Exercise Inventory System
Comprehensive, science-backed exercise database for MoPlan. Status: Documentation Complete - Ready for implementation
Overview
This exercise inventory is designed as an unlimited, filterable database - not a curated list. The system can hold all possible exercises and intelligently filter/select them based on:
- User goals and preferences
- Available equipment
- Experience level
- Training context (split, volume, fatigue)
- Scientific evidence
Documentation Structure
Reference Documents
Scientific foundations and research backing the data model.
| Document | Description |
|---|---|
| reference/01-anatomy-muscle-groups.md | 14 muscle groups with anatomy, functions, recovery parameters |
| reference/02-movement-patterns.md | 7 fundamental patterns, strength curves, balance ratios |
| reference/03-emg-research.md | EMG activation data by muscle group, peer-reviewed sources |
Schema Documents
Data model, relationships, and selection algorithm.
| Document | Description |
|---|---|
| schema/01-data-model.md | Complete entity definitions with TypeScript interfaces |
| schema/02-relationships.md | ERD, junction tables, cardinality, query patterns |
| schema/03-selection-algorithm.md | 3-phase algorithm: Filter → Score → Select |
| schema/04-exercise-entry-guide.md | How to add exercises: templates, enums, validation, review checklist |
| schema/05-integration-analysis.md | Integration with plan generation docs, gaps analysis |
| schema/06-scientific-alignment-verification.md | Research verification: All parameters cross-referenced with peer-reviewed studies |
Core Entities
Primary Entities
| Entity | Purpose | Key Attributes |
|---|---|---|
| Exercise | Central hub | Goal scores, difficulty, strength curve, hypertrophy mechanisms |
| MuscleGroup | 14 muscle groups | Recovery hours, volume ranges, antagonist relationships |
| MovementPattern | 7 patterns | Opposite patterns, recommended ratios |
| Equipment | Gym/home items | Availability flags, alternatives |
| ExerciseType | Classification | Set ranges, order priority, volume multipliers |
| ExecutionMode | 20+ techniques | Fatigue/time multipliers, goal compatibility |
| TrainingGoal | User objectives | Rep ranges, rest periods, type weights |
Junction Tables
| Junction | Connects | Extra Attributes |
|---|---|---|
| ExerciseMuscle | Exercise ↔ MuscleGroup | Role (primary/secondary/stabilizer), activation % |
| ExerciseEquipment | Exercise ↔ Equipment | Required flag, quantity |
| ExerciseMode | Exercise ↔ ExecutionMode | Effectiveness rating |
| ExerciseRelationship | Exercise ↔ Exercise | Type (alternative/progression/superset), similarity |
Selection Algorithm
Three-phase approach for intelligent exercise selection:
┌─────────────────────────────────────────────────────────────────────────┐
│ EXERCISE SELECTION FLOW │
├─────────────────────────────────────────────────────────────────────────┤
│ │
│ [All Exercises] ──► PHASE 1: FILTER ──► [Candidate Pool] │
│ • Equipment available │
│ • Difficulty appropriate │
│ • Targets needed muscles │
│ • Not recently used │
│ │
│ [Candidate Pool] ──► PHASE 2: SCORE ──► [Ranked Exercises] │
│ • Goal alignment (30%) │
│ • Muscle targeting (25%) │
│ • Pattern need (15%) │
│ • User preference (15%) │
│ • Variety (10%) │
│ • Evidence quality (5%) │
│ │
│ [Ranked Exercises] ──► PHASE 3: SELECT ──► [Final Selection] │
│ • Meet volume targets │
│ • Balance patterns │
│ • Order by fatigue/complexity │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Key Design Decisions
1. Unlimited Inventory
The database is designed to grow indefinitely. Unlike a curated "top 100" list, this system:
- Holds all valid exercises
- Uses smart filtering to surface relevant options
- Enables personalization through scoring
- Supports variations and progressions
2. Multi-Dimensional Scoring
Each exercise is evaluated against multiple dimensions:
- Goal scores: strength, hypertrophy, endurance, power, weight_loss
- Hypertrophy mechanisms: mechanical_tension, metabolic_stress, muscle_damage
- Safety metrics: joint_stress, injury_risk_areas, contraindications
- Programming attributes: strength_curve, difficulty, technical_demand
3. Relationship-Aware
The system understands:
- Alternatives: Same muscles, different equipment
- Progressions/Regressions: Difficulty scaling
- Superset pairs: Complementary exercises
- Antagonist patterns: Push/pull balance
4. Evidence-Based
All exercise data is backed by:
- Peer-reviewed EMG studies (Tier 1)
- Academic/professional sources (Tier 2)
- Evidence-based expert consensus (Tier 3)
Research Sources
Tier 1: Peer-Reviewed EMG Studies
- Boeckh-Behrens & Buskies (2000) - Comprehensive EMG analysis
- Contreras et al. - Glute activation research
- Schoenfeld et al. - Hypertrophy and exercise selection
Tier 2: Academic/Professional
- NSCA Exercise Technique Manual
- ACE Exercise Library
- Strength & Conditioning Research
Tier 3: Evidence-Based Experts
- Stronger by Science
- Jeff Nippard (EMG-based selections)
- Renaissance Periodization
Related Documents
- ../05-exercise-database.md - Implementation schema
- ../02-ten-pillars.md - Exercise selection principles
- ../04-execution-modes.md - Execution variations
Last Updated: 2025-12-08