Multimodal dataset for fall detection. Includes acceleration data collected from a tag and two smartwatches, and location reported by the tag. More details about the data collection procedure can be found in notes.md.

Contents

The repository contains:

data/location_data.csv and data/full_acceleration – preprocessed acceleration and location data from 10 participants and mannequin simulated falls with target variable identified

data/subsampled_acceleration_data.csv – subsampled acceleration dataset used for training the AI model

notes.md – description of activities performed and notes from data collection

videos – reference videos for performed activities

Authors

Piotr Sowiński – research methodology, data collection and processing

Monika Kobus – research methodology, data collection

Anna Dąbrowska – research methodology, methodological supervision

Kajetan Rachwał – data collection

Karolina Bogacka – research methodology

Krzysztof Baszczyński – research methodology, data collection

Anastasiya Danilenka – research methodology, data collection and processing

Acknowledgements

This work is part of the ASSIST-IoT project that has received funding from the EU’s Horizon 2020 research and innovation programme under grant agreement No 957258.

The Central Institute for Labour Protection – National Research Institute provided facilities and equipment for data collection.

License

The dataset is licensed under the Creative Commons Attribution 4.0 International License.

Context

SMS Spam Collection Data Set

Acknowledgements

Data Set Information:

This corpus has been collected from free or free for research sources at the Internet:

-> A collection of 425 SMS spam messages was manually extracted from the Grumbletext Web site. This is a UK forum in which cell phone users make public claims about SMS spam messages, most of them without reporting the very spam message received. The identification of the text of spam messages in the claims is a very hard and time-consuming task, and it involved carefully scanning hundreds of web pages. The Grumbletext Web site is: [Web Link]. -> A subset of 3,375 SMS randomly chosen ham messages of the NUS SMS Corpus (NSC), which is a dataset of about 10,000 legitimate messages collected for research at the Department of Computer Science at the National University of Singapore. The messages largely originate from Singaporeans and mostly from students attending the University. These messages were collected from volunteers who were made aware that their contributions were going to be made publicly available. The NUS SMS Corpus is avalaible at: [Web Link]. -> A list of 450 SMS ham messages collected from Caroline Tag's PhD Thesis available at [Web Link]. -> Finally, we have incorporated the SMS Spam Corpus v.0.1 Big. It has 1,002 SMS ham messages and 322 spam messages and it is public available at: [Web Link]. This corpus has been used in the following academic researches:

The BlueHealth International Survey was a deliverable of the Horizon 2020 BlueHealth project. It addressed the lack of coordinated and harmonised data across countries on people’s recreational visits to natural environments, in particular blue spaces (i.e. natural environments where water is a salient feature), and their effects on people’s physical and psychological health.

The data was collected from nationally representative samples of adults from 11 European countries across the course of the years 2017-2018 by the market research company, YouGov. Along with the survey data, geographical exposures are supplied (e.g. land cover classes, air pollution) which were appended to the participant's given residence. Residential addresses have been removed from this dataset. Filtering variables and summary variables are also provided.

The survey consists of the following modules of questions: subjective well-being items, items concerning frequencies of visits to natural environments, natural environment perceptions, recent bluespace visit characteristics, an experimental module on water quality, health and well-being items, and demographic items.

Publications related to this data mention that data were collected in 18 countries. The data deposit herein refers to the 11 countries where the data collection was funded by the BlueHealth project. Data from the remaining 7 countries and territories was not funded by the same source and we do not have the permission to deposit these as a combined data file.

We request that users acknowledge the use of this data in the following ways in any outputs they produce:

• Acknowledge the BlueHealth project protocol paper: https://doi.org/10.1136/bmjopen-2017-016188
• Acknowledge the technical report describing the methodology of the BlueHealth International Survey: https://doi.org/10.17605/OSF.IO/7AZU2
• Acknowledge this data deposit.
• Acknowledge the source of funding with the following text: "The BlueHealth International Survey data was created using funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 666773."

The Global Health Data Exchange (GHDx) is a catalog that provides relevant data on population health. The catalog contains surveys, censuses, vital statistics, and other health-related data. The GHDx was created by the Institute for Health Metrics and Evaluations (IHME), an independent global health research center at the University of Washington. The GHDx is a place where information about data is brought together, discussed, and featured in the context of health and demographic research. The GHDx raises awareness about different groups collecting data worldwide and provides standardized citations to encourage appropriate acknowledgment of data owners’ contributions.

Midcourse Space Experiment (MSX). Original acknowledgement for data: This research made use of data products from the Midcourse Space Experiment. Processing of the data was funded by the Ballistic Missile Defense Organization with additional support from NASA Office of Space Science. This research has also made use of the NASA/ IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

SuperDARN RAWACF files for the calendar year 2012. INSTRUMENT INFORMATION AND RULES OF THE ROAD:

*** Before downloading and using the data, make sure to read the README file in the top directory of this collection. ***

SuperDARN is an international collaboration operating high frequency (HF) radars deployed in the northern and southern hemispheres to measure ionospheric plasma circulation. Each partner institution secures funding and manages operations for their own facilities. The continued availability of SuperDARN data depends on the proper acknowledgement of data by its users. Guidelines for data acknowledgement are as follows:

When data from an individual radar or radars are used, users must contact the principal investigator(s) of those radar(s) to obtain the appropriate acknowledgement information and to offer collaboration, where appropriate. Contact information is available in the README file for this collection.

For all usage of SuperDARN data, users are asked to include the following standard acknowledgement text: “The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.”

While SuperDARN has an open data use policy, i.e., prior permission to access and analyse the data is not required, the data user is strongly encouraged to establish early contact with any Principal Investigator whose data are involved in the project to discuss the intended usage and collaboration. Data can be subject to limitations that are not immediately evident to users. In addition, some data are embargoed for use by designated Principal Investigators for a period of one year. SuperDARN and the organizations that contributed data must be acknowledged in all reports and publications that use SuperDARN data.

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