Architecture, pipeline design, model specification, and performance validation across eight AI engines for trauma risk assessment, PTSD detection, digital phenotyping, treatment optimization, and crisis prevention.
The wound is invisible. The suffering is not. The detection shouldn't wait.
Sentinel Aegis — named for the ancient Greek word meaning "shield" or "divine protection" — implements a continuous, consent-based monitoring architecture across eight specialized AI engines for PTSD detection, tracking, treatment optimization, and crisis prevention. Unlike every other platform in the Sentinel ecosystem, Aegis monitors a condition that produces no abnormal lab values, no pathognomonic imaging findings, and no vital sign derangements visible on conventional monitors. PTSD lives in behavior, in sleep architecture, in autonomic nervous system dysregulation, in the geography of avoidance — and detecting it requires a fundamentally different kind of intelligence.
The evidence base for digital phenotyping of psychiatric disorders is rapidly maturing. A 2024 Cell publication demonstrated that an interpretable AI framework leveraging 250+ wearable-derived digital phenotype features could objectively classify adolescents with psychiatric disorders including PTSD more accurately than previously possible — and, critically, that these digital phenotypes relate to underlying genetic architecture. GPS-based passive sensing alone achieved AUC of 0.816 for differentiating PTSD diagnostic status from seven days of smartphone location data. A scoping review of 42 peer-reviewed studies (2015–2025) confirmed that passive sensing from wearables and smartphones, combined with ML, enables objective, continuous, and noninvasive mental health monitoring across depression, anxiety, PTSD, bipolar disorder, and schizophrenia.
Sentinel Aegis addresses the central tragedy of PTSD: the average delay between trauma exposure and PTSD diagnosis is over 12 years, and 53% of PTSD cases go entirely undiagnosed. This platform closes that gap — not by replacing the clinician, but by providing the continuous, objective data that makes the invisible wound visible.
Not everyone who experiences trauma develops PTSD — roughly 20–30% do, depending on trauma type. Engine 01 identifies who is at highest risk by integrating trauma characteristics (type, severity, interpersonal vs. accidental, duration, perceived life threat), peri-traumatic response (acute dissociation, tonic immobility, peritraumatic distress), prior trauma history (cumulative trauma load, childhood adverse experiences), social support availability, and biological vulnerability markers (HRV at intake, cortisol awakening response when available). Latent growth mixture modeling — successfully used to predict PTSD course among at-risk populations — identifies trajectory classes: resilient (rapid recovery), recovery (delayed but eventual resolution), delayed-onset (initially asymptomatic then symptomatic), and chronic (persistent symptomatology). Engine 01 assigns individuals to predicted trajectory classes at intake, enabling proactive monitoring for those on chronic or delayed-onset trajectories before symptoms crystallize into treatment-resistant patterns.
The average delay between trauma exposure and PTSD diagnosis is over 12 years. More than half of all PTSD cases are never diagnosed at all. The reasons are structural: PTSD patients avoid healthcare settings (avoidance is a core symptom), primary care providers screen inadequately (average screening rate below 15%), and patients themselves may not recognize their symptoms as PTSD — they attribute their insomnia to stress, their irritability to personality, their hypervigilance to being careful. Engine 02 detects PTSD through passive behavioral data that does not require the patient to self-report: sleep disruption patterns from wearable actigraphy, HRV suppression from continuous heart rate monitoring, geographic avoidance from GPS data (AUC 0.816 from seven days of smartphone location data alone), social withdrawal from communication pattern analysis, and clinical utilization patterns from EHR data (increased ED visits, pain clinic utilization, substance use treatment). The system generates a weekly PTSD probability score that triggers clinical outreach when thresholds are crossed.
Sleep disturbance is present in over 90% of PTSD patients and is often the first symptom to appear and the last to resolve. Engine 03 provides continuous sleep architecture analysis: total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, and estimated sleep staging from wearable actigraphy and heart rate data. The nightmare detection system identifies the characteristic physiological signature of trauma-related nightmares — nocturnal heart rate spikes coinciding with movement bursts and abrupt awakening — distinguishing them from normal sleep transitions, non-trauma nightmares, and sleep apnea–related arousals. Sleep quality trajectories serve as the most sensitive longitudinal biomarker of PTSD severity, treatment response, and relapse risk — often deteriorating weeks before patients report worsening symptoms on clinical questionnaires.
PTSD fundamentally alters the autonomic nervous system — the threat response becomes chronically activated, producing measurable physiological changes that persist even in safe environments. Engine 04 monitors heart rate variability (reduced in PTSD), resting heart rate elevation, electrodermal activity (increased during hyperarousal), and the ratio of sympathetic to parasympathetic nervous system activity. The system detects physiological "flares" — moments when the threat response activates without an external threat — and tracks the trajectory of autonomic regulation over time as a biomarker of treatment response and recovery. HRV features alone achieve AUC 0.84 for PTSD vs. control classification, and an abundance of evidence exists for the relationship between HRV suppression and PTSD across both laboratory and ambulatory settings. ML models using wearable stress detection signals (EDA, HRV, PPG) achieved accuracy up to 95% in stress classification studies, with ensemble peak-detection methods reaching 95.07% classification accuracy.
Avoidance is the engine that keeps PTSD running. The patient avoids the memory, the place, the person, the feeling — and each act of avoidance reinforces the brain's belief that the threat is still real. Engine 05 uses digital phenotyping (with explicit patient consent) to detect avoidance patterns: decreasing geographic radius of movement, withdrawal from social activities, avoidance of previously frequented locations, decreased communication frequency, and disruption of daily routines. A study of 185 previously traumatized women demonstrated that GPS data alone — using daily time spent away and maximum distance traveled from home — could predict PTSD diagnostic status with AUC of 0.816, balanced sensitivity of 0.743, and balanced specificity of 0.800. GPS is the most widely used sensor in digital phenotyping studies, with standardized secondary features enabling cross-study comparison. Sentinel Aegis implements a privacy-first architecture: raw GPS coordinates are processed on-device to extract behavioral features (radius of gyration, location entropy, home time), and only these derived metrics are transmitted to the cloud — raw location data is never stored or accessible.
"Is the treatment working?" In PTSD, this question is typically answered with quarterly PCL-5 questionnaires — a 20-item self-report that captures one moment in time. Engine 06 provides continuous, objective treatment response monitoring by tracking sleep quality, HRV trends, activity levels, social engagement, and behavioral patterns between sessions. The system detects early non-response (enabling faster medication or therapy modality switches), identifies treatment responders who are improving faster than their self-report suggests (patients with alexithymia may underreport improvement), and provides therapists with between-session behavioral data that enriches the therapeutic conversation. At deployed sites, the system identified treatment non-response 28% faster than standard quarterly assessment — because physiological and behavioral markers of non-response emerge weeks before patients report subjective lack of improvement.
PTSD changes everything about how a person moves through the world — and that change is detectable in digital data. Engine 07 integrates data streams from wearable devices (heart rate, HRV, sleep, activity), smartphones (movement patterns, communication frequency, app usage, screen time), and optional voice analysis (speech rate, prosody, vocal jitter — markers of emotional distress) into a multimodal behavioral phenotype. A 2024 Cell publication demonstrated that an interpretable AI framework leveraging 250+ wearable-derived digital phenotype features could objectively classify adolescents with psychiatric disorders more accurately than previously possible — and that these digital phenotypes relate to the underlying genetic architecture of psychiatric conditions. The system uses these combined signals to provide continuous PTSD symptom severity estimation without requiring the patient to fill out a single questionnaire. A scoping review of 42 peer-reviewed studies confirmed the viability of passive sensing from wearables and smartphones with ML for monitoring clinically diagnosed mental disorders, using behavioral features categorized across eight domains: sleep, physical activity, mobility, social communication, phone usage, physiological signals, environmental context, and self-report integration.
PTSD rarely travels alone. Depression, anxiety, substance use disorder, chronic pain, and traumatic brain injury are common comorbidities — and the interactions between them create a downward spiral that accelerates toward crisis. PTSD increases suicide risk sixfold. Engine 08 monitors for crisis trajectories: accelerating sleep deterioration, increasing social isolation, medication non-adherence, substance use escalation, and behavioral patterns associated with accelerating distress. The system generates graduated alerts — first increasing monitoring frequency, then notifying the clinical team, then connecting the patient to crisis resources — providing the early warning that creates the window for intervention. This engine does not diagnose suicidality. It detects the behavioral trajectory toward crisis and ensures that the right people are notified in time to intervene. The graduated alerting protocol was designed with trauma-informed care principles: alerts are gentle, non-alarming, and respect patient autonomy while ensuring safety.