PHYSICAL AI SOLUTIONS

Egocentric Video Data Collection for AI Training

Global embodied AI data operations partner with Asia-led scale, industrial capture capability, teleoperation readiness, multilingual workforce, and QA-controlled delivery

Wearable Capture

First-person perspective data

Real-World Data

Indoor & outdoor environments

Scalable Collection

Large-scale data programs

Quality Assured

Multi-step validation & checks

End-to-End Egocentric AI Pipeline

From real-world data capture to deployment-ready robotic intelligence

1

Egocentric Capture

First-person visual data is collected using wearable RGB-D cameras in homes, warehouses, and lab-like settings to capture real human behavior.

Devices: Wearables Output: Raw multi-modal video
2

Video QA & Cleaning

Captured footage is reviewed for blur, motion artifacts, missing segments, corrupted frames, and unusable interactions before entering annotation.

Process: QA filtering Goal: High-quality training data
3

Annotation

Frames are enriched with object labels, task boundaries, hand pose landmarks, action tags, and scene semantics required for downstream model learning.

Labels: Objects + actions Signals: Pose + semantics
4

Teleoperation Data

Operators control robotic systems to perform demonstrations that pair human intent with robot actions, creating strong imitation learning examples.

Mode: Human-guided robot control Use: Demonstration learning
5

Dataset Packaging

Cleaned sensory streams and action traces are synchronized, normalized, and packaged into training-ready state-action sequences for model pipelines.

Format: Structured sequences Ready for: Training + evaluation
6

Sim-to-Real Support

Real-world observations are aligned with simulation environments to reduce domain gaps and improve transfer from virtual learning to physical execution.

Bridge: Simulation ↔ reality Benefit: Better deployment transfer
7

Robot Evaluation & HITL

Trained systems are tested in live environments with human-in-the-loop review, failure analysis, and iterative feedback for safer and more reliable behavior.

Validation: Real-world performance Loop: Human feedback refinement

Sensorized Physical AI Pipeline

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Human Interaction

Real-world manipulation, motion, and task execution performed by a person in natural environments to generate grounded behavioral data.

Input Source: Human activity Captured Signal: Motion + intent
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RGB + Depth Camera

Egocentric visual streams are captured with scene depth, object distance, and spatial geometry for richer environmental understanding.

Modalities: RGB + depth Output: Video + scene geometry
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IMU Motion Tracking

Orientation, acceleration, and temporal movement patterns are captured to estimate body dynamics and continuous motion trajectories.

Sensor Type: IMU Signals: Acceleration + rotation
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Gripper State Capture

Grasp force, open-close state, contact behavior, and manipulation events are recorded to align hand intent with robotic actuation.

Tracked State: Grasp actions Use Case: Manipulation learning
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MediaPipe 21-Point Hand Tracking

Fine-grained hand skeleton landmarks and finger articulation are extracted for dexterous action modeling and pose-aware learning tasks.

Landmarks: 21 hand points Focus: Finger pose estimation
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Edge + Cloud Sync

Multi-modal streams are synchronized, buffered, and transferred from edge devices to cloud pipelines for storage, processing, and orchestration.

Architecture: Edge to cloud Purpose: Sync + processing
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Physical AI Training

Unified sensor, vision, and action streams are transformed into state-action datasets used to train robotics and embodied intelligence systems.

Output: Training-ready dataset Target: Robotics + embodied AI
OVERVIEW

Transforming Real-World Experiences into AI-Ready Data

Egocentric data collection captures human interactions from a first-person perspective, enabling AI systems to better understand real-world environments. Our solutions focus on collecting high-quality, contextual datasets that improve the performance of machine learning models across industries.

From wearable camera deployments to structured data delivery, we ensure every dataset is accurate, scalable, and optimized for AI training.

100K+

Data Points Collected

95%

Annotation Accuracy

50+

Projects Delivered

What You Get

  • First-person video datasets
  • High-quality annotations
  • Structured & AI-ready formats
  • Scalable data pipelines
  • Secure & compliant delivery
WHAT WE DO

End-to-End Egocentric Data Collection Solutions

We provide scalable, high-quality first-person data collection and processing services designed to power AI, robotics, and immersive technologies.

Data Collection

First-person video capture using wearable devices across real-world environments.

Annotation & Labeling

Detailed annotation including object detection, tracking, and activity recognition.

Data Processing

Cleaning, structuring, and preparing datasets for machine learning pipelines.

Quality Assurance

Multi-layer validation ensuring high accuracy and consistency in datasets.

KEY FEATURES

Powering AI with High-Quality Egocentric Data

Our egocentric data collection solutions are designed to deliver scalable, accurate, and real-world datasets for next-generation AI applications.

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Wearable Data Capture

Capture first-person video using body-mounted cameras across real-world scenarios.

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Real-World Environments

Indoor, outdoor, industrial, and dynamic environments for diverse datasets.

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Custom Data Pipelines

Tailored workflows for collection, annotation, validation, and delivery.

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Scalable Operations

Large-scale data collection programs with distributed teams.

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AI-Ready Datasets

Clean, structured, and validated datasets optimized for training models.

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Quality Assurance

Multi-level QA processes ensuring accuracy and consistency.

CAPABILITIES

End-to-End Egocentric Data Capabilities

From data capture to delivery, we provide complete pipelines tailored for AI, robotics, and immersive applications.

Data Collection

  • Wearable camera deployment
  • First-person video capture
  • Indoor & outdoor environments
  • Multi-location data sourcing

Annotation & Labeling

  • Object detection & tracking
  • Human activity recognition
  • Frame-by-frame annotation
  • Custom labeling workflows

Data Processing

  • Data cleaning & filtering
  • Frame extraction & structuring
  • Metadata generation
  • Format standardization

Quality Assurance

  • Multi-level QA checks
  • Accuracy validation
  • Human-in-the-loop review
  • Error correction workflows

Scalable Operations

  • Distributed workforce
  • Large-scale data pipelines
  • Rapid project scaling
  • Global data coverage

Delivery & Integration

  • Custom dataset formats
  • API & cloud delivery
  • Integration with ML pipelines
  • Secure data handling
USE CASES

Real-World Applications of Egocentric Data

Powering AI models across industries with high-quality first-person datasets.

Robotics Training

Train robots using real human actions and object interactions.

Autonomous Systems

Improve perception models with real-world navigation data.

AR/VR Applications

Enhance immersive experiences with first-person datasets.

Human Activity Recognition

Detect and classify real-world human actions and behaviors.

Retail & Shopping Analytics

Understand customer behavior using first-person interaction data.

Healthcare & Training

Capture real procedures for training and AI-assisted diagnostics.

REAL-WORLD ACTIVITIES

Capturing Real Human Actions in Natural Environments

We collect egocentric data across diverse real-world scenarios to train AI systems with authentic human interactions.

Kitchen Activities

  • Cooking & food preparation
  • Cutting vegetables
  • Washing dishes

Household Tasks

  • Cleaning & organizing
  • Laundry & ironing
  • Daily home activities

Workplace Scenarios

  • Office desk interactions
  • Tool handling
  • Industrial workflows

Outdoor Activities

  • Walking & navigation
  • Shopping interactions
  • Public environment capture
CASE STUDY

How We Helped Train AI with Egocentric Data

Delivering scalable, high-quality first-person datasets for real-world AI applications.

Client Challenge

A leading AI company needed large-scale egocentric video datasets to train models for human activity recognition and robotics applications. The challenge was collecting diverse, real-world data across multiple environments while maintaining high accuracy and consistency.

Our Solution

We deployed wearable camera setups across multiple locations, capturing first-person interactions in indoor and outdoor environments. Our team handled end-to-end workflows including data collection, annotation, validation, and structured delivery.

Results

  • 100,000+ annotated video frames delivered
  • 95%+ annotation accuracy achieved
  • Reduced model training time by 40%
  • Improved AI performance in real-world scenarios
WHY CHOOSE US

Your Trusted Partner for Egocentric Data Collection

We combine scale, quality, and domain expertise to deliver AI-ready datasets that power real-world applications.

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End-to-End Solutions

From data collection to annotation and delivery, we handle the complete pipeline.

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High-Quality Data

Multi-level QA processes ensure accuracy, consistency, and reliability.

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Scalable Operations

We support large-scale projects with distributed teams and rapid execution.

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Real-World Expertise

Experience in collecting data across diverse environments and industries.

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Custom Workflows

Tailored solutions designed to meet specific AI and ML requirements.

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Secure & Compliant

Data privacy, security, and compliance standards maintained throughout.

Ready to Build High-Quality AI Datasets?

Partner with us to collect scalable, real-world egocentric data tailored to your AI and machine learning needs.