Egocentric Household Task Data Collection
Egocentric Video Dataset Collection
Client
A physical AI / robotics-focused technology company
Objective
The objective is to collect first-person (egocentric) video data of everyday household tasks, enabling the client to train embodied and physical AI models to understand human-object interaction and task execution in real home environments.
Methodology
Respondents are recruited against defined quotas covering task type, household setup, and demographic profile, to ensure a representative spread of real-world home environments.
Data is captured on the respondent's own smartphone, restricted to devices of iPhone 11 or newer, to maintain a consistent baseline for camera and sensor quality across all submissions.
Participants follow a structured, task-based capture protocol — performing defined household activities (such as cooking, cleaning, or laundry) while recording in egocentric (first-person) view.
Submissions are reviewed against task-completion and quality criteria before being accepted into the delivered dataset.
Key Learnings
Standardizing on a minimum device specification (iPhone 11+) was essential to keeping footage quality consistent across a distributed respondent base.
Task- and quota-driven recruitment avoided over-indexing on a narrow set of household profiles, giving the dataset broader behavioural coverage.
Running this as an ongoing, continuous programme rather than a one-off batch required sustained field operations coordination for household access, scheduling, and respondent management.
Training embodied or physical AI models on real-world household tasks? Market Xcel runs ongoing, quota-driven egocentric data collection at scale — talk to us about extending or launching your programme.
