Activity Vision

Overview

Activity Vision is a privacy-first productivity monitoring solution that uses computer vision to understand work patterns without invasive keystroke logging or continuous screen recording.

The Problem

Remote work monitoring tools face a dilemma:

• Too invasive = employee distrust
• Too lax = no actionable insights

We needed to find a balance that respects privacy while providing useful productivity data.

Technical Solution

Snapshot Analysis

Instead of recording screens, we take periodic snapshots and immediately process them locally:

class ActivityClassifier:
    def __init__(self):
        self.ocr_engine = TesseractEngine()
        self.classifier = load_model('activity_classifier.h5')

    def analyze_screenshot(self, image):
        # Extract text for app identification
        text = self.ocr_engine.extract(image)

        # Classify activity type
        features = self.extract_features(image, text)
        activity = self.classifier.predict(features)

        # Delete image immediately
        del image

        return activity, confidence

Privacy by Design

• Images are processed locally and immediately deleted
• Only activity categories are sent to the server
• No keystroke logging or clipboard monitoring
• User can pause tracking anytime

Activity Categories

The model classifies work into categories:

1. Coding: IDE usage, terminal commands
2. Communication: Email, Slack, meetings
3. Research: Documentation, web browsing
4. Design: Creative tools, presentations
5. Administrative: Spreadsheets, documents

Model Training

We trained on 50,000+ anonymized screenshots with:

• Multi-task learning (app + activity type)
• Data augmentation for screen variations
• Transfer learning from ImageNet

Edge Deployment

For privacy and latency, the model runs entirely on-device:

• Model size: 15MB (quantized)
• Inference: <100ms on CPU
• Memory: <200MB RAM

Results

• 5,000+ active users across 50 companies
• 100ms average processing time
• 94% accuracy in activity classification
• Zero privacy complaints in 2 years

Future Improvements

• Attention pattern analysis (with consent)
• Meeting engagement scoring
• Burnout prediction alerts
• Integration with project management tools