Summary of Dammi:daily Activities in a Psychologically Annotated Multi-modal Iot Dataset, by Mohsen Falah Rad et al.
DAMMI:Daily Activities in a Psychologically Annotated Multi-Modal IoT dataset
by Mohsen Falah Rad, Kamrad Khoshhal Roudposhti, Mohammad Hassan Khoobkar, Mohsen Shirali, Zahra Ahmadi, Carlos Fernandez-Llatas
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DAMMI dataset aims to address the shortage of publicly available datasets for testing and evaluating intelligent healthcare systems and analytical techniques. The dataset includes daily activity data collected via home-installed sensors, smartphone data, and a wristband over 146 days, as well as daily psychological reports provided by a team of psychologists. This comprehensive dataset spans significant events such as the COVID-19 pandemic, New Year’s holidays, and the religious month of Ramadan, offering additional opportunities for analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new dataset called DAMMI to support researchers in developing affordable health solutions for elderly individuals. The dataset includes data from sensors installed at home, smartphones, and a wristband, as well as daily psychological reports. This data is collected over 146 days and includes special events like the COVID-19 pandemic. |
