Rehabilitation Training Analysis Skill

Core Functions

The rehabilitation-training analysis skill provides comprehensive analysis of rehabilitation data, helping users track recovery progress, identify improvement patterns, and optimize their training plan.

Main function modules:

1. Recovery-progress analysis - assess trends in functional improvement and rehabilitation effect
2. Functional-improvement curves - visualize changes in ROM, strength, balance, and other functional metrics
3. Pain-pattern recognition - analyze trends in pain scores and triggers
4. Goal-attainment assessment - track completion of rehabilitation goals
5. Rehabilitation-phase analysis - assess current-phase progress and readiness to transition
6. Training-adherence assessment - analyze execution of the training plan

Triggers

The skill triggers automatically in the following cases:

1. The user runs /rehab progress to view recovery progress
2. The user runs /rehab analysis for a rehabilitation analysis
3. The user runs /rehab trends to view trend analysis
4. The user runs /rehab report to generate a rehabilitation report

Execution Steps

Step 1: Read data

Read the rehabilitation data files:

• data/rehabilitation-tracker.json - main rehabilitation profile
• data/rehabilitation-logs/YYYY-MM/YYYY-MM-DD.json - daily training logs

Data validation:

• Check whether the files exist
• Validate the integrity of the data structure
• Confirm there are enough data points (recommended: at least 3 assessments or 10 days of training records)

Step 2: Functional-assessment trend analysis

Range-of-motion (ROM) analysis:

- Analyze ROM measurements at different time points
- Compute ROM-improvement rate (degrees/week)
- Identify ROM plateaus or regressions
- Predict time to reach target ROM
- Compare with the target range

Strength-improvement analysis:

- Track strength-grade changes (MMT score)
- Identify strength-gain patterns
- Compare recovery speed across muscle groups
- Assess strength imbalances

Balance-function analysis:

- Balance-test score trend
- Single-leg-stance time improvement
- Balance-stability assessment
- Fall-risk change

Step 3: Pain-pattern analysis

Pain time-series analysis:

- Analyze morning-pain trend
- Analyze post-activity pain trend
- Identify pain-worsening/relief patterns
- Relate pain to training intensity

Pain-trigger identification:

- Relationship between specific exercises and pain
- Correlation between training intensity and pain
- Relationship between activity type and pain
- Effect of time factors on pain

Step 4: Training-adherence calculation

Adherence metric:

Adherence = (actual sessions / planned sessions) × 100%

Analysis dimensions:

• Weekly adherence
• Monthly adherence
• Overall adherence
• Adherence by training type

Step 5: Goal-attainment assessment

Goal-progress tracking:

• Compute each goal's completion percentage
• Estimate goal-attainment time
• Identify lagging goals
• Provide goal-adjustment suggestions

Step 6: Rehabilitation-phase assessment

Current-phase analysis:

• Phase-goal completion
• Readiness to enter the next phase
• Phase-transition suggestions

Step 7: Generate report

The output includes:

• Recovery-progress summary
• Functional-improvement trends
• Pain control
• Training-adherence rating
• Goal-attainment assessment
• Phase-progress suggestions
• Personalized advice

Output Format

Recovery-progress report structure

# Recovery Progress Report
**Report date**: YYYY-MM-DD
**Rehabilitation duration**: X days
**Current phase**: Phase X - phase name

## 1. Recovery-progress summary

[Overall rating: excellent/good/fair/needs improvement]
- Rehabilitation duration: X days (week X)
- Sessions completed: X
- Training adherence: X%
- Current-phase progress: X%

## 2. Functional-improvement trends

### Range of motion (ROM)
- [joint] [movement]: baseline X° → current X° → improvement X°
- Improvement rate: X°/week
- Estimated time to target: X weeks
- Trend analysis: [description]

### Strength assessment
- [muscle group]: baseline X/5 → current X/5 → improvement X grades
- Strength-gain pattern: [description]
- Strength balance: [assessment]

### Balance function
- [test type]: baseline X → current X → improvement X
- Balance stability: [assessment]
- Fall risk: [assessment]

## 3. Pain control

- Average pain level: X/10
- Pain trend: [improving/stable/worsening]
- Pain pattern: [description]
- Triggers: [identified triggers]
- Pain-control suggestions: [suggestions]

## 4. Training adherence

- Overall adherence: X%
- Planned sessions: X
- Actual sessions: X
- Adherence rating: [excellent/good/fair/needs improvement]
- Reasons for missed sessions: [if any]

## 5. Goal attainment

### Goals achieved (X)
- Goal 1: [description] - achieved: YYYY-MM-DD
- ...

### Goals in progress (X)
- Goal 1: [description] - current progress: X% - estimated: YYYY-MM-DD
- ...

### Lagging goals (X)
- Goal 1: [description] - current progress: X% - needs attention

## 6. Rehabilitation-phase progress

**Current phase**: Phase X - [phase name]
- Phase goals completed: X/X
- Phase progress: X%
- Phase duration: X weeks
- **Phase rating**: [rating]

**Ready to enter the next phase**: [yes/no]
- [reason ready] / [items still needed]

## 7. Personalized advice

### Training advice
- [specific training advice]

### Goal-adjustment advice
- [goal-adjustment advice]

### Phase-transition advice
- [phase-transition advice]

### Cautions
- [things to watch]

## 8. Next assessment

**Next assessment date**: YYYY-MM-DD
**Assessment focus**: [items to focus on]

Brief progress report

## Recovery Progress Brief

📊 **Overall progress**: good
⏱️ **Duration**: week X (X days)
🎯 **Phase**: Phase X - [phase name]

**Functional improvement**:
- ROM: +X° (rate X°/week) ✅
- Strength: +X grades ✅
- Balance: +X% ✅

**Pain control**: avg X/10 ([trend])
**Training adherence**: X% ([rating])
**Goal attainment**: X/X (X%)

**Current phase**: X/X goals complete
**Next-phase readiness**: [yes/no]

💡 **Advice**: [1-2 core suggestions]

Data Sources

Main data file

• Path: data/rehabilitation-tracker.json
• Fields read:
• user_profile - profile and rehabilitation basics
• rehabilitation_goals - goal list
• exercise_log - training log
• functional_assessments - functional-assessment records
• phase_progression - phase-progress records
• pain_diary - pain diary
• statistics - statistics

Log data files

• Path: data/rehabilitation-logs/YYYY-MM/YYYY-MM-DD.json
• Fields read:
• daily_summary - daily training summary
• exercise_sessions - session details
• pain_entries - pain records
• assessments - assessment records
• notes - daily notes

Analysis Algorithms

1. Improvement-trend analysis

Linear regression:

Fit the functional-improvement trend with least squares
Improvement rate = (current value - baseline) / time interval

Improvement-pattern recognition:

• Linear improvement: steady, continuous improvement
• Stepwise improvement: rapid improvement after a plateau
• Plateau: improvement stalls
• Regression: function declines (needs attention)

2. Pain time-series analysis

Moving average:

7-day moving-average pain = sum(last 7 days' pain) / 7

Pain-trend judgment:

• Improving: pain score down ≥20%
• Stable: change <20%
• Worsening: pain score up ≥20%

3. Adherence calculation

Overall adherence = (actual training days / planned training days) × 100%

Per-type adherence = (type actual completed / type planned) × 100%

Adherence rating:

• Excellent: ≥90%
• Good: 75-89%
• Fair: 60-74%
• Needs improvement: <60%

4. Goal-attainment prediction

Linear extrapolation:

Predicted time = current date + ((target value - current value) / improvement rate)

Factors considered:

• Recent improvement rate
• Plateau history
• Training adherence

5. Phase-transition readiness

Readiness score:

Readiness = (phase goals achieved / total phase goals) × 100%

Readiness ≥ 80%: recommend entering the next phase
Readiness 60-79%: may consider next phase, with caution
Readiness < 60%: recommend continuing the current phase

Safety and Privacy

Data-security principles

1. Local storage

• All rehabilitation data is stored only on the user's local device
• Not uploaded to any cloud server
• Not shared with third parties

1. Privacy protection

• Personal health information is kept strictly confidential
• Data files contain no personally identifying information
• The user fully controls data access

1. Data integrity

• Raw data is not modified
• Analysis results are based on real data
• Supports data export and backup

Medical safety boundaries

What the system must NOT do:

• ❌ Does not provide specific rehabilitation-exercise prescriptions
• ❌ Does not replace a physical therapist's guidance
• ❌ Does not diagnose injuries or complications
• ❌ Does not adjust the rehabilitation-phase plan
• ❌ Does not predict recovery prognosis time
• ❌ Does not manage acute pain or injury

What the system CAN do:

• ✅ Provide data analysis and trend identification
• ✅ Provide progress tracking and goal management
• ✅ Provide general rehabilitation advice
• ✅ Provide reminders to seek professional rehabilitation care
• ✅ Record training and assessment data
• ✅ Generate recovery-progress reports

Important notes:

• All rehabilitation plans should follow a physical therapist's guidance
• Any worsening pain or functional regression warrants prompt care
• Regular professional assessment is key to rehabilitation success
• System suggestions are for reference only and do not replace professional judgment

Error Handling

Data-read errors

Type 1: file not found

Error: "Rehabilitation data file not found; first use /rehab start to begin tracking"
Suggestion: guide the user to start recording

Type 2: insufficient data

Error: "Not enough data; at least 3 functional assessments or 10 days of training records are needed"
Current data: X assessments, X days of training
Suggestion: recommend the user keep recording

Type 3: data-structure error

Error: "Data-file structure is abnormal; please check data integrity"
Suggestion: recommend reinitializing the rehabilitation profile

Analysis errors

Type: computation error

Error: "An error occurred during analysis; please try again later"
Suggestion: log the error, show basic data

Output errors

Type: report generation failed

Error: "Report generation failed; try simplifying the query or contact technical support"
Suggestion: provide a simplified report or raw-data export

Usage Examples

Example 1: view recovery progress

User input:

/rehab progress

Skill execution:

1. Read rehabilitation-tracker.json
2. Read the last 30 days of rehabilitation logs
3. Analyze functional-improvement trends
4. Compute training adherence
5. Assess goal attainment
6. Generate a progress report

Output:

# Recovery Progress Report

## Recovery-progress summary
📊 Overall progress: good
⏱️ Duration: week 6 (36 days)
🎯 Current phase: Phase 3 - strengthening

## Functional improvement
- Knee flexion: 30° → 120° (+90°) ✅
- Knee extension: -10° → 0° (+10°) ✅
- Quadriceps strength: 3/5 → 4/5 (+1 grade) ✅
- Single-leg stance: 5 s → 30 s (+25 s) ✅

## Pain control
- Average pain: 1.5/10 (well controlled)
- Pain trend: stable ✅

## Training adherence: 92% (excellent)

## Goal attainment: 8/14 (57%)
- ✅ Achieved: 8
- 🔄 In progress: 5
- ⚠️ Lagging: 1

## Phase progress
Phase 3 progress: 2/5 goals complete (40%)
Next-phase readiness: needs more work

💡 Advice: maintain current training intensity; focus on quadriceps strengthening

Example 2: analyze functional-improvement trends

User input:

/rehab trends rom

Skill execution:

1. Extract all ROM assessment records
2. Plot the ROM-improvement curve (text description)
3. Compute improvement rate
4. Predict time to target
5. Identify improvement pattern

Output:

# Range-of-Motion (ROM) Trend Analysis

## Knee flexion
📈 Improvement trend: good

Timeline:
- 5/15: 30° (baseline)
- 5/30: 70° (+40°)
- 6/15: 100° (+30°)
- 6/20: 120° (+20°)

Improvement rate: 8.6°/week
Target: 135°
Estimated completion: 2025-08-01 (about 2 more weeks)
Trend: continued improvement ✅

## Knee extension
✅ Target achieved

Timeline:
- 5/15: -10° (baseline)
- 6/10: 0° (target reached) ✅
- 6/20: 0° (maintained)

Improvement rate: 2.5°/week
Target: 0°
Achieved: 2025-06-10
Trend: maintaining target ✅

## Advice
- Knee flexion is progressing well; keep it up
- Maintain extension training to avoid flexion contracture
- Flexion target expected within 2 weeks

Example 3: assess pain patterns

User input:

/rehab analysis pain

Skill execution:

1. Extract all pain records
2. Analyze pain time-series change
3. Identify pain triggers
4. Assess pain-control effect

Output:

# Pain Pattern Analysis

## Pain trend
📉 Overall trend: improving

- Baseline pain: 5/10
- Current pain: 2/10
- Improvement: 3 points (60% improvement) ✅

## Time-series pattern
- Morning pain: 1/10 (mild)
- Post-activity pain: 2/10 (mild)
- Resting pain: 0/10 (none)

## Trigger identification
- Main triggers: prolonged sitting, stairs
- Pain-aggravating activities: descending stairs, deep squats
- Relieving factors: rest, ice, elevation

## Training-related pain
- Average pain during training: 1.2/10 (safe range)
- Post-training pain: 2/10 (resolves quickly)
- Adherence not affected by pain ✅

## Advice
- Pain well controlled; maintain current intensity
- Rest and ice after training
- Avoid pain-aggravating activities (deep squats, descending stairs)
- If pain >4/10, seek care for evaluation

Correlation Analysis

Link with the exercise module

Correlation analysis:

• Rehabilitation training vs. recovery of exercise capacity
• Training intensity vs. heart-rate change
• Functional improvement vs. daily activity level

Example:

User runs /rehab analysis correlation fitness
The skill reads:
- rehabilitation-tracker.json
- fitness-tracker.json
- analyzes the correlation between training and exercise metrics

Link with the sleep module

Correlation analysis:

• Training intensity vs. sleep quality
• Pain level vs. sleep duration
• Recovery-period sleep needs

Link with the medication module

Correlation analysis:

• Analgesic-use trend
• Medication vs. training intensity
• Pain control vs. medication adherence

Usage Scenarios

Scenario 1: new user starts rehabilitation

User: /rehab start acl-surgery 2025-05-01
System: initialize the profile, set baseline goals, provide initial advice
Skill: rehabilitation-analyzer (optional, for initial assessment)

Scenario 2: record daily training

User: /rehab exercise slr 3x15 pain2
System: record training data, update the log
Skill: not triggered (record only)

Scenario 3: view the progress report

User: /rehab progress
System: call the rehabilitation-analyzer skill
Skill: full analysis, generate a progress report

Scenario 4: analyze a specific function

User: /rehab trends rom
System: call the rehabilitation-analyzer skill
Skill: ROM-specific analysis, generate a trend report

Scenario 5: assess pain patterns

User: /rehab analysis pain
System: call the rehabilitation-analyzer skill
Skill: pain-specific analysis, identify patterns and triggers

Skill version: v1.0 Last updated: 2026-01-06 Maintainer: MedClawMini