using equipment already used by most practitioners. We also limited analysis to the acceleration phase of the SRP to limit a deliberate strategy by participants to run differently to indicate limited fatigue.

As expected, we quantified minimal changes in NMF (measured via changes in ground contact time and vertical stiffness) across the eight-week micro cycle in the L-HIIT + RT group, however the H-HIIT + RT group showed significant changes in these metrics, indicating the presence of NMF.

Figure 6. Changes in ground contact time (s) assessed via GPS-embedded accelerometers pre-interval training across an eight-week micro cycle. p < 0.05 * p < 0.01 *** p < 0.001 versus. Training Week 1.

Figure 7. Changes in vertical stiffness (kN/m2) assessed via GPS-embedded accelerometers pre-interval training across an eight-week micro cycle. p < 0.05 * p < 0.01 *** p < 0.001 versus Training Week 1.

As a researcher, this gave me great confidence in the ability to utilise current wearable technology to quantify NMF in athletes, but in a time efficient and more task-specific manner compared to current practices outside of the training environment.

How can we apply these findings in practice?

This research provides applied practitioners with a greater ability to understand the NMF that occurs from various running based interval sessions, along with an ability to successfully quantify metrics associated with NMF using wearable technology. In addition, they can also have confidence in isolating SRP phases to gain greater insight into athlete NMF status and create more knowledge into how athletes change their running strategy under fatigue to produce common movements (e.g., acceleration). This therefore assists in the decision-making process surrounding training load and adjustments to training design, as well as potentially being a key tool in monitoring excess NMF and limiting injury risks in athletes.

Extracting and analysing raw accelerometer data also takes time and lots of computational resources, which can leave these opportunities unavailable for practitioners. This research also shows already-known metrics including PlayerLoad, peak velocity, and isolating the vertical axis of PlayerLoad can be used to measure NMF. As there are high vertical forces during running, there will often be changes detected in this axis under NMF that indicate changes in movement strategy.

Peak running velocity will also typically change under fatigue during an SRP, even though the velocity is controlled. Prior research determined changes in peak running velocity under fatigue in soccer players (3) and is a simple and familiar metric for practitioners. Despite some wearable data being difficult to extract (e.g., ground contact time, vertical stiffness), practitioners can still measure NMF using wearable technology in SRP tests using metrics that are already available to them.

Therefore, even if some metrics are unable to be collected to infer NMF, practitioners can be confident in still being able to measure NMF using wearable technology as part of their everyday monitoring practices.

Final Reflections

Incorporating NMF assessment using wearable technology into live workflows would be revolutionary for Sport Science. The ability to assess player fatigue in real time during training and matches would allow practitioners to make instant decisions regarding changes in running strategies under fatigue, potentially reducing the risk of injury occurrences.

This research has demonstrated that NMF can be accurately measured using wearable technology and can be used to inform decision making, but currently this is limited to post-session processing and cannot be done in live workflows. Researchers and technology developers should research live accelerometer monitoring further to develop this into a reality for sports teams worldwide. 

We are only beginning to scratch the surface with wearable NMF monitoring, and lots more can be done. We should apply this work into the monitoring practices of elite and sub-elite teams across a variety of sports to assess the ecological validity of these tools in real-world scenarios. Despite this being part of my work currently, early indications suggest it works and can be incorporated with minimal disruptions, which is very exciting for practitioners of the future. I look forward to seeing what NMF monitoring will be like over the next ten years.

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Frequently Asked Questions

What is neuromuscular fatigue (NMF) and why is it important in sport?

NMF is simply defined as a reduction in muscle force, that is often reversible through recovery. Depending on the amount of NMF induced, some athletes may take longer to recover more than others. There are key links between NMF and risk of injury (4), and the amount of NMF in athletes helps inform practitioners about loading and training design in athletes to prevent excess residual fatigue and reduce injury risk.

How can wearable technology be used to measure neuromuscular fatigue?

We can use wearable technology using SRP testing to measure NMF. The key aim is to utilise wearables (i.e., GPS, IMU’s) to measure HOW athletes are running under fatigue, and if there is any evidence of them compensating to maintain physical output. Metrics including distance/velocity, and ground contact time are key to understanding NMF in athletes, and the level of change in these metrics can indicate the amount of NMF athletes are experiencing.

How can I trust that the data I collect from a GPS unit is reliable and sensitive to detect true changes in athlete NMF?

Multiple research papers in the area confirm that use of GPS embedded accelerometers is reliable and sensitive to detect true changes in NMF (5, 6). Practitioners are recommended to conduct internal reliability and sensitivity testing on their own populations, but can be confident in assessing true changes in NMF.

What are the best metrics to monitor for neuromuscular fatigue in athletes?

Measuring changes in ground contact time and vertical stiffness are key to monitoring changes in NMF. These two metrics give practitioners very important information on how athletes are running under fatigue, such as spending longer durations on the ground to maintain running velocity and being less stiff and being unable to deal with forces achieved during running. If practitioners cannot get these metrics through their wearable technology, common metrics including peak velocity and PlayerLoad will also provide similar information on how athletes are running under the presence of NMF.

Why does running strategy change under fatigue even when external load stays the same?

Athletes may experience accumulations of fatigue when undertaking an activity, even when the external loading remains constant. These accumulations can be from residual fatigue from prior sessions for example, and even if the external load is the same, there can be compensations of running strategy to maintain the desired load. This is often evidenced through increases in ground contact time to maintain performance, and decreased vertical stiffness as the athlete can no longer produce the same forces and mechanical energy under fatigue.

References

1.     Garrett J, Graham SR, Eston RG, Burgess DJ, Garrett LJ, Jakeman J, Norton K. A novel method of assessment for monitoring neuromuscular fatigue in Australian rules football players. Int. J. Sports. Physiol. Perform. 2019 May 1;14(5):598-605. https://pubmed.ncbi.nlm.nih.gov/30427228/

2.     Gerhardy M, Fyfe J, Feros S, Hamilton L. Breaking down the standardised run: additional insights into the monitoring of neuromuscular fatigue. J. Sci. Med. Sport. 2024 Oct 1;27:S78. https://doi.org/10.1016/j.jsams.2024.08.089

3.     Garrett J, Akyildiz Z, Leduc C, van den Hoek D, Manuel Clemente F, Ardigò LP. Peak running speed can be used to monitor neuromuscular fatigue from a standardized running test in team sport athletes. Res. Sports. Med. 2023 Jul 4;31(4):319-30. https://pubmed.ncbi.nlm.nih.gov/34383574/

4.     Taylor JL, Burkhart TA. Tired of ACL Injures: A Review of Methods and Outcomes of Neuromuscular Fatigue as a Risk Factor for ACL Injuries. Biomechanics. 2025 Feb 3;5(1):11. https://www.mdpi.com/2673-7078/5/1/11

5.     Leduc C, Tee J, Lacome M, Weakley J, Cheradame J, Ramirez C, Jones B. Convergent validity, reliability, and sensitivity of a running test to monitor neuromuscular fatigue. Int. J. Sports. Physiol. Perform. 2020 Sep 1;15(8):1067-73. https://pubmed.ncbi.nlm.nih.gov/32663385/

6.     Horsley BJ, Tofari PJ, Halson SL, Kemp JG, Chalkley D, Cole MH, Johnston RD, Cormack SJ. Validity and reliability of thoracic-mounted inertial measurement units to derive gait characteristics during running. J. Strength. Cond. Res. 2024 Feb 1;38(2):274-82. https://pubmed.ncbi.nlm.nih.gov/37884006/