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QIF-T0089

critical

Tremor and movement neurological profiling (IMU-based motor disorder detection and health inference)

Tier 3 — Demonstrated (Lab-proven)

Legacy status: DEMONSTRATED

Smartphone IMU sensors can detect pathological tremor patterns characteristic of neurological conditions: Parkinson's resting tremor (4-6 Hz), essential tremor (8-12 Hz), cerebellar tremor (3-5 Hz), and physiological tremor changes from medication, fatigue, or substance use. The mPower study (Bot et al. 2016) enrolled 16,000 participants and demonstrated that smartphone sensor data alone can distinguish Parkinson's patients from healthy controls with >90% accuracy. Beyond tremor, fine motor control degradation (touchscreen interaction patterns, typing dynamics) reveals cognitive decline, medication effects, intoxication levels, and fatigue. This is covert neurological diagnosis without the subject's knowledge or consent — the phone becomes a continuous neurological monitor. The data reveals protected health information about neurological conditions, substance use, and cognitive function from sensors that require no permission.

Technique Details

Tactic
QIF-S.HV
Status
DEMONSTRATED
Bands
S1, S2, N1, N7

Therapeutic Application

Smartphone IMU sensors detect pathological tremor frequencies and fine motor control degradation to infer neurological conditions, medication effects, and cognitive state

Clinical Analog

Remote Parkinson's monitoring and neurological screening

Treats

  • Parkinson's disease symptom tracking (mPower study)
  • essential tremor monitoring
  • multiple sclerosis motor assessment
  • medication effect monitoring (levodopa response tracking)

Neural Impact

4 of 7 neural bands affected

S1 S2 N1 N7

Drag to rotate. Click a region to learn more.

Click or hover over a glowing region to see the attack techniques targeting it and their severity.

DSM-5-TR Diagnostic Mappings

Diagnostic category references for threat modeling, not diagnostic claims.

F45 Somatoform disorders F44.4 Conversion Disorder F20 Schizophrenia Spectrum F32 Major Depressive Disorder F90 ADHD F42 OCD F82 Developmental Coordination Disorder F30 Manic episode F43 PTSD / Trauma F80 Communication Disorders F60 Personality Disorders F63 Impulse-Control Disorders F01 Vascular dementia F98.4 Stereotyped movement disorders

Pathway: N7 (PFC/M1) → executive function; N1 (spinal cord) → reflexes

Following Poldrack (2006), brain region disruption does not uniquely predict psychiatric outcomes.

Scoring

NISS v1.1 NISS:1.1/BI:N/CR:L/CD:L/CV:I/RV:F/NP:N
CVSS v4.0 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
2.7Low
BICRCDCVRVNP
 

Governance

Neurorights at Risk

This technique threatens 3 of the 4 proposed neurorights (Ienca & Andorno, 2017).

Consent Complexity
1.35 / 4.0

FDORA §3305 Compliance

Non-Cyber Device (missing: software)
Regulatory Coverage
0.4 / 1.0
524B Requirements
TM VA SA PM
Regulatory Gaps
  • ! CVSS cannot express neural-specific impacts
  • ! 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) 2.7 2.7 Low -
Child (10yr) + ADHD 2.7 3.2 Low +0.48
Adult with ALS 2.7 3.1 Low +0.44

Validation Status

Theoretical / Not yet validated. This technique has not been independently tested. See the validation dashboard for what has been tested.

Qinnovate Neural Security Atlas Edit this on GitHub