# DaySensOS Glasses: A Zero-Retention, Privacy-First Passive Life Analysis Framework with Multi-Level Context Grammar for Smart Glasses **Fatih Dinc** ORCID: 0009-0003-0484-4286 (https: //orcid. org/0009-0003-0484-4286) *May 2026* DOI: 10. 5281/zenodo. 19960744 (https: //doi. org/10. 5281/zenodo. 19960744) --- ## Abstract DaySensOS Glasses is a novel, ISO-oriented framework for AI-enabled smart glasses that enables passive, privacy-compliant life analysis. It employs an innovative 4-level context grammar (Physical → Semantic → Temporal → Strategic) to aggregate raw sensor data into semantically meaningful insights without cloud dependency. A strict Zero-Retention Architecture ensures all raw camera, audio, and screen data is irrevocably deleted immediately after feature extraction. A granular Privacy & Consent Gate allows users to approve data streams independently and revocably. This work establishes the technical foundation and state of the art for personal, AI-powered productivity and wellbeing coaching using wearable sensors, while adhering to ISO/IEC 27001, 27701, and 29100 privacy principles. The mark "DaySensOS" is registered at the German Patent and Trade Mark Office under DPMA file number 302026224284. 7. This technical report is permanently archived under DOI 10. 5281/zenodo. 19960744. --- ## 1. Introduction Smart glasses offer an unprecedented opportunity for passive, continuous life analysis without disrupting the user's natural behavior. However, existing solutions either require active manual input, rely on cloud processing of sensitive personal data, or fail to provide semantically meaningful daily summaries that translate raw sensor readings into actionable life insights. DaySensOS Glasses addresses all three gaps simultaneously by combining on-device machine learning inference, a hierarchical 4-level context grammar, and a strict zero-retention privacy architecture. The system is designed for the Ray-Ban Meta Wayfarer Gen 2 (RW4012) and leverages the Meta Wearables Device Access Toolkit for camera and audio access. All inference runs locally on the paired smartphone, ensuring that no raw sensor data ever leaves the device. The core tension that DaySensOS resolves is the conflict between the "Quantified Self" movement's desire for comprehensive self-tracking and the fundamental right to digital sovereignty. By processing everything locally and deleting raw data immediately, DaySensOS offers deep personal insights without creating a surveillance footprint. --- ## 2. System Architecture: The 4-Level Context Grammar The central innovation of DaySensOS is a hierarchical grammar that transforms unstructured sensor streams into structured, semantically rich daily profiles. This grammar operates on four distinct levels: ### Level 1 – Physical Layer (Raw Sensor Inference) At the lowest level, the system receives camera frames from the smart glasses at configurable intervals (default: every 5 minutes), audio features from the Bluetooth microphone, and optionally screen context from the paired smartphone (with separate user consent). Two model backends process these inputs: - **MobileNetV3** for primary object and scene classification (e. g. , "desk", "diningₜable", "parkbench") - **EfficientNet-B0** (Places365-weighted) for environmental scene recognition (e. g. , "office", "restaurant", "forestₚath") The ensemble output consists of a label, a confidence score, a motion estimate, and an audio level reading. ### Level 2 – Semantic Layer (LifeOS Contexts) A rule engine maps the raw labels into one of nine semantically stable LifeOS contexts using a configurable YAML rule set (`contextₘapping. yaml`): | Context | Typical Activities | Rule Example | |---------|-------------------|--------------| | Deep Work | Focused solo work, writing, coding | `desk` + `laptop` + motion 2) | | Eating & Pause | Meals, coffee breaks | `diningₜable` OR `kitchen` + food item detected | | Movement | Walking, running, gym | `gym` OR (`parkbench` + motion > 0. 7) | | Outdoor & Nature | Park time, garden | `forestₚath` + high green percentage in frame | | Transit | Commuting, driving | `carᵢnterior` + high motion | | Household | Bathroom, bedroom, sleeping | `bedroom` + darkness detected | | Spiritual Practice | Meditation, prayer (optional) | Manual intent check-in or context-specific labels | | Leisure & Consumption | Reading, watching, socializing | `livingᵣoom` + `couch` + `tv` | ### Level 3 – Temporal Layer (Episodes & Sessions) Consecutive identical contexts are aggregated into temporal episodes with start time, end time, duration, and average sensor readings. Episodes of the same type across the day form "Sessions" (e. g. , "Deep Work Session 1: 09: 10–11: 40", "Deep Work Session 2: 14: 05–15: 30"). ### Level 4 – Strategic Layer (Daily Metrics) The final level computes a feature vector from the day's episodes, producing the metrics that feed the evening coach: - **Focus Score** (0–10): Based on total Deep Work duration, longest uninterrupted block, and fragmentation penalty - **Energy Index** (0–10): Derived from movement, meal regularity, and mood indicators - **Social Richness** (0–10): Duration and mood quality of collaborative and leisure-social episodes - **Movement Index** (0–10): Minutes in active movement and outdoor exposure - **Routine Stability** (0–10): Comparison of main episode start times against a rolling 7-day average - **Transitions Count**: Number of context switches during the day - **Outdoor Exposure**: Total minutes spent outside --- ## 3. Privacy & Design The design of DaySensOS is based on "Privacy by Design" principles (ISO 25010, ISO 29100) to ensure the user's digital sovereignty in an environment of ubiquitous sensing. The system addresses privacy through three core mechanisms: ### 3. 1 Zero-Retention Architecture Unlike cloud-based lifelogging systems that persist raw data for model improvement or profiling, DaySensOS implements a strict Zero-Retention Policy. All unstructured sensor data (camera frames, audio signals) is processed exclusively in volatile working memory (RAM). Once an analysis pipeline (inference) is complete, the raw data is immediately deleted or overwritten by subsequent data. Only the semantic metadata and trend scores extracted by the 4-level context grammar are stored long-term. No raw image, audio sample, or screen pixel is ever written to persistent storage. ### 3. 2 Consent Gate Mechanism DaySensOS is designed to return full control over sensing to the user. The Consent Gate functions as the central access control instance. At the software level, sensors are activated only when an explicit or contextually defined trigger is present. This architecture prevents the "always-on" scenario by placing sensors in a dormant state unless an analytical necessity exists. The user receives full transparency through a dashboard showing which sensors are active and which data categories are currently being processed. Consent is managed per stream (camera, audio, screen) independently and can be revoked instantly. ### 3. 3 Audit Traceability and Transparency To ensure trustworthiness and conformity with security standards (ISO 27001), DaySensOS implements Audit Traceability. The system logs the decision logic of the inference pipelines and context mapping in an append-only JSONL audit file. This file contains no sensitive raw data, but only encrypted timestamps and the system's classification decisions ("System Trace"). This enables the user to verify the history of automated decision-making without compromising privacy by storing raw information. Through this separation of analytical output and sensor data, DaySensOS sets a new standard for local, privacy-compliant personal analytics frameworks. --- ## 4. Methodical Delimitation (Prior Art Position) DaySensOS occupies a distinct, previously unclaimed space in the patent landscape: personal analytics with structured semantic enrichment, fully on-device, with legally auditable privacy architecture. ### 4. 1 Delimitation from Safety-Critical Automotive Systems EP 2 693 278 B1 (Audi AG) teaches selective redundancy in image-processing control units for functional safety (ISO 26262). The patent describes a mechanism where a second, redundant calculation is performed only for results falling into a "first group" of safety-critical actuator interventions (e. g. , emergency braking). | Feature | EP 2 693 278 B1 (Audi) | DaySensOS | |---------|------------------------|-----------| | Domain | Automotive safety (ISO 26262) | Personal Analytics / Coaching | | Actuator intervention | Yes (brake, airbag) | No (recommendations only) | | Redundancy trigger | Safety-critical result group | Confidence gate / Quality gate | | Redundancy purpose | Phantom event prevention | Metric quality improvement | | Data retention | Buffering until second calculation | Zero-Retention after extraction | | Normative reference | ISO 26262 (Safety) | ISO 25010, ISO 27001, ISO 29100 (Privacy/UX) | DaySensOS is not a safety-critical control unit within the meaning of ISO 26262 and triggers no safety-critical actuator interventions. The architecture uses no selective redundancy for preventing phantom events in safety controllers, but employs confidence and quality gates for improving data quality in a zero-retention environment. Patent infringement is excluded due to the different domain and purpose. ### 4. 2 Further Delimitations - **US 10, 528, 121 B2 (Samsung): ** Automatic wearable configuration via environmental sensors – lacks context grammar and zero-retention architecture. - **WO 2015/100215 A1 (Intel): ** Privacy framework for smart glasses (bystander protection) – lacks struct
Fatih Dinc (Fri,) studied this question.