UMAFall dataset

(Universidad de Málaga-FALL & ADL repository)

 

Eduardo Casilari (Coordinator), Jose-Antonio Santoyo-Ramón, Jose Manuel Cano-García

Departamento de Tecnología Electrónica

ecasilari@uma.es

April 2017

 

 

ZIP file with the dataset

 

 

Videos of the Fall movements

 

Videos of the ADL movements

 

 

DESCRIPTION OF UMAFall DATASET

Eduardo Casilari, Jose A. Santoyo-Ramón, Jose M. Cano-García

Universidad de Málaga (Departamento de Tecnología Electrónica)

Departamento de Tecnología Electrónica, Universidad de Málaga, 29071 Malaga (Spain)

Telephone No. 34 952132755, Fax No. 34 952131447

June 2018

 

 

Abstract. This document describes the files containing the mobility traces generated by a group of 19 experimental subjects that emulated a set of predetermined ADL (Activities of Daily Life) and falls. The traces are aimed at evaluating fall detection algorithms and can be freely downloaded from:

http://webpersonal.uma.es/de/ECASILARI/Fall_ADL_Traces/UMA_FALL_ADL_dataset.html

 

1. Description of the testbed

The initial experimental testbed (with 17 volunteers) was developed by Jose Antonio Santoyo-Román for his Msc. Thesis (presented in June 2016), which was supervised by Eduardo Casilari, associate professor in the University of Malaga (Spain). The second version of the previous dataset included two extra experimental subjects and new types of ADL (in particular, several hand activities, such as clapping hands, raising the hands, opening a door or making a phone call).These new samples were generated in April 2017.

During the execution of the movements, the subject transports a network consisting of five wireless nodes: an Android smartphone (which is located in a trouser pocket) and four motes attached to different parts of the body (ankle, wrist, chest and waist) through elastic bands, as it is illustrated in Figure 1.

 

Figure2

Fig.1. Location of the sensors (red arrows) and the smartphone (green arrow)

 

The motes were implemented in SimpleLink Multi-Standard CC2650 SensorTag units of Texas Instruments, which are provided with a Bluetooth Low Energy (BLE) interface and a multi-chip MPU-9250 module by InvenSense, housing a tri-axis accelerometer, a triaxial gyroscope and a magnetometer.

During the tests, two different smartphone models were employed. The models and characteristics of the built-in accelerometers are presented in Table 1.

 

Smartphone Model

Integrated Accelerometer

Range

Resolution

Samsung S5

MPU6500 (Invensense)

±2 𝑔

6.103515·10−5 𝑔

LG G4

LGE Accelerometer (BOSCH)

±16 𝑔

1.213651 ·10−4 𝑔

Table 1. Characteristics of the employed smartphones

 

The orientation of the sensors (SensorTag and Smartphones), which is sketched in Figure 2, was the same for all the experiments. The picture indicates the orientation of the sensors for the five considered locations when the subject's body is standing up with the hands down. In the case of the wrist, x-axis is parallel to the arm length.

Please remark that in the measurements captured by SensorTags x-axis represents the direction that is perpendicular to the floor while for the smartphone the equivalent axis is y-axis.

 

figura

Fig.2. Orientation of the onbody sensors during the experiments

 

All the experiments were executed in a domestic environment (see pictures in Figure 3), including a bedroom (A), a living room (B) and scales in an apartment block. Falls were mimicked on a mattress on a terrace.

Fig.3. Domestic environment where the experiments took place

 

In the deployed architecture, as soon as every experiment is initiated, the four Sensortags and the Smartphone activate their embedded mobility sensors (accelerometer, gyroscope and magnetometer), which periodically capture the corresponding magnitude. The sampling rate in the smartphone was fixed to 200 Hz, while the employed rate in the SensorTags was 20 Hz.

The program running in the SensorTags sends via BLE the captured samples to a specific app running in the smartphone. The app, which was specifically designed for the testbed, is in charge of storing the measurements transmitted from the four SensorTags as well as those captured by the smartphones. For every received sample, the smartphone associates a timestamp and the Bluetooth MAC address of the mote that transmitted it. For each experiment, all the samples from the five mobility sensors (the four SensorTags and the smartphone) are stored in a CSV (Comma Separated Value) file.

Thus, each CSV file includes the measurements of the 5 sensors for a single movement (ADL or fall) executed by a particular subject. All the movements are monitored during 15 seconds. 

The whole dataset includes 746 files, which are compressed in the file: UMAFall_Dataset.zip.

The typology of the executed movements can be visualized (except for four types of ADLs, numbered 9 to 12 in the next section) in a set of videos which can be downloaded from the same Web page of the traces.

 

1.  Information of the CSV file names

The name of the CSV file with the traces indicates:

-The numerical ID of the subject (from 1 to 19) that executed the movement. The personal features (gender, age, height and weight) of each experimental subject are presented in Table 2.

-The type of the movement (ADL or FALL).

-The subtype of movement (typology of the ADL or fall). The samples include 12 different typologies of ADLs and 3 different types of falls.

·         Types of Executed ADLs: 1) normal walking, 2) light jogging, 3) body bending, 4) hopping, 5) climbing stairs (up), 6) climbing stairs (down), 7) lying down and getting up from a bed, 8) sitting down (and up) on (from) a chair, 9) clapping hands (applauding), 10) raising the hands, 11) making a phone call, 12) opening a door.

·         Types of Emulated Falls (on a mattress): 1) lateral, 2) frontal 3) backwards)

-The number of the trial of the same type and subtype executed by that user (as long as subjects may repeat every movement up to 18 times).

-The date (year, month, day) and time (hour,min, sec.) in which the experiment was conducted.

 

Table 2. Personal characteristics of the participants in the testbed.

Subject ID

Gender

Age

Height (cm)

Weight (kg)

Subject 1

Female

67

156

76

Subject 2

Female

22

167

63

Subject 3

Male

68

168

97

Subject 4

Male

27

173

90

Subject 5

Male

24

179

68

Subject 6

Male

24

175

79

Subject 7

Male

28

195

81

Subject 8

Female

22

167

57

Subject 9

Male

55

170

83

Subject 10

Male

19

178

68

Subject 11

Male

26

176

73

Subject 12

Female

51

155

55

Subject 13

Female

18

159

50

Subject 14

Female

22

164

52

Subject 15

Male

26

179

67

Subject 16

Male

21

173

77

Subject 17

Female

27

166

66

Subject 18

Male

24

177

66

Subject 19

Female

23

163

93

 

3. Content of the files

Header. Every CSV file begins with a header describing the characteristics of the experiment: the features of the Subject, the type of movement (ADL, fall), a Boolean value indicating if the experiment corresponds to a fall, the movement subtype, the number of the trial, the number of employed sensors (5), the characteristics of the employed accelerometers and the Bluetooth MAC addresses, ID and location of the five nodes that integrate the network (the smartphone and the four SensorTags).

All the lines in the header begins with the character ‘%’.

Traces

After the header, every line in the files corresponds to a measurement captured by a particular mobility sensor of a determined node (mote or SensorTag).

The format of the lines, which is also explained in the file header, includes 7 numerical values separated by a semicolon:

-The time (in ms) since the experiment began.

-The number of the sample (for the same sensor and node).

-The three real numbers describing the measurements of the triaxial sensor (x-axis, y-axis and z-axis). The units are g, °/s or µT depending on whether the measurement was performed by an accelerometer, a gyroscope or a magnetometer, respectively.

-An integer (0, 1 or 2) describing the type of the sensor that originated the measurement (Accelerometer = 0 , Gyroscope = 1, Magnetometer = 2)

- An integer (from 0 to 4) informing about the sensing node (the correspondence between this numerical code and the Bluetooth MAC address and position of the motes is described in the file header).

 

The lines are ordered following the source (sensor/mote) of the samples. So, the lines corresponding to the samples for the same sensor and mote are presented at a stretch. So, two samples from different sensors and/or motes that were measured at the same time are separated in the files.

 

 

References

The source and authors of this publicly available dataset should be acknowledged in all publications in which it is utilized as by referencing any of the following papers as well as this web-site:

·         Santoyo-Ramón, José Antonio, Eduardo Casilari, and José Manuel Cano-García. "Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection with supervised learning." Sensors 18.4 (2018): 1155.

·         Casilari, Eduardo, Jose A. Santoyo-Ramón, and Jose M. Cano-García. "UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection." Procedia Computer Science 110 (2017): 32-39.