WEDA-FALL

WEDA-FALL - Wrist Elderly Daily Activity and Fall Dataset. A fall-related Dataset captured at 50Hz using a smartwatch on the wrist, containing Elderly People Data.

The device used to compile this dataset is the Fitbit Sense. This dataset was compiled using the Fitbit Gather Data Mechanism. One can use this tool to gather more data and expand this dataset.

Any questions you may have, feel free to contact joaojtmarques@tecnico.ulisboa.pt.

Description of the Datasets’ movements

This dataset has compiled with two types of movements. Falls and ADLs (Activities of Daily Life). A fall usually happens given a context and a cause. Having that in mind, the types of falls compiled in this dataset are detailed in Table 1.

Table 1: Types of Falls selected for this work

Code	Activity
F01	Fall forward while walking caused by a slip
F02	Lateral fall while walking caused by a slip
F03	Fall backward while walking caused by a slip
F04	Fall forward while walking caused by a trip
F05	Fall backward when trying to sit down
F06	Fall forward while sitting, caused by fainting or falling asleep
F07	Fall backward while sitting, caused by fainting or falling asleep
F08	Lateral fall while sitting, caused by fainting or falling asleep

ADL selection was based on the frequency of real-life activities and similarity to fall movements that could generate False Positives. ADL choice had the position of the device in mind - the wrist - and its selection is detailed in Table 2.

Table 2: Types of ADLs selected for this work

Code	Activity
D01	Walking
D02	Jogging
D03	Walking up and downstairs
D04	Sitting on a chair, wait a moment, and get up
D05	Sitting a moment, attempt to get up and collapse into a chair
D06	Crouching (bending at the knees), tie shoes, and get up
D07	Stumble while walking
D08	Gently jump without falling (trying to reach high object)
D09	Hit table with hand
D10	Clapping Hands
D11	Opening and closing door

Participants Description

Falling is an issue that mainly affects elderly people. No previous wrist-based dataset provided Elderly People Data. This dataset does. The groups of participants are, therefore, divided in two: Young Participants and Elder Participants. The statistics of each group can be found in Table 3 and 4, respectively. The combined statistics of this dataset is detailed in Table 5.

Table 3: Statistics of Young Participants

User_id	Age	Height (m)	Weight (Kg)	Gender
1	22	1.76	56.3	Male
2	22	1.78	56.0	Male
3	20	1.73	69.5	Male
4	21	1.70	57.1	Female
5	23	1.67	59.6	Male
6	22	1.67	69.0	Male
7	21	1.78	68.1	Male
8	23	1.62	61.0	Female
9	22	1.70	52.0	Female
10	23	1.83	77.0	Male
11	23	1.69	61.8	Female
12	23	1.78	64.5	Female
13	22	1.79	66.0	Male
14	46	1.84	83.0	Male

Table 3: Statistics of Elder Participants

User_id	Age	Height (m)	Weight (Kg)	Gender
21	95	1.70	71.0	Male
22	85	1.53	62.0	Female
23	82	1.60	60.0	Female
24	81	1.52	63.0	Female
25	81	1.73	72.0	Female
26	83	1.75	85.0	Male
27	89	1.71	71.5	Male
28	88	1.57	52.5	Female
29	77	1.60	65.9	Female
30	80	1.79	72.0	Male
31	88	1.63	53.0	Female

Table 4: Overall statistics of Participants

Number of Males	Number of Females	Min - Max Age	Average Age	Min - Max Height (m)	Average Height (m)	Min - Max Weight (Kg)	Average Weight (Kg)
13	12	20 - 95	50.48	1.52 - 1.84	1,699	52 - 85	66.3

The activities each Elder individual performed is detailed in Table 5. Note that Elder Participants were not asked to perform any Fall nor activities that could harm the individual, since it is impossible to assure their safety in these type of movements.

Table 5: Activities Elder Participants performed

User id	Activities performed
21	D01; D04; D09; D10; D11;
22	D01; D04; D09; D10; D11;
23	D01; D03; D04; D09; D10; D11;
24	D01; D03; D04; D09; D10; D11;
25	D01; D04; D09; D10; D11;
26	D01; D03; D04; D09; D10; D11;
27	D01; D03; D04; D09; D10; D11;
28	D01; D03; D04; D09; D10; D11;
29	D01; D03; D04; D09; D10; D11;
30	D01; D03; D04; D09; D10; D11;
31	D01; D03; D04; D09; D10; D11;

Dataset Acquisition Conditions

First and foremost, it is important to denote that every fall movement was performed in a mattress to avoid the risk of injury. This work’s dataset is accompanied with Videos detailing the exact conditions of each movement.

Younger Participants were asked to repeat each activity three times. Fall F08 was asked to be repeated 4 times, where the first two the volunteer would fall towards the side of the watch, while in the last two he would fall to the opposite side. From YP alone, this dataset totals 350 (14 × 7 × 3 + 14 × 4) fall signals, and 462 (14 × 11 × 3) ADL signals. The number of epetitions each elder was asked to performed varied a lot, based on each volunteer’s mobility, comfort and fatigue. The total EP signals is 157, having on total 619 ADL Signals.

This dataset, that was acquired at 50Hz, also provides 4 more frequencies: 40Hz, 25Hz, 10Hz and 5Hz. These frequencies of data were obtained from the 50Hz frequency data. Both accelerometer, gyroscope and orientation sensor data were gathered in this dataset.

For the frequency of 50Hz, it is also provided the vertical acceleration. Acceleration was projected on an inertial referential, where the previous z-axis value of acceleration now corresponds to the acceleration projected in the direction of the vertically upward vector, usually aligned with and opposite to the gravity vector.

It is also provided the begin and end of actual falls, that is, the second where the fall actually starts and ends, correspondent to the 4 phases of fall (pre-fall, impact, body adjustment, and post-fall phases). These timestamps were manually assembled, and they can have mistakes. That information is in dataset/fall_timestamps.csv.

Every filename in the dataset follows the following format: <user_id>R<trial_counter><sensor_type>.csv, and is stored in a directory that identifies the movement, whose name is the code of each movement (see Table 1 and 2). The <user_id> distinguishes each volunteer, even though it does not identify them, since it is an abstract integer. The is the identifier of the trial of the movement since each movement was usually repeated more than once. For instance, the file dataset/50Hz/F07/U02_R03_accel.csv identifies the accelerometer readings with 50Hz for the third trial of fall number 7 (code F07) of the user with <user_id> = 2.

Acknowledgements

This work was supported by the project IntelligentCare – Intelligent Multimorbidity Management System (Reference LISBOA-01-0247-FEDER-045948) is co-financed by the ERDF – European Regional Development Fund through the Lisbon Portugal Regional Operational Program – LISBOA 2020 and by the Portuguese Foundation for Science and Technology – FCT under CMU Portugal