StrideIQ helps runners sanity-check their form from a short side-view video. Upload a 10-20 second clip and it highlights common issues as a quick verification tool.
StrideIQ is a running form analysis tool that empowers runners to assess their technique by uploading a short side-view video. The application is designed for anyone who runs—from beginners curious about their stride to experienced athletes monitoring efficiency—offering a quick, convenient verification without the need for a professional gait analysis lab. By analyzing just a few seconds of footage, StrideIQ highlights common form issues and provides data-driven recommendations, making it an accessible entry point into running biomechanics. Its core value lies in democratizing running form analysis, turning a smartphone video into actionable feedback in seconds.
Many runners suffer from injuries caused by inefficient form, yet access to professional gait analysis remains limited and costly. Runners often have no easy way to check if their overstride, heel strike, or pelvic drop is contributing to knee pain or shin splints. Without objective feedback, subtle problems persist, potentially leading to chronic issues. StrideIQ addresses this gap by offering a free, immediate self-assessment that can be done anywhere. It provides the first line of defense—a quick sanity check that helps runners decide if they need deeper coaching or medical attention, reducing the guesswork and anxiety around form-related injuries.
The analysis process begins with capturing a side-view video that meets specific criteria for reliable results. StrideIQ requires a clip of at least 3 seconds of real-time running, containing 4–6 strides, filmed with the camera fixed at roughly hip height and the runner moving consistently left‑to‑right or right‑to‑left. The tool supports 1080p footage at 30 fps, which is typical for modern smartphones, and also accepts 240 fps slow‑motion video from devices like the iPhone’s Slo‑mo mode. Good lighting and a subject that fills 60–90% of the frame height are recommended to ensure the algorithm can accurately track body position. These guidelines maximize the signal for the computer vision model, allowing it to reliably detect contact events and segment the stride.
Once the video is uploaded, users can choose between two analysis modes: Lite and Full. Lite mode is the default, optimized for faster processing and adequate for a quick initial check. Full mode delivers higher accuracy by using a more computationally intensive algorithm, which is better suited for detailed biomechanical scrutiny. This tiered approach gives runners the flexibility to balance speed and depth; a coach might use Lite to screen multiple athletes rapidly, then switch to Full for an in‑depth evaluation when needed. Both modes output the same types of results—form flags, metrics, and recommendations—but Full mode provides more precise skeleton tracking and finer detection of subtle movement patterns.
Publisher
A
admin
Launch Date2025-12-31
Platformweb
Pricingfree