Core Monitoring & Feedback Loop: An In-Depth Discussion

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Core Monitoring & Feedback Loop: An In-Depth Discussion

Goal: Building the Foundation for Posture Feedback

The goal of this article is to explain the fundamental functionality of monitoring user posture via webcam and providing real-time feedback through the menu bar icon. This is a crucial step in developing a posture correction application, as it establishes the complete user-facing feedback loop. This loop consumes posture data from an AI/ML detection engine and translates it into actionable user feedback. Think of it as the core value delivery mechanism for any posture-focused application, like Posely. It's about taking complex pose detection and turning it into something useful for the end-user.

To fully understand the implications, this core functionality needs to be completed before other features, such as a Progress Dashboard or Onboarding, can provide a complete user experience. This is because the real-time feedback is the foundation upon which these other features are built. Without it, the dashboard and onboarding would lack the live data they need to be effective. So, let's dive deeper into how this core feedback loop works and why it's so important.

Key Integration: The integration is a critical aspect. This stage bridges the AI/ML engine (likely running in a separate process) with the main UI processes. This means establishing communication pathways that allow the posture data to flow seamlessly from the detection engine to the user interface, where it can be displayed and acted upon. This involves setting up Inter-Process Communication (IPC) patterns, which are essential for real-time posture feedback. IPC is the backbone that allows different parts of the application to talk to each other, ensuring that the posture information is delivered promptly and accurately.

Scope: Implementing a Production-Ready System

Our scope is to implement a production-ready monitoring and feedback system. This means building something that's not just a proof of concept, but a robust and reliable system that can be used by real users. This involves several key aspects, let's break them down:

  1. Camera Permissions: The system needs to request camera permissions with clear privacy explanations on the first launch. This is crucial for building user trust. We need to be transparent about why we need camera access and how we're going to use the data. Think of it as the first impression – we want to make sure it's a good one. Explaining that the processing happens locally and that no video is stored is a great way to address privacy concerns upfront.

  2. User Baseline Calibration: Establishing a user baseline is also very important. This involves guiding the user through a calibration process to capture their ideal posture. This baseline then becomes the reference point for detecting deviations and providing feedback. It's like setting a personal benchmark – the system learns what your good posture looks like so it can tell when you're slouching. Calibrating the system is also very essential because it ensures personalized feedback, as everyone's ideal posture may be slightly different.

  3. AI/ML Detection Engine Integration: The system integrates an AI/ML detection engine to consume posture data. This is where the magic happens! The AI analyzes the video feed and provides information about the user's posture. The integration process must be seamless, ensuring that the data flows correctly from the engine to the rest of the system. This is like plugging in the brain – it needs to connect properly to receive and process information.

  4. Ambient Feedback: Ambient feedback needs to be provided through menu bar icon color changes (GREEN/YELLOW/RED). This is a subtle but effective way to give users real-time feedback without being intrusive. A quick glance at the icon tells you whether you're maintaining good posture or if you need to sit up straight. Think of it as a gentle nudge – a visual cue to help you stay aware of your posture throughout the day.

  5. Privacy Maintenance: Privacy needs to be maintained by processing all video frames ephemerally without persistence. This is a critical requirement. We need to ensure that no video data is stored, and all processing happens in real-time. This reassures users that their privacy is being taken seriously. It's like having a conversation that disappears as soon as it's over – nothing is recorded or stored.

  6. User Control: The system provides user control through pause/resume functionality and settings access. Users should be able to easily pause or resume monitoring, and access settings to adjust the system to their preferences. This gives users a sense of control and ownership. It's like having a remote control – you can turn it on or off whenever you want.

User Stories: A Deeper Dive into Functionality

Story 1.1: Setup Application & Request Permissions

Priority: Critical

Dependencies: Epic 0 (Electron app structure)

Estimated Effort: 6 hours

The first user story revolves around requesting camera permissions on the first launch with a clear privacy explanation. This is a critical step in building user trust. We need to make sure users understand why we need camera access and how their data remains private. Transparency is key here, guys. We want to assure them that their privacy is our top priority.

Acceptance Criteria:

  • On the first launch, the app presents a simple, multi-step onboarding flow that requests camera permissions. This makes the process user-friendly and easy to follow.
  • If permissions are granted, the app proceeds to the next step in the onboarding flow (camera feed display). This ensures a smooth user experience.
  • If permissions are denied, the app shows a clear message explaining why camera access is essential and provides a button to open the OS system settings. This empowers users to make an informed decision and easily adjust their settings if needed.

Technical Implementation:

  • Process Model: Permission request handled via IPC between Renderer (UI) and Main process. This separation of concerns allows for a more organized and maintainable codebase.
  • Main Process Logic: Use systemPreferences.askForMediaAccess('camera') (macOS) or equivalent Windows API. This ensures compatibility across different operating systems.
  • Renderer Process Logic: Onboarding UI component triggers IPC call when the user clicks