DAMMI:Daily Activities in a Psychologically Annotated Multi-Modal IoT dataset
<|reference_start|>DAMMI:Daily Activities in a Psychologically Annotated Multi-Modal IoT dataset: The growth in the elderly population and the shift in the age pyramid have increased the demand for healthcare and well-being services. To address this concern, alongside the rising cost of medical care, the concept of ageing at home has emerged, driven by recent advances in medical and technological solutions. Experts in computer science, communication technology, and healthcare have collaborated to develop affordable health solutions by employing sensors in living environments, wearable devices, and smartphones, in association with advanced data mining and intelligent systems with learning capabilities, to monitor, analyze, and predict the health status of elderly individuals. However, implementing intelligent healthcare systems and developing analytical techniques requires testing and evaluating algorithms on real-world data. Despite the need, there is a shortage of publicly available datasets that meet these requirements. To address this gap, we present the DAMMI dataset in this work, designed to support researchers in the field. The dataset includes daily activity data of an elderly individual collected via home-installed sensors, smartphone data, and a wristband over 146 days. It also contains daily psychological reports provided by a team of psychologists. Furthermore, the data collection spans significant events such as the COVID-19 pandemic, New Year's holidays, and the religious month of Ramadan, offering additional opportunities for analysis. In this paper, we outline detailed information about the data collection system, the types of data recorded, and pre-processed event logs. This dataset is intended to assist professionals in IoT and data mining in evaluating and implementing their research ideas.<|reference_end|>
@article{rad2024dammi:daily,
    title={DAMMI:Daily Activities in a Psychologically Annotated Multi-Modal IoT
  dataset},
    author={Mohsen Falah Rad, Kamrad Khoshhal Roudposhti, Mohammad Hassan
  Khoobkar, Mohsen Shirali, Zahra Ahmadi, Carlos Fernandez-Llatas},
    journal={arXiv preprint arXiv:2410.04152},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04152},
    primaryClass={cs.AI}
}