GENERAL INFORMATION ........................... 1. Dataset title: UMAFall: Fall Detection Dataset (Universidad de Malaga) 2. Authors: Eduardo Casilari, Jose A.Santoyo-Ramón 3. Author contact information: Eduardo Casilari. Email: ecasilari@uma.es METHODOLOGICAL INFORMATION ................................. 1. Description of the methods for collection/generation of data: The files contain 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. Several video clips describing the performed movements are also included. Reference: Casilari, Eduardo; A.Santoyo-Ramón, Jose (2018). UMAFall: Fall Detection Dataset (Universidad de Malaga). figshare. Dataset. https://doi.org/10.6084/m9.figshare.4214283.v8 2. Data processing methods 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) 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. 3. Software or instruments needed to interpret the data: Data are represented throug CSV (Comma Separated Values) plain-text files which can be directly loaded and processed from any programming language. 4. Standards and calibration information, if appropriate: Sensors are supposed to be calibrated by the vendor. 5. Environmental or experimental conditions: All the experiments were executed in a domestic environment, including a bedroom , a living room and scales in an apartment block. Falls were mimicked on a mattress on a terrace. FILE OVERVIEW ---------------------- The dataset is organized as a single directory containing a set of CSV-formatted files . The name of each CSV file indicates: -The numerical ID of the subject (from 1 to 19) that executed the movement. -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. DATA-SPECIFIC INFORMATION: ------------------------------------------- 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 ‘%’. 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). MORE INFORMATION ------------------- Refer to the following articles for further information about the dataset: · 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.