How AI Catches Squat Form Mistakes That Coaches Miss: A Machine Learning Analysis

Introduction: The Form Analysis Problem

I've been building an AI system that analyzes strength training form from video. Not as an academic exercise, but as a real product (SportSensAI) currently being used by high school teams and individual athletes.

The question that drove this work: Can machine learning catch form mistakes that human coaches miss?

The short answer: Yes. But the interesting part is how and why.

This post dives deep into the technical approach, the challenges of real-world deployment, and what I learned about the intersection of biomechanics and computer vision. If you're building similar systems, researching pose estimation for fitness, or just curious about how AI "sees" movement, this is for you.

Why Squat Form Matters (And Why It's Hard to Get Right)

Before diving into the ML approach, let's establish why squat form analysis is both critical and difficult.

The Injury Cost

Poor squat form isn't just ineffective—it's dangerous. The three most common injuries from improper squatting:

1. Knee injuries: From valgus collapse (knees caving inward)
2. Lower back strain: From excessive forward lean or spinal flexion
3. Hip impingement: From insufficient external rotation

According to research from the National Strength & Conditioning Association, form-related injuries account for 60% of all weightlifting injuries in high school athletes. Most of these are preventable with proper coaching.

Recent research published in the Journal of Exercise Science demonstrates the measurable impact of AI-based form analysis: personalized training programs that incorporate machine learning assessment yield a 20% reduction in injury incidence and 15% improvement in performance gains compared to traditional coaching alone.

The Coaching Bottleneck

Here's the problem: A high school strength coach typically oversees 50-100+ athletes. During a team lift session, they can't watch everyone simultaneously. Even experienced coaches admit they catch maybe 30-40% of form errors in real-time.

Zach Lush, CFSC (Certified Functional Strength Coach) and MPM (Mental Performance Mastery) certified, co-founder of SportSensAI and strength coach with over a decade of experience, explains the challenge:

"In a team setting, I can watch maybe 5-6 athletes closely during a lift session. The rest? I'm catching major breakdowns at best. The athletes who need the most eyes on them—beginners with poor movement patterns—are often the ones training alone during off-season. That's where injuries happen."

The athletes who need the most attention—beginners with poor movement patterns—are the ones most likely to train when no coach is present (summer off-season, home workouts, individual training).

This is the gap AI can fill.

Squat Depth Standards: Context Matters

One of the most debated aspects of squat form is depth. "How low should I go?" The answer depends entirely on your training context and goals.

Powerlifting Standard

In competitive powerlifting, the rule is clear: the hip crease must descend below the top of the knee. This is "parallel" depth. Anything above this results in a red light (failed lift). Powerlifters often train slightly below parallel to ensure they hit depth under competition conditions with maximal loads.

Olympic Weightlifting Standard

Olympic lifters require full depth—hamstrings to calves, hip crease well below parallel. This is necessary because the clean and snatch require catching the bar in a deep squat position. Olympic weightlifting squats (both front and back) are performed ass-to-grass to build the strength and positional strength needed for competition lifts.

Functional Fitness & General Strength

For general strength development and injury prevention, the standard is parallel or slightly below. This depth activates the glutes and hamstrings maximally while minimizing knee stress for most individuals. Going deeper than parallel is beneficial if mobility allows, but forcing depth with compensations (lumbar flexion, heel lift) creates more risk than reward.

AI Adaptation: SportSensAI asks about training context (powerlifting, Olympic lifting, general strength) before analysis and adjusts depth standards accordingly. A "good" squat for one context might be flagged as insufficient depth for another.

Common Squat Variations and Form Differences

Not all squats are created equal. The bar position, stance, and equipment change the biomechanics significantly:

AI Challenge: The system needs to recognize which variation is being performed to apply appropriate form standards. A "good" low bar squat has more forward lean than a "good" high bar squat, but both are correct within their context.

Mobility Requirements for Proper Squat Form

Poor squat form is often not a technique issue—it's a mobility limitation. The body compensates when it can't achieve required joint ranges of motion.

AI Insight: When the system detects form breakdowns, it often suggests mobility assessments before assuming it's a technique error. If an athlete consistently shows heel lift despite cueing, the issue is likely ankle mobility, not attention to coaching.

The Technical Approach: From Video to Feedback

Building an AI form analyzer involves several distinct stages. Here's how the SportSensAI system works:

Stage 1: Pose Estimation

The Goal: Extract 2D/3D joint coordinates from video frames.

I evaluated three main approaches:

1. OpenPose (CMU): Excellent accuracy, too slow for mobile deployment
2. MediaPipe Pose (Google): Fast, mobile-ready, good enough accuracy
3. MoveNet (TensorFlow): Fast, but less robust to occlusion

Final Choice: Initially prototyped with MediaPipe, but the production system uses a different approach—large language models with vision capabilities (more on this later).

Why Traditional Pose Estimation Has Limitations

MediaPipe and similar systems extract 33 body landmarks (shoulders, hips, knees, ankles, etc.) and give you (x, y, z) coordinates. From these, you can calculate joint angles:

• Knee flexion: Angle between hip-knee-ankle
• Hip flexion: Angle between torso-hip-knee
• Ankle dorsiflexion: Angle between knee-ankle-foot

Sounds straightforward, right? In practice, there are challenges:

Stage 2: The Multimodal AI Approach

After months of fighting pose estimation limitations, I took a different approach: Use vision-language models that can understand movement conceptually.

Instead of extracting coordinates and running classifiers, the system:

1. Samples frames from the video (typically 5-10 frames spanning the rep)
2. Feeds frames + prompt to a vision-capable LLM
3. Gets structured analysis in natural language

Here's the actual prompt structure (simplified):

You are an expert strength coach analyzing squat form. Review these video frames
showing one squat rep. Analyze:

DEPTH & RANGE OF MOTION
- Did the athlete reach parallel (hip crease below knee)?
- Was depth consistent across reps?

KNEE TRACKING
- Did knees track over toes or collapse inward (valgus)?
- Was knee movement controlled or jerky?

BACK POSITION
- Was spine neutral or rounded/hyperextended?
- Did chest stay up or collapse forward?

BAR PATH (if visible)
- Did the bar travel vertically or drift forward?

FOOT PRESSURE
- Did heels stay planted or lift?
- Was weight balanced or shifted to toes?

Provide:
1. What they did well (positive reinforcement)
2. Critical issues (injury risk, highlighted)
3. Minor improvements (technique optimization)
4. Specific coaching cues (actionable fixes)

Why This Works Better

Stage 3: Structured Output & Safety Scoring

Raw LLM output is natural language. For a production system, you need structure:

interface FormAnalysis {
  depth: {
    assessment: string;
    severity: 'good' | 'minor' | 'critical';
  };
  kneeTracking: { ... };
  backPosition: { ... };
  barPath: { ... };
  footPressure: { ... };

  safetyScore: number; // 0-100
  techniqueTips: string[];
  injuryRisks: string[];
}

The model is prompted to output JSON (with schema validation), which the app then parses and displays.

Safety Scoring Algorithm:

• Start at 100
• -30 for critical issues (severe valgus, spinal flexion under load)
• -10 for minor issues (slight heel lift, inconsistent depth)
• -5 for technique suboptimality (bar path drift, incomplete lockout)

Anything below 70 triggers a warning. Below 50, the app suggests reducing weight or seeking in-person coaching.

The Five Squat Form Mistakes AI Catches (That Coaches Often Miss)

Based on analyzing thousands of squat videos through the SportSensAI system, here are the form errors the AI catches most reliably:

CRITICAL ISSUE: Right hip is consistently 2-3 inches higher than left at
depth. This indicates mobility restriction or strength imbalance.

INJURY RISK: Continued training with asymmetry can cause SI joint pain and
compensatory spinal rotation.

FIX: Before next squat session:
- 90/90 hip stretch (right side) - 2 min
- Half-kneeling hip flexor stretch (right side) - 90 sec
- Retest with bodyweight squats

If asymmetry persists after mobility work, see a physical therapist
to rule out structural issues.

Squat Programming Principles: Turning Analysis into Progress

AI form analysis is only valuable if it translates to better training outcomes. Here's how to program squats effectively while using AI feedback to guide progression:

Beyond Squats: What Else Can AI Analyze?

The same approach works for other compound movements and sports-specific skills. For baseball pitchers, we've written a comprehensive comparison of AI pitching analysis vs professional systems like TrackMan.

Technical Challenges & Limitations

Building this system taught me what AI is good at (and what it's not). Transparency matters in ML:

Conclusion: AI as Strength Coach Augmentation

After building this system and seeing it used by real athletes, here's my take:

AI won't replace strength coaches.

The coach-athlete relationship, program design, motivation—these are fundamentally human.

But AI absolutely scales coaching attention.

One coach can effectively support 100 athletes with AI assistance, vs. 20-30 without it.

For teams without full-time S&C staff (most high schools, club sports), AI form analysis democratizes access to coaching feedback that was previously available only to elite programs with dedicated staff.

The question isn't "Can AI coach as well as a human?" It's "Can AI provide enough value to improve outcomes for athletes who otherwise have zero coaching?"

The answer, based on the data I've seen, is yes.

Try It Yourself

If you want to experience AI form analysis firsthand:

References & Further Reading