Person Home Activity Dataset

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Project Overview:

Objective

To build a dataset of person home activity data that captures daily activities and behaviors of individuals within their homes. This dataset can be used for various purposes, such as developing smart home applications, improving healthcare monitoring, or conducting behavioral research.

Scope

Collect detailed and diverse data on individuals’ activities, routines, and environmental conditions within their homes while ensuring privacy and data security.

Sources

IoT Devices: Deploy a variety of Internet of Things (IoT) devices, such as smart sensors, cameras, and wearables, within participants’ homes to capture data.
Surveys and Self-Reporting: Participants can self-report their activities through surveys, mobile apps, or voice assistants, providing additional context.

Data Collection Metrics

Number of Participants: 100 participants (for example)
Data Collection Period: 3 months (for example)

Annotation Process

Stages

Motion Data: Collect data on motion and movement within the home using motion sensors or cameras.
Audio Data: Capture ambient audio to detect conversations and sounds.
Environmental Data: Measure factors like temperature, humidity, and lighting conditions.
Device Usage: Monitor the usage of home appliances and devices.
Location Data: Track the location of participants within the home using localization technologies.
Health Data: If relevant, collect health-related data using wearables or health monitoring devices.

Annotation Metrics

Time Stamps: Record timestamps for each activity.
Participants’ Characteristics: Gather information about participants’ age, gender, and other relevant demographics.
Activity Labels: Annotate collected data with activity labels (e.g., cooking, watching TV, sleeping).

Quality Assurance

Stages

Data Quality: Implement data quality checks to ensure accuracy and reliability of collected data.
Privacy Protection: Strictly adhere to privacy regulations and obtain informed consent from participants. Ensure that data is anonymized and cannot be traced back to specific individuals.
Data Security: Implement robust data security measures to protect sensitive information.

QA Metrics

Data Accuracy: Ensure data accuracy through regular validation checks.
Privacy Compliance: Regularly audit data handling processes for privacy compliance.

Conclusion

The Person Home Activity Dataset provides a valuable resource for a wide range of applications, from improving smart home systems to understanding human behavior within domestic environments. With diverse data sources, activity annotations, and stringent privacy measures, it offers researchers and developers a comprehensive dataset while safeguarding participants’ privacy.

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