🖥️How Recommendations are Generated

How Recommendations are Generated

Our recommendation engine uses a blend of data analytics and AI-driven insights to provide personalized advice based on your performance data.

How It Works

1. Data Collection: Once your video has been processed, our system gathers a variety of performance metrics—from speed and cadence in running to jump height and reactive strength index in jumping, and bar speed and power in weightlifting.

2. Benchmark Comparison: These metrics are compared against optimal ranges and historical data from your previous sessions. The system identifies deviations that may indicate areas for improvement.

3. Rule-Based & AI Analysis: Our engine uses predefined rules along with machine learning models to analyze the data. For example:

   • Running: If your ground contact time is longer than ideal or cadence is low, the system will flag these metrics.

   • Jumping: Discrepancies between jump height and flight time or an imbalanced reactive strength index trigger specific feedback.

   • Weightlifting: Lower than expected bar speed or high catch times may prompt recommendations on technique adjustments.

   • Walking: If your cadence is below the recommended range, contact time is prolonged, or stride symmetry is imbalanced, the system will highlight these metrics. For example, excessive contact time or overstriding may trigger recommendations to increase step frequency or adjust posture for smoother walking mechanics.

   • Mobility Assessment: If your squat depth is inconsistent, hip symmetry drifts beyond ideal ranges, or trunk angle increases excessively, the system flags these issues. Metrics like descent/ascent time or pelvic tilt help detect stability, mobility restrictions, and compensations, prompting recommendations for mobility drills or technique adjustments.

   • Workspace Wellness (Ergonomics): If your cranio-vertebral angle indicates forward head posture, desk–elbow delta exceeds ergonomic standards, or wrist angles fall outside neutral ranges, the system highlights these deviations. Spinal curvature (kyphosis/lordosis) and pelvic tilt are analyzed to identify potential ergonomic or posture-related concerns.

4. Generating the Feedback: Using generative AI, the system then crafts clear, actionable recommendations. These suggestions are displayed alongside your report, providing context and steps for improvement.

5. Presentation: Recommendations are shown in a concise, expandable format. Each recommendation includes a short explanation, supporting data, and often a benchmark or target value to aim for.

What to Expect in the Recommendations Section

• Tailored Advice: Each recommendation is specific to your current performance. For instance, a runner might receive a suggestion to “Increase your cadence by 5 steps per minute to improve efficiency.”

• Visual and Textual Feedback: The feedback appears both as text and, where applicable, within graphical annotations in your reports.

• Continuous Refinement: As you log more sessions, our system continuously learns from your data. This means recommendations evolve over time to remain relevant to your progress and training goals.

Examples:

Example Shoe Recommendations

1. Jordan Retro 6 G White/Khaki

   • Metrics: Moderate cushion (8-12 mm), 6.5 mm heel-to-toe drop.

   • Recommendation: "Great for stability with your 11.0 km/h pace, but consider a lower drop to reduce contact time."

   • Supporting Data: Contact time > 6s suggests inefficiency with current drop.

2. Air Jordan 7 Retro SE Vachetta

   • Metrics: High cushion (420.5 mm avg), low drop (0-4 mm).

   • Recommendation: "Ideal for reducing landing impact and improving flight time consistency."

   • Supporting Data: Flight time variability detected in data.

Enhanced Examples

Example 1: Improving Efficiency

• Data: Contact time averages 9s, stride length 420.5 mm.

• Recommendation: "Switch to a zero-drop shoe (e.g., Jordan Nu Retro 1) to reduce contact time to 3-6s and boost stride length to 430 mm."

• Target: Efficiency increase of 5-10% at 11.0 km/h.

Example 2: Balancing Comfort and Speed

• Data: Cushion avg 6.5 mm, speed 11.0 km/h.

• Recommendation: "Opt for moderate cushioning (8-12 mm) like the Jordan Retro 6 G to maintain comfort without sacrificing pace."

• Target: Consistent flight time across strides.