2023
Matey-Sanz, Miguel; Casteleyn, Sven; Granell-Canut, Carlos
Dataset of inertial measurements of smartphones and smartwatches for human activity recognition Journal Article
In: Data in Brief, vol. 51, pp. 109809, 2023, ISSN: 2352-3409.
Abstract | Links | BibTeX | Tags: activity recognition, dataset, machine learning, smartphone app, smartwatch, symptoms
@article{Matey2023c,
title = {Dataset of inertial measurements of smartphones and smartwatches for human activity recognition},
author = {Miguel Matey-Sanz and Sven Casteleyn and Carlos Granell-Canut},
doi = {https://doi.org/10.1016/j.dib.2023.109809},
issn = {2352-3409},
year = {2023},
date = {2023-12-15},
journal = {Data in Brief},
volume = {51},
pages = {109809},
abstract = {This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.},
keywords = {activity recognition, dataset, machine learning, smartphone app, smartwatch, symptoms},
pubstate = {published},
tppubtype = {article}
}
This article describes a dataset for human activity recognition with inertial measurements, i.e., accelerometer and gyroscope, from a smartphone and a smartwatch placed in the left pocket and on the left wrist, respectively. Twenty-three heterogeneous subjects (μ = 44.3, σ = 14.3, 56% male) participated in the data collection, which consisted of performing five activities (seated, standing up, walking, turning, and sitting down) arranged in a specific sequence (corresponding with the TUG test). Subjects performed the sequence of activities multiple times while the devices collected inertial data at 100 Hz and were video-recorded by a researcher for data labelling purposes. The goal of this dataset is to provide smartphone- and smartwatch-based inertial data for human activity recognition collected from a heterogeneous (i.e., age-diverse, gender-balanced) set of subjects. Along with the dataset, the repository includes demographic information (age, gender), information about each sequence of activities (smartphone's orientation in the pocket, direction of turns), and a Python package with utility functions (data loading, visualization, etc). The dataset can be reused for different purposes in the field of human activity recognition, from cross-subject evaluation to comparison of recognition performance using data from smartphones and smartwatches.
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas
UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests Journal Article
In: IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 221-230, 2023, ISSN: 2832-7322.
Abstract | Links | BibTeX | Tags: A-wear, dataset, Wi-Fi
@article{Bravenec2023c,
title = {UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/JISPIN.2023.3335882},
issn = {2832-7322},
year = {2023},
date = {2023-11-22},
journal = {IEEE Journal of Indoor and Seamless Positioning and Navigation},
volume = {1},
pages = {221-230},
abstract = {This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.},
keywords = {A-wear, dataset, Wi-Fi},
pubstate = {published},
tppubtype = {article}
}
This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas
UJI Probes: Dataset of Wi-Fi Probe Requests Proceedings Article
In: 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1.
Abstract | Links | BibTeX | Tags: A-wear, dataset, Wi-Fi
@inproceedings{Bravenec2023b,
title = {UJI Probes: Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/IPIN57070.2023.10332508},
isbn = {979-8-3503-2012-1},
year = {2023},
date = {2023-09-25},
booktitle = {2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-6},
publisher = {IEEE},
abstract = {This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.},
keywords = {A-wear, dataset, Wi-Fi},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.