--- license: cc-by-4.0 task_categories: - other task_ids: [] tags: - gait-analysis - biomechanics - pose-estimation - mediapipe - blazepose - wearable - digital-health - markerless - movement-intelligence - rehabilitation - locomotion - on-device-ml language: - en size_categories: - 1K Markerless smartphone gait analysis dataset — 100+ sessions, 33-landmark MediaPipe BlazePose poses, confidence-gated biomechanics, and Movement Intelligence Index (MII) scores. Aggregate statistics only in v0.1; clip-level benchmark planned for v0.2. --- # Arisole StrideIQ Gait Dataset — v0.1 Seed Release > **Paper:** *Toward an Open, Privacy-Preserving Benchmark for Markerless Gait Analysis* > **Authors:** Varun Srivastava, Arisole > **Version:** 0.1 (Seed Release — aggregate statistics, no per-clip data) > **License:** CC BY 4.0 > **DOI:** *(pending Zenodo assignment)* --- ## Dataset Summary The **Arisole StrideIQ Gait Dataset** is the first open, smartphone-native, markerless gait-analysis benchmark derived from real-world walking sessions captured entirely on consumer hardware — no wearables, no motion-capture suits, no laboratory infrastructure required. **v0.1 (this release)** publishes aggregate platform statistics from 100+ consented walking sessions captured using the Arisole StrideIQ mobile application. It establishes the data schema, biomechanical pipeline, and baseline distributions ahead of a planned v0.2 clip-level benchmark. ### Why This Matters Existing gait datasets (e.g., CASIA-B, CMU MoCap, DIRO) require laboratory hardware costing tens of thousands of dollars. StrideIQ captures equivalent biomechanical signals from a standard smartphone camera, opening gait analysis to: - Low-resource clinical settings globally - Remote rehabilitation monitoring - Robotics locomotion research (Open X-Embodiment compatible schema) - Population-scale epidemiology --- ## Dataset Structure ### v0.1 Contents (Aggregate Statistics) | Field | Description | |-------|-------------| | `session_count` | 100+ walking sessions | | `landmark_schema` | 33 MediaPipe BlazePose landmarks (x, y, z, visibility) | | `biomechanical_features` | Stride length, cadence, step symmetry, trunk sway, knee flexion angle | | `confidence_gating` | Frames with landmark visibility < 0.6 excluded | | `MII_score` | Movement Intelligence Index — composite gait health score (0–100) | | `platform_distributions` | Aggregate histograms of MII and feature distributions | ### Coming in v0.2 - Clip-level landmark sequences (with explicit participant consent) - Demographic metadata (age band, self-reported health condition category) - Per-session biomechanical time-series - Leaderboard for automated gait quality prediction --- ## Biomechanical Pipeline ``` Smartphone Video │ ▼ MediaPipe BlazePose (on-device inference) │ 33 landmarks @ 30fps ▼ Confidence Gate (visibility threshold 0.6) │ Filters occluded/low-quality frames ▼ Biomechanical Feature Extraction ├─ Stride length estimation (hip–ankle geometry) ├─ Cadence (step frequency via heel-strike detection) ├─ Step symmetry (L/R stride time ratio) ├─ Trunk sway (shoulder midpoint lateral displacement) └─ Knee flexion angle (thigh–shin vectors) │ ▼ Movement Intelligence Index (MII) Weighted composite score — normalized to 0–100 Higher = more efficient, more symmetric gait ``` **Key architectural insight (P2 companion paper):** The full biomechanical pipeline runs entirely **on-device**. The landmark payload transmitted to the cloud is ~130 KB/session vs. ~24.3 MB for the source video — a **~200× compression** with zero raw biometric video leaving the device. --- ## Usage ```python from datasets import load_dataset # Load v0.1 aggregate statistics ds = load_dataset("ctechvent/arisole-strideiq-gait") print(ds) ``` > **Note:** v0.1 contains aggregate statistics and the data schema. > Clip-level data will be added in v0.2. Subscribe to / watch this repository > to be notified when v0.2 is released. --- ## Related Resources | Resource | Link | |----------|------| | Companion Paper (P1) | *Toward an Open, Privacy-Preserving Benchmark for Markerless Gait Analysis* — Zenodo DOI (pending) | | On-Device Architecture (P2) | *The Server Doesn't Need to See You Move* — Zenodo DOI (pending) | | Global Health Context (P3) | *Movement Poverty* — Zenodo DOI (pending) | | Arisole Website | [arisole.com](https://arisole.com) | | arXiv Preprint | Pending endorsement (arXiv submission ID: 7864429) | --- ## Comparison to Existing Gait Datasets | Dataset | Capture Method | Cost | Clip-Level | Smartphone | Open License | |---------|---------------|------|-----------|-----------|-------------| | CASIA-B | Multi-camera lab | $$$$ | ✅ | ❌ | ❌ | | CMU MoCap | Optical MoCap | $$$$ | ✅ | ❌ | ✅ | | TUG Dataset | Clinical sensors | $$$ | ✅ | ❌ | Varies | | **StrideIQ (this)** | **Smartphone** | **$0** | **v0.2** | **✅** | **✅ CC-BY** | --- ## Data Collection & Privacy All sessions captured via the Arisole StrideIQ app under informed consent. v0.1 contains **aggregate statistics only** — no per-user or per-clip data is included. The raw video never leaves the participant's device (on-device inference architecture). Institutional review: *(IRB/ethics review in progress for v0.2 clip release)* --- ## Citation If you use this dataset in your research, please cite: ```bibtex @dataset{srivastava2026strideiq, title = {Arisole StrideIQ Gait Dataset (v0.1 Seed Release)}, author = {Srivastava, Varun}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/ctechvent/arisole-strideiq-gait}, version = {0.1}, license = {CC-BY-4.0} } ``` Companion paper citation: ```bibtex @article{srivastava2026openmarkerless, title = {Toward an Open, Privacy-Preserving Benchmark for Markerless Gait Analysis}, author = {Srivastava, Varun}, journal = {arXiv preprint}, year = {2026}, note = {arXiv submission ID: 7864429, pending endorsement} } ``` --- ## Maintainers **Varun Srivastava** — Founder, Arisole **Arisole / Ctech Ventures** Contact: [move@arisole.com](mailto:move@arisole.com) | [arisole.com](https://arisole.com) *Feedback, collaboration requests, and robotics/rehabilitation research partnerships welcome.*