QIF-T0088
highGait biometric identification (IMU-based walking pattern fingerprint for persistent tracking)
Tier 2 — Validated (Independently Replicated)
Legacy status: CONFIRMED
Every person has a unique gait signature determined by limb length ratios, joint flexibility, muscle strength distribution, and neurological motor control patterns. Smartphone IMU sensors (accelerometer + gyroscope) carried in a pocket capture this signature with high fidelity — Muaaz & Mayrhofer (2017) achieved >95% identification accuracy across 175 subjects. The gait signature is: (1) difficult to spoof (requires altering unconscious motor patterns), (2) captured passively (phone in pocket during normal walking), (3) persistent across sessions and devices, and (4) capturable without any explicit permission. Gait biometrics enable persistent user tracking even when other identifiers (cookies, device IDs, face) are unavailable. Combined with T0089 (neurological profiling), gait data also reveals health conditions affecting motor control.
Technique Details
- Tactic
- QIF-S.FP
- Status
- CONFIRMED
- Bands
- S1, S2, S3
✚ Therapeutic Application
Smartphone IMU sensors capture unique walking patterns determined by biomechanics and neurological motor control; ML models fingerprint individuals for persistent tracking
Clinical Analog
Gait analysis for rehabilitation and neurological monitoring
Treats
- Parkinson's disease gait monitoring
- fall risk assessment in elderly
- post-stroke rehabilitation tracking
- orthopedic recovery monitoring
Neural Impact
3 of 7 neural bands affected
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Click or hover over a glowing region to see the attack techniques targeting it and their severity.
Scoring
NISS:1.1/BI:N/CR:N/CD:N/CV:I/RV:F/NP:N CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:H/SI:N/SA:N Governance
Neurorights at Risk
This technique threatens 2 of the 4 proposed neurorights (Ienca & Andorno, 2017).
FDORA §3305 Compliance
- ! CVSS partially captures risk; neural dimensions missing
- ! No FDA pathway for consumer sensor exploitation
Population Vulnerability
CRB vulnerability adjustment (γ=0.30) accounts for age, diagnosis severity, consent capacity, and device dependency.
| Population | NISS Base | Adjusted | Severity | Delta |
|---|---|---|---|---|
| Adult (Default) | 2.0 | 2.0 | Low | - |
| Child (10yr) + ADHD | 2.0 | 2.4 | Low | +0.35 |
| Adult with ALS | 2.0 | 2.3 | Low | +0.32 |
Validation Status
Theoretical / Not yet validated. This technique has not been independently tested. See the validation dashboard for what has been tested.