Introduction

Whether you’re working in clinical rehab or high-performing sport, understanding how someone moves is key – and that’s where temporal gait parameters come in. These time-based metrics, like step duration or stride time, are becoming powerful digital biomarkers for spotting movement asymmetries, tracking recovery, or even monitoring the progression of conditions like Parkinson’s disease.

The problem? The tools we’ve traditionally relied on – motion capture systems, force plates, and pressure mats – are confined to labs. They’re accurate, yes, but they also come with big limitations: small capture spaces, expensive setups, and movements that don’t always reflect real-world activity. As a result, practitioners have often been stuck with data that’s technically precise but not functionally relevant.

That’s why there’s growing excitement around wearable technologies that bring gait assessment out of the lab and into everyday life. These tools offer a more flexible, scalable, and realistic way to understand how people truly move- whether they’re walking through a clinic hallway or sprinting on a track.

Methodology

We worked with 34 healthy, competitive team sport athletes – an even mix of 17 males and 17 females – all actively training and injury-free at the time of testing. All testing took place on an indoor sprint track equipped with high-performance measurement systems. We used 30 meters of Optojump and four Kistler force plates laid out in sequence – a setup designed to capture accurate foot contact data at multiple speeds.

Each athlete completed five repetitions at a range of movement speeds (outlined in Table 1). The Optojump system was set up to capture data across the full trial distance, while the force plates were placed in the final 4 meters — except during “acceleration start” trials, where they were positioned in the first 4 meters to capture the initial push-off phase. Data were averaged across the trial for purposes of analysis.

Results

We compared the DANU system against two gold-standard technologies — force plates and Optojump — across key temporal gait metrics. The results show remarkably strong agreement across all comparisons.

DANU vs. Force Plates

DANU closely matched force plate readings across contact time, swing time, and stride time. For stride time, the mean difference was minimal (1 ms), with an intraclass correlation coefficient (ICC) of 0.998, indicating near-perfect consistency. Contact time showed a minimal mean difference of just 1 ms and an ICC of 0.994. Even swing time, often more variable, maintained strong agreement with an ICC of 0.954. Bland-Altman limits of agreement remained tight for all parameters, and all correlations were statistically significant (p < 0.001).

DANU vs. Optojump

Similarly, DANU performed exceptionally well when compared to Optojump. Stride time differences were again negligible (0.87 ms), with an ICC of 1.0. Contact time showed a slightly higher mean difference (13 ms) but still very high agreement (ICC = 0.992). Swing time remained the most variable, with a mean difference of -13 ms and an ICC of 0.916 — still indicating excellent agreement.

Across both comparisons, Pearson correlations were highly significant (p < 0.001), confirming strong linear relationships between DANU and both reference systems.

Table 2. Temporal Parameter Agreement Across Devices and Speeds: DANU Vs. Force Plates (FP)

Table 3. Temporal Parameter Agreement Across Devices and Speeds: DANU Vs. Optojump (Opto)

What Do These Results Tell Us?

The results from this validation study are clear: the DANU system holds up incredibly well when compared to lab-based gold standards like force plates and Optojump. Across all key temporal gait metrics — including contact time, swing time, and stride time — the DANU system showed excellent agreement with both reference tools.

What’s especially encouraging is the consistency of DANU’s performance. Metrics like stride time showed virtually no difference from force plates or Optojump, with near-perfect intraclass correlations (ICC > 0.99). Even more variable measures like swing time still demonstrated strong agreement, suggesting that DANU can reliably capture more subtle elements of gait timing.

These findings are important because they demonstrate that a wearable, field-based solution like DANU can provide lab-level accuracy — without the constraints of traditional motion capture environments. This opens the door for coaches, clinicians, and researchers to gather meaningful, high-quality data in real-world settings, whether that’s on the track, in a clinic, or during live gameplay.

While lab-based systems will always have their place for detailed biomechanical analysis, these results show that tools like DANU can bring that level of insight into the environments where athletes and patients actually move.