Data in brief 23 (2019) 103839
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Data in brief
journal homepage: www.elsevier.com/locate/dib
Data Article
A dataset for the development and optimization
of fall detection algorithms based on wearable
sensors
Valentina Cotechini, Alberto Belli, Lorenzo Palma,
Micaela Morettini, Laura Burattini*, Paola Pierleoni
Politecnica delle Marche, Ancona, Italy
Department of Information Engineering, Universita
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 4 February 2019
Received in revised form 1 March 2019
Accepted 7 March 2019
Available online 15 March 2019
This paper describes a dataset acquired on 8 subjects while
simulating 13 types of falls and 5 types of Activities of Daily Living
(ADL), each repeated 3 times. In details, data includes 4 simulated
falls forward (falling on knees ending up lying, ending in lateral
position, ending up lying, ending up lying with recovery), 4
backward (falling sitting ending up lying, ending in lateral position, ending up lying, ending up lying with recovery), 2 lateral
right (ending up lying, ending up lying with recovery), 2 lateral left
(ending up lying, ending up lying with recovery), and 1 syncope.
Simulated ADL are: lying on a bed then standing; walking a few
meters; sitting on a chair then standing; go up or down three
steps; and standing after picking something. Data were acquired
using a MARG sensor, a wearable multisensory device tied to the
subject's waist, that recorded time-variations of the subject's acceleration and orientation (expressed through the yaw, pitch and
roll angles). These data can be useful in the development and test
of algorithms to automatically identify and classify fall events. Fall
detection systems are particularly useful when a subject is alone
and not able to stand up after a fall, since an automatic alarm can
be sent remotely to receive proper help.
© 2019 The Author(s). Published by Elsevier Inc. This is an open
access article under the CC BY-NC-ND license (http://
creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:
Human fall
Fall detection
MARG (Magnetic Angular Rate and Gravity)
sensor
Wearable device
* Corresponding author.
E-mail addresses: vcotechini@gmail.com (V. Cotechini), a.belli@univpm.it (A. Belli), l.palma@univpm.it (L. Palma), m.morettini@univpm.it (M. Morettini), l.burattini@univpm.it (L. Burattini), p.pierleoni@univpm.it (P. Pierleoni).
https://doi.org/10.1016/j.dib.2019.103839
2352-3409/© 2019 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://
creativecommons.org/licenses/by-nc-nd/4.0/).
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V. Cotechini et al. / Data in brief 23 (2019) 103839
Specifications table
Subject area
More specific subject
area
Type of data
How data was
acquired
Bioengineering of movement
Fall detection
Graph, video
Wearable MARG (Magnetic Angular Rate and Gravity) sensor integrating a magnetometer
(HMC5883L, Honeywell, USA), an accelerometer (ADXL345, Analog Devices, USA) and a gyroscope
(ITG-3200, InvenSense Inc., USA).
Data format
Raw and analyzed
Experimental factors Raw data from sensors and analyzed data obtained from data fusion algorithm.
Experimental
Healthy young subjects simulating 13 falls (4 forward, 4 backward, 2 lateral right, 2 lateral left, 1
features
syncope) and 5 actions of daily living, while wearing MARG devise.
Data source location Sensor Network and Internet of Things laboratory, Department of Information Engineering, Universit
a
Politecnica delle Marche, Ancona, Italy
Data accessibility
Data is with this article
Related research
P. Pierleoni, A. Belli, L. Palma, M. Pellegrini, L. Pernini, S. Valenti, A High Reliability Wearable Device for
Elderly Fall Detection, IEEE SENSORS JOURNAL VOL. 15 NO. 8 (2015) 4544-53 [1]
article
Value of the data
This kind of data could be useful:
to develop algorithms to automatically identify and classify a fall event;
to test and compare already existing fall-detection algorithms;
to develop alarm systems for falls occurring at home especially among people (like elderly) who might be unable to seek
for help;
to support future studies on biomechanics of human fall.
1. Data
The proposed dataset is the only one that provides a complete set of data detected through a MARG
sensor placed at the waist of the subject representing the most comfortable and least invasive position
for wearing a sensor. Moreover, compared to other datasets in the literature [2], it is the only one that
presents a complete set of falls including syncope also defined as backwards fall against a wall.
Data are organized in two main directories, Fall and ADL each containing several folders as depicted
in Fig. 1; each path ends with three folders, FileTXT and Graph (as represented for ADL/Lying&Stand
only), containing the data files of each subject, and TrainingVideo, containing files .mp4.
The files inside FileTXT are called Si_j.txt, where i ¼ 1,2…8 indicates the subject, and j ¼ 1,2,3 indicates the repetition. Each .txt file is composed of 14 columns, separated by semi column, that
represent the timeseries of the following parameters listed in the first row:
- 1st column is time in seconds (s);
- 2nd to 4th columns contain acceleration along x, y and z axes (acc_x, acc_y, acc_z) expressed in
gravitational acceleration g (1 g ¼ 9.80665 m/s2);
- 5th to 7th columns contain angular rate with respect to x, y and z axes (gyr_x, gyr_y, gyr_z),
expressed in degree per second (⁰/s);
- 8th to 10th columns (mag_x, mag_y, mag_z) contain the Earth's magnetic field along x, y and z axes,
expressed in Gauss (G), 1 G ¼ 110 4 T (Tesla);
- 11th column contains the Signal Vector Magnitude (SVM) of the acceleration in g, computed as the
square root of the sum of squared acceleration components;
- 12th to 14th columns contain orientational yaw, pitch and roll angles around z, y and x axes,
respectively, expressed in degree ( ).
V. Cotechini et al. / Data in brief 23 (2019) 103839
3
Fig. 1. Data folders organization.
The length of recordings is variable among different subjects, trials and repetitions; thus, the
number of rows in each .txt file varies accordingly.
The files inside Graph are called Si_j.jpg, where i ¼ 1,2…8 indicates the subject, and j ¼ 1,2,3 indicates the repetition. Each .jpg file contains two subplots; the upper plot represents the SVM of the
acceleration over time, while lower plot represents the trend of the pitch and roll angles over time.
The files inside TrainingVideo show how to simulate a specific fall or Activities Daily Living (ADL).
Overall, there are 432 .txt files, 432 .jpg files and 18 .mp4 files.
2. Experimental design, materials and methods
The experiment was performed on 8 healthy volunteers (6 males and 2 females, from 22 to 29 years
old) and was carried out in accordance with the Declaration of Helsinki. Each subject signed an
informed written consent before participating.
The experimental protocol was proposed by Noury et al. [3] and previously used in Refs. [1,4] and
partially in Ref. [5] to simulate realistic scenarios of falls and ADL. After a proper video training, each
subject has simulated, for 3 times, 13 types of falls and 5 types of ADL as reported in Table 1. Specifically,
4 simulated falls are forward (falling on knees ending up lying, ending in lateral position, ending up
lying, ending up lying with recovery), 4 backward (falling sitting ending up lying, ending in lateral
position, ending up lying, ending up lying with recovery), 2 lateral right (ending up lying, ending up
lying with recovery), 2 lateral left (ending up lying, ending up lying with recovery), and 1 syncope.
Simulated ADL are lying on a bed then standing, walking a few meters, sitting on a chair then standing,
go up or down three steps, and standing after picking something.
Each subject has worn an elastic belt on which was fixed, through adhesive Velcro, the device
described in Ref. [1], including a MARG sensor inside a hard-plastic box. The MARG sensor integrates 3axis HMC5883L magnetometer (Honeywell, USA, with resolution of 4 mG in ±8 G fields), ADXL345
accelerometer (Analog Devices, USA, with resolution of 4 mg/LSB in ±16 g range) and ITG-3200 gyroscope (InvenSense Inc., USA, with resolution of 14.375 LSBs per /s in ±2000 /s range). A proper data
fusion algorithm applied to the raw signals of the three sensors [6] was used in Ref. [1] to compute yaw,
pitch and roll angles representing the subject's orientation, i.e. subject's rotation around the z, y and x
axis, respectively.
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V. Cotechini et al. / Data in brief 23 (2019) 103839
Table 1
Classification of the simulations performed in the experiment.
FALL
Forward
Backward
Lateral right
Lateral left
Syncope
ADL
falling on knees ending up lying
ending in lateral position
ending up lying
ending up lying with recovery
falling sitting ending up lying
ending in lateral position
ending up lying
ending up lying with recovery
ending up lying
ending up lying with recovery
ending up lying
ending up lying with recovery
slipping against a wall ending up sitting
lying on a bed then standing
walking a few meters
sitting on a chair then standing
climbing three steps
standing after picking something
Falls were performed safely with the supervision of support staff on a 15 cm thick mattress.
Acknowledgments
This work funded by the Italian Ministry of Education, University and Research. It was carried out in
the Sensor Network and Internet of Things Laboratory by the Telecommunication and Bioengineering
Politecnica delle Marche, Italy. Authors wish to thank the students who
Groups at the Universita
simulated the falls.
Transparency document
Transparency document associated with this article can be found in the online version at https://
doi.org/10.1016/j.dib.2019.103839.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.dib.2019.103839.
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