Motion Analysis App Development
Budget: £250 – £750 GBP
KI Data Capture & Interpretation iPad App — Developer Collaboration Brief
1) Overview
I’m building an iPad app for Kinetic Intelligence® (KI) that captures movement performance (video + sensor data) and converts it into simple, actionable KI scores and coaching decisions in real time.
This is not a generic “movement app.” It’s a pitch-side performance and rehab decision tool designed for:
• Football clubs (primary)
• Education/training bodies (secondary)
• Performance/rehab practitioners
I (Kevin Pratt) will collaborate closely as the KI methodology owner: defining metrics, interpreting outputs, designing test protocols, and validating the coaching recommendations.
⸻
2) Project Goals (What “success” looks like)
V1 Launch Goal: a polished iPad app that can be used pitch-side to:
• Record video of an athlete performing specific tests
• Overlay pose tracking live/on replay
• Auto-segment reps (or provide easy manual trimming)
• Compute and display KI metrics with confidence
• Generate a “KI Snapshot” screen that outputs:
• Scores (MI / LI / DI)
• 3 core KI metrics with trends
• One stoplight risk flag
• One action plan (cue + constraint + drill + progression rule)
• Export session summaries (PDF + annotated video clip)
Primary objective: immediate coaching decisions, not data overload.
⸻
3) Target Platform & Tech Preferences
Platform: iPadOS (iPad-first experience)
Preferred stack (open to discussion):
• Swift / SwiftUI
• AVFoundation (video capture)
• Apple Vision pose estimation (2D; 3D where supported)
• Core Motion (IMU signals for smoothness/intensity proxies)
• Optional later: ARKit/RealityKit for LiDAR devices
Constraints:
• Offline-first (works on pitch, sync later optional)
• Fast: near real-time scoring/feedback
• Privacy: data stored locally by default; cloud sync can be phased in
⸻
4) Core User Workflows (Must-have)
A) Create session
• Select athlete (or quick-add)
• Choose session template (Decel / COD / Sprint / Landing-Reaccel / RTP Bridge)
• Select intent (e.g., Soft / Fast / Stable / Powerful / Reactive)
B) Capture rep(s)
• Record video
• Show skeleton overlay
• Auto detect rep start/stop (or coach-friendly manual trim)
• Tag key context quickly (surface type, fatigue, pain, RPE)
C) Review + Interpret
• Replay with overlay + key event markers
• Display KI Snapshot
• Show “Why” (top 3 drivers) and confidence score
• Provide action plan (cue + constraint + drill + progression rule)
D) Track
• Athlete timeline with trends over time (LAT)
• Compare to baseline / previous sessions
• Flag regressions and plateaus
E) Export
• PDF summary
• Shareable annotated clip (overlay + headline scores)
⸻
5) V1 Metrics (Launch Set)
App must support metric computation + explanation with confidence scores:
1. Movement Optionality Index™ (MOI)
“How many usable movement solutions can the athlete access under constraint?”
2. Intent–Output Coherence™ (IOC)
“Did movement output match stated intent?”
3. Adaptive Capacity Ratio™ (ACR)
“How well is quality maintained as task difficulty increases?”
4. Stability–Variability Balance™ (SVB)
“Is the athlete stable enough but not rigid; variable enough but not chaotic?”
5. Longitudinal Adaptation Trajectory™ (LAT)
“Are scores moving in the right direction over time?”
Important: I will provide definitions, scoring rules, thresholds, and test protocols. The developer builds the pipeline, data structures, and UI to compute/display these reliably.
⸻
6) “KI Snapshot” Screen (Signature Feature)
One screen that sells the system:
• MI / LI / DI scores (large, simple)
• MOI, IOC, ACR (with trend arrows)
• One stoplight risk indicator (green/amber/red)
• Action Plan auto-generated:
• 1 cue
• 1 constraint
• 1 drill
• 1 progression rule
• “Show me why” expands to top drivers and annotated replay
⸻
7) Functional Requirements
Capture
• Stable video recording with good UX
• Frame-accurate timestamping
• Save raw video + processed features
Pose pipeline
• Joint tracking overlay (replay + live if possible)
• Confidence per joint / per rep
• Smoothing + dropout handling
Rep segmentation
• Auto detection (v1 can be heuristic + manual override)
• Coach-friendly trimming and labeling
Metric engine
• Modular computation per test type
• Outputs: score 0–100 + confidence + drivers
• Ability to update metric definitions without rewriting the whole app
Data model
• Athlete profile
• Baselines
• Sessions → tests → reps → features → metric results
• Versioning (so metric updates don’t break historical data)
Export
• Clean PDF report generation
• Export clip with overlay + headline metrics (at least basic)
⸻
8) Non-Functional Requirements
• Fast UI, minimal taps
• Robust offline behavior
• Data integrity + backups
• Clear error states (“tracking lost”, “confidence low”)
• Logging/telemetry (local debug logs at minimum)
⸻
9) Collaboration Expectations
I will provide:
• KI methodology, definitions, and scoring logic
• Test protocols and success criteria
• Interpretation language and action plan rules
• Pilot feedback from practitioners
Developer/studio provides:
• iPad app engineering (capture, pose, pipelines, UI)
• Technical decisions + architecture
• Build iterations and QA
• TestFlight builds and release support
Working rhythm:
• Weekly sprint planning + review
• Rapid iterations with me testing in the field
• Shared backlog (Notion/Jira/Trello)
⸻
10) Deliverables
Phase 1 (MVP Prototype)
• Capture + pose overlay + rep trim
• Manual tagging
• MOI + IOC computed for 1–2 session templates
• Basic KI Snapshot
Phase 2 (Launch v1.0)
• Full launch metric set (5)
• 5 football-first session templates
• Export PDF + annotated clip
• Athlete profiles + trends (LAT)
• Polished UI/UX
Phase 3 (Post-launch enhancements)
• ARKit/LiDAR depth (where supported)
• Core ML classification for movement types/quality tiers
• Cloud sync + team dashboards
⸻
11) What I Need From You (Developer Response)
Please include:
• Recommended architecture + stack
• Timeline estimate by phase
• Team composition (iOS, ML/CV, designer)
• Relevant prior work (video, computer vision, sports/health)
• Risks/constraints you foresee
• Ballpark cost range per phase (if applicable)
⸻
12) IP, Data, and Commercial Notes
• KI metrics and naming are proprietary to Kinetic Intelligence®
• Developer code can be negotiated (work-for-hire preferred)
• Athlete data is sensitive; privacy-by-design is essential
• Any third-party SDKs must be disclosed and approved
1) Overview
I’m building an iPad app for Kinetic Intelligence® (KI) that captures movement performance (video + sensor data) and converts it into simple, actionable KI scores and coaching decisions in real time.
This is not a generic “movement app.” It’s a pitch-side performance and rehab decision tool designed for:
• Football clubs (primary)
• Education/training bodies (secondary)
• Performance/rehab practitioners
I (Kevin Pratt) will collaborate closely as the KI methodology owner: defining metrics, interpreting outputs, designing test protocols, and validating the coaching recommendations.
⸻
2) Project Goals (What “success” looks like)
V1 Launch Goal: a polished iPad app that can be used pitch-side to:
• Record video of an athlete performing specific tests
• Overlay pose tracking live/on replay
• Auto-segment reps (or provide easy manual trimming)
• Compute and display KI metrics with confidence
• Generate a “KI Snapshot” screen that outputs:
• Scores (MI / LI / DI)
• 3 core KI metrics with trends
• One stoplight risk flag
• One action plan (cue + constraint + drill + progression rule)
• Export session summaries (PDF + annotated video clip)
Primary objective: immediate coaching decisions, not data overload.
⸻
3) Target Platform & Tech Preferences
Platform: iPadOS (iPad-first experience)
Preferred stack (open to discussion):
• Swift / SwiftUI
• AVFoundation (video capture)
• Apple Vision pose estimation (2D; 3D where supported)
• Core Motion (IMU signals for smoothness/intensity proxies)
• Optional later: ARKit/RealityKit for LiDAR devices
Constraints:
• Offline-first (works on pitch, sync later optional)
• Fast: near real-time scoring/feedback
• Privacy: data stored locally by default; cloud sync can be phased in
⸻
4) Core User Workflows (Must-have)
A) Create session
• Select athlete (or quick-add)
• Choose session template (Decel / COD / Sprint / Landing-Reaccel / RTP Bridge)
• Select intent (e.g., Soft / Fast / Stable / Powerful / Reactive)
B) Capture rep(s)
• Record video
• Show skeleton overlay
• Auto detect rep start/stop (or coach-friendly manual trim)
• Tag key context quickly (surface type, fatigue, pain, RPE)
C) Review + Interpret
• Replay with overlay + key event markers
• Display KI Snapshot
• Show “Why” (top 3 drivers) and confidence score
• Provide action plan (cue + constraint + drill + progression rule)
D) Track
• Athlete timeline with trends over time (LAT)
• Compare to baseline / previous sessions
• Flag regressions and plateaus
E) Export
• PDF summary
• Shareable annotated clip (overlay + headline scores)
⸻
5) V1 Metrics (Launch Set)
App must support metric computation + explanation with confidence scores:
1. Movement Optionality Index™ (MOI)
“How many usable movement solutions can the athlete access under constraint?”
2. Intent–Output Coherence™ (IOC)
“Did movement output match stated intent?”
3. Adaptive Capacity Ratio™ (ACR)
“How well is quality maintained as task difficulty increases?”
4. Stability–Variability Balance™ (SVB)
“Is the athlete stable enough but not rigid; variable enough but not chaotic?”
5. Longitudinal Adaptation Trajectory™ (LAT)
“Are scores moving in the right direction over time?”
Important: I will provide definitions, scoring rules, thresholds, and test protocols. The developer builds the pipeline, data structures, and UI to compute/display these reliably.
⸻
6) “KI Snapshot” Screen (Signature Feature)
One screen that sells the system:
• MI / LI / DI scores (large, simple)
• MOI, IOC, ACR (with trend arrows)
• One stoplight risk indicator (green/amber/red)
• Action Plan auto-generated:
• 1 cue
• 1 constraint
• 1 drill
• 1 progression rule
• “Show me why” expands to top drivers and annotated replay
⸻
7) Functional Requirements
Capture
• Stable video recording with good UX
• Frame-accurate timestamping
• Save raw video + processed features
Pose pipeline
• Joint tracking overlay (replay + live if possible)
• Confidence per joint / per rep
• Smoothing + dropout handling
Rep segmentation
• Auto detection (v1 can be heuristic + manual override)
• Coach-friendly trimming and labeling
Metric engine
• Modular computation per test type
• Outputs: score 0–100 + confidence + drivers
• Ability to update metric definitions without rewriting the whole app
Data model
• Athlete profile
• Baselines
• Sessions → tests → reps → features → metric results
• Versioning (so metric updates don’t break historical data)
Export
• Clean PDF report generation
• Export clip with overlay + headline metrics (at least basic)
⸻
8) Non-Functional Requirements
• Fast UI, minimal taps
• Robust offline behavior
• Data integrity + backups
• Clear error states (“tracking lost”, “confidence low”)
• Logging/telemetry (local debug logs at minimum)
⸻
9) Collaboration Expectations
I will provide:
• KI methodology, definitions, and scoring logic
• Test protocols and success criteria
• Interpretation language and action plan rules
• Pilot feedback from practitioners
Developer/studio provides:
• iPad app engineering (capture, pose, pipelines, UI)
• Technical decisions + architecture
• Build iterations and QA
• TestFlight builds and release support
Working rhythm:
• Weekly sprint planning + review
• Rapid iterations with me testing in the field
• Shared backlog (Notion/Jira/Trello)
⸻
10) Deliverables
Phase 1 (MVP Prototype)
• Capture + pose overlay + rep trim
• Manual tagging
• MOI + IOC computed for 1–2 session templates
• Basic KI Snapshot
Phase 2 (Launch v1.0)
• Full launch metric set (5)
• 5 football-first session templates
• Export PDF + annotated clip
• Athlete profiles + trends (LAT)
• Polished UI/UX
Phase 3 (Post-launch enhancements)
• ARKit/LiDAR depth (where supported)
• Core ML classification for movement types/quality tiers
• Cloud sync + team dashboards
⸻
11) What I Need From You (Developer Response)
Please include:
• Recommended architecture + stack
• Timeline estimate by phase
• Team composition (iOS, ML/CV, designer)
• Relevant prior work (video, computer vision, sports/health)
• Risks/constraints you foresee
• Ballpark cost range per phase (if applicable)
⸻
12) IP, Data, and Commercial Notes
• KI metrics and naming are proprietary to Kinetic Intelligence®
• Developer code can be negotiated (work-for-hire preferred)
• Athlete data is sensitive; privacy-by-design is essential
• Any third-party SDKs must be disclosed and approved