Generated from prompt:
Create a 10-slide GDG TechSprint-style presentation for the project 'Focus-Link'. Use a modern, clean, and Google Developer Group theme. Include the following structure: 1️⃣ **Title Slide:** Focus-Link: Real-Time Focus & Distraction Detection System — include team name (Focus), team leader (Nayandeep), and YouTube link https://youtu.be/xIsKo9hg6cM?si=PgGjl5LdnLwUmEY-. 2️⃣ **Problem Statement:** Existing productivity and learning systems lack a real-time, hardware-independent way to track human focus while simultaneously identifying visual and audio distractions. As a result, users remain unaware of when and why their attention drops at the moment it actually happens. 3️⃣ **Solution Overview:** Real-time device-based focus monitoring that tracks concentration and environment using webcam and microphone. Generates focus score (0–100%) and provides instant feedback with Gemini AI. 4️⃣ **Features & Technologies Used:** Mediapipe, YOLOv8, Sounddevice, Gemini AI, OpenCV. 5️⃣ **Architecture Diagram:** Dual AI pipeline — one for user behavior analysis (facial tracking, gaze, posture) and one for environmental detection (object and sound identification). Include fusion engine layer correlating both streams. 6️⃣ **Process Flow:** Calibration → Real-time tracking → Detection → Correlation → Feedback. 7️⃣ **Wireframes / MVP Snapshots:** Visual mockups or placeholders for system dashboard and real-time detection interface. 8️⃣ **Use of Google Technologies:** Mediapipe, Gemini AI, Google Colab for model training, and TensorFlow. 9️⃣ **Impact & Future Vision:** Include future features — sentiment analysis, multi-user tracking, mobile deployment, cloud analytics. 🔟 **Links & Credits:** GitHub, YouTube Demo, Team details (Nayandeep, Shreshth, Shreyash, Nitin). End with tagline: “Focus is the art of filtering noise — our AI just learned to master it.”
5-slide GDG TechSprint pitch for Focus-Link: real-time webcam/mic-based focus tracking (0-100% score) with distraction detection via MediaPipe, YOLOv8, Gemini AI. Covers problem, solution, architectur
This title slide, named "Focus-Link," presents a "Real-Time Focus & Distraction Detection System." The subtitle credits Team: Focus, led by Nayandeep.
Team: Focus | Leader: Nayandeep
Source: https://youtu.be/xIsKo9hg6cM?si=PgGjl5LdnLwUmEY-
Current systems lack real-time, hardware-independent focus tracking and visual/audio distraction detection. Users remain unaware of instant attention drops, as productivity tools fail to provide immediate awareness.
The slide's left column highlights key features: real-time webcam and microphone monitoring for focus and distraction tracking, a dynamic 0-100% focus score, and instant personalized Gemini AI feedback. The right column lists technologies used, including Mediapipe for facial/pose tracking, YOLOv8 for object detection, Sounddevice for audio capture, OpenCV for video processing, and Gemini AI for feedback generation.
| Key Features | Technologies Used |
|---|---|
| Real-time webcam and microphone monitoring tracks user focus and distractions. Generates dynamic focus score (0-100%). Delivers instant, personalized feedback powered by Gemini AI. | Mediapipe for facial/pose tracking, YOLOv8 for object detection, Sounddevice for audio capture, OpenCV for video processing, Gemini AI for intelligent feedback generation. |
The slide outlines a five-phase workflow for user focus monitoring: Calibration (Mediapipe baseline), Tracking (OpenCV real-time), Detection (Mediapipe/YOLOv8 distractions), Correlation (fusion engine), and Feedback (Gemini AI scores). It details descriptions and key components for each phase in a tabular format.
{ "headers": [ "Phase", "Description", "Key Components" ], "rows": [ [ "Calibration", "Establish baseline for user behavior (face/gaze/posture) and environment.", "Mediapipe, initial webcam/mic setup" ], [ "Tracking", "Real-time monitoring of user and surroundings.", "OpenCV, webcam, microphone, Sounddevice" ], [ "Detection", "Identify distractions: gaze deviation, posture changes, objects, sounds.", "Mediapipe, YOLOv8" ], [ "Correlation", "Fuse dual pipelines to correlate behavior with environmental factors.", "Fusion engine" ], [ "Feedback", "Generate focus score (0-100%) and instant alerts.", "Gemini AI" ] ] }
Source: Dual AI pipeline: User behavior (face/gaze/posture) + Environment (objects/sounds). Fusion engine. Flow: Calibration → Tracking → Detection → Correlation → Feedback.
This conclusion slide showcases Google technologies like MediaPipe, Gemini AI, Google Colab, and TensorFlow, with a subtitle on AI mastering focus. It outlines future visions such as sentiment analysis and mobile deployment, credits the team (Nayandeep as leader), and shares GitHub and YouTube links.
**Google Technologies
Future Vision
Team Focus
Connect
“Focus is the art of filtering noise — our AI just learned to master it.”
Source: Focus-Link Team | GDG TechSprint
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