Creating A Rule-Based Issue Detection System For Exercise Form Analysis

Hey guys! Today, we're diving deep into creating a rule-based issue detection system that's going to revolutionize how we analyze movement and exercise form. This is a super exciting project, and we're building it to automatically detect and categorize those pesky movement issues that can hinder progress and even lead to injuries. Think of it as your personal AI coach, but way more detailed!

Overview

Our main goal here is to build a robust rule engine that can pinpoint and categorize movement issues during various exercises. This system will be a crucial component of our larger feedback generation system. It's like giving our AI eyes that can see exactly what's going on with your form. This phase, Phase 4.1, is a direct continuation of Phase 4: Feedback Generation and relies heavily on the insights we gained from Phase 3.5 - Injury Risk. We're estimating this will take about 2-3 days to complete, so let's get cracking!

Tasks

To get this show on the road, we've got a few key tasks lined up. Each one is a stepping stone to our final, awesome issue detection system.

Creating the Issue Detector Service

First up, we need to create the core of our system: src/services/issue_detector_service.rs. This is where the magic happens! This service will house all the logic for detecting and categorizing movement issues. It's the brain of our operation, and we're going to make it a smart one.

Defining Issue Rules per Exercise

Next, we're going to define specific issue rules for different exercises. This is where we get super granular. For each exercise, we'll outline the common issues that can occur. Think of these as the "watch-out-fors" for each movement. For example:

• Squat: We're looking for things like "Knees caving inward", "Insufficient depth", and "Excessive forward lean".
• Deadlift: Common culprits include "Lower back rounding", "Early arm pull", and "Poor lockout".
• Running: We'll be on the lookout for "Overstriding", "Heel striking", and "Excessive vertical oscillation".
• Push-up: Keep an eye out for "Elbows flaring", "Hip sagging", and "Incomplete ROM".
• Plank: Issues like "Hip sagging", "Hip piking", and "Head dropping" are on our radar.

These rules are the foundation of our detection system. The more detailed and accurate we make them, the better our system will be at identifying problems.

Assigning Severity Levels

Not all issues are created equal, right? Some are minor, while others could lead to serious injuries. That's why we're going to assign severity levels to each issue: critical, warning, or minor. This helps us prioritize feedback and alert users to the most pressing concerns first. A critical issue might be something that poses an immediate risk of injury, while a minor issue might just be a slight form imperfection.

Including Timestamps and Frame of Occurrence

To make our feedback even more actionable, we'll include the exact timestamp and frame where each issue occurs. This lets users rewind and see precisely what went wrong. It's like having a slow-motion replay for your form! Imagine being able to pinpoint the exact moment your knees started caving in during a squat – super helpful, right?

Adding Confidence Scores

Our system isn't perfect (yet!), so we'll also include confidence scores (ranging from 0 to 1) for each detected issue. This tells users how sure our system is about the issue. A higher score means we're more confident in the detection, while a lower score might warrant a closer look. This adds a layer of transparency and helps users interpret the feedback more effectively.

Detecting Recurring vs. One-Time Issues

Is an issue a one-time fluke, or a recurring problem? That's what we want to know! Our system will differentiate between issues that happen occasionally and those that pop up repeatedly. Recurring issues are usually a sign of ingrained movement patterns that need addressing.

Prioritizing Issues

When multiple issues are detected, we need a way to prioritize them. We'll use a simple but effective formula: frequency * severity. This means that frequent, severe issues will bubble to the top, ensuring users focus on the most critical problems first.

Formatting Issues as MovementIssue Structs

To keep things organized, we'll format each detected issue as a MovementIssue struct. This struct will contain all the relevant information about the issue, such as the type of issue, severity, timestamp, confidence score, and more. It's like a neat little package for each problem we identify.

Adding Unit Tests

No good system is complete without thorough testing! We'll be adding unit tests to ensure our issue detection logic is working correctly. These tests will help us catch bugs early and ensure our system is reliable.

Documenting Issue Rules

Last but not least, we'll be documenting all our issue rules. This is crucial for maintainability and transparency. It helps us (and others) understand why certain issues are flagged and how the system works under the hood.

Deliverables

So, what will we have at the end of this sprint? Here's the rundown:

• IssueDetectorService implementation: The heart of our issue detection system.
• Issue rule definitions: A comprehensive list of rules for each exercise.
• Unit tests: To ensure our system is working flawlessly.
• Issue detection documentation: A guide to understanding our issue detection logic.

Acceptance Criteria

How will we know if we've nailed it? Here are the criteria our system needs to meet to be considered a success:

• Issues detected match expert assessment (>85%): Our system should align with how a human expert would assess movement.
• Severity levels appropriate: The assigned severity levels should accurately reflect the risk associated with each issue.
• Timestamps accurate: The timestamps should pinpoint the exact moment an issue occurs.
• Confidence scores calibrated: The confidence scores should provide a realistic assessment of our system's certainty.
• Tests pass: All unit tests must pass, indicating our code is functioning as expected.

Diving Deeper into Key Concepts

Now that we've laid out the plan, let's zoom in on some of the key concepts that make this rule-based issue detection system tick. We'll explore why each component is crucial and how they fit together to create a powerful tool for exercise form analysis.

The Importance of Rule-Based Systems

Why go with a rule-based system in the first place? Well, rule-based systems offer a few key advantages. They're transparent, explainable, and relatively easy to maintain. Unlike complex machine learning models (which we might explore later!), rule-based systems operate on clearly defined rules. This means we can easily understand why a particular issue was flagged and adjust the rules as needed. It's like having a clear recipe for issue detection!

Imagine if our system just told you,