A GlucoGlance White Paper
Integrating Wearable Sensor Fusion to Resolve Non-Insulin Glucose Drivers
Executive Summary
The primary challenge in automated insulin management is the “Grey Area”, glucose fluctuations that do not correlate with Insulin On Board (IOB) or Carbohydrate On Board (COB). This paper proposes a methodology for identifying and quantifying these “ghost” drivers by establishing a mathematical Variance (Δ) field. By fusing real-time heart rate (BPM) and step cadence with a user-defined basal blueprint, we can isolate metabolic acceleration and infusion site failure with quantifiable signal classification.
1. The Problem: The Static Insulin Model
Standard insulin therapy relies on static parameters (CF, CR, Basal). However, exercise and physiological stress introduce variables that alter insulin sensitivity dynamically. Without real-time metabolic data, these shifts are often misidentified as insulin-delivery failures or carb-count errors, leading to “stacking” and hypoglycemia.
2. The Proposed Solution: The Variance Field (Δ)
Our algorithm moves from a “Detection” model to a “Deviation” model. By establishing a 24-hour Basal Blueprint (Theoretical Floor), the system calculates an Expected Glucose State.
The Fundamental Equation:
- Any non-zero Variance must be attributed to a physical cause.
- Negative Variance + High BPM/Cadence: Identified as Metabolic Draw (Exercise-induced insulin sensitivity).
- Positive Variance + Low BPM: Identified as Absorption Resistance (Potential site failure or occlusion).
3. Data Acquisition & Methodology
The system utilizes a “Silent Data Stream” to minimize user burden:Metabolic Tachometer: Real-time HR polling via Health Services API (Android/WearOS) to measure absolute heart rate frequency.Relative Intensity: Comparison of current BPM to Resting Heart Rate (RHR) from Health Connect, establishing an intensity coefficient without requiring age/weight data.Physical Verification: Step Cadence (Steps per Minute) verifies if heart rate spikes are mechanical (exercise) or psychological (stress/caffeine).
4. Reverse-Carb Attribution
To resolve the “Food Ghost” variable without dual-entry, the algorithm employs a Reverse-Carb Assumption. When a significant IOB increase is detected without a corresponding correction-need, the system back-calculates the dose into Estimated Carbs using the user’s established Carb Ratio (CR).
5. Clinical Application: Site Efficiency Scoring
By utilizing the Pod-State Transition (Disconnected > Active) as a trigger, the system maps the Variance to specific infusion sites. Over time, this identifies physiological “Site Fatigue,” allowing clinicians to recommend more efficient rotation patterns based on quantitative absorption data rather than anecdotal reporting.
Conclusion
By treating glucose management as a sensor-fusion problem rather than a simple insulin-tracking task, we resolve the “Grey Area” of glycemic control. This model provides the user with an “Insurance Policy” against site failure while providing exercise physiologists with the data needed to weight insulin decay against metabolic load.