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ADL Detection Challenge at IEEE Healthcom 2016 Conference
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http://adl-challenge.wearables-at-work.biz
http://www.lustcon.de/WordPress/wearables-at-work/2016/03/adl-challenge/
http://ieeehealthcom2016.com/ http://ieeehealthcom2016.com/workshops

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Copyright Note
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The data are intended to be used for the ADL Detection Challenge at IEEE Healthcom 2016 Conference only.
Research usage is permitted, but requires a notification to the owner of the data, a note to the owner and proper citation.

Commercial usage of the data requires a written permission of the data owner. Please contact: 
Owner of the data: Heartsome Europe GmbH
EMail: adlchallenge@wearables-at-work.net

Citation for the data:
Waldhör K, Lutze R (2016) ADL Detection Challenge at IEEE Healthcom 2016 Conference - Wearables at work. http://www.lustcon.de/WordPress/wearables-at-work/2016/03/adl-challenge/.

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The ADL Detection Challenge
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Automatic recognition and surveillance of the regular occurrence of ADLs – activities of daily life like tooth brushing, shaving, drinking, eating, combing, writing and more – can help in detecting accidents in the household or gradual changes in the behavior of elderly persons. In detail, the identification of health critical situations like dehydration, tumbles or disorientation is of great importance. If the elderly persons are suffering from mild cognitive impairment (MCI) of beginning dementia and a potentially dangerous situation is detected, external help can be alerted automatically in order to clarify the situation, to prevent further damages or mitigate them.

Smartwatches and wearables are ideal tools collecting necessary data with their embedded sensors and applying various recognition algorithms for detecting ADLs based on these data. The advantage of these wearables technologies is their “invisibility”, meaning others will not immediately recognize that a wearer may have a handicap. This is in contrast to other products which typically stigmatize their users (e.g. emergency alert wrist bands resp. buttons).

This challenge is targeting to bring together developers, researchers and companies who want to demonstrate their achievements in this area.
Organization of the Challenge

A set of training data from real ADLs together with their classification will be made available through this web site. Interested participants can download the training data after registration and create detection models for the data. Participants are completely free how to implement their detection task. The only restriction is that later on participants must be able to run their models in real time in the workshop.

In the workshop the organizers will distribute several test data, also from real ADLs. The participants can then use their trained models to run and classify the test data and prepare their classification results in a standardized way. They will have about 60 minutes for this task. After this the participants show and explain their models, solutions and classifications results in a short presentation. After the presentations the best solution is chosen and receives a prize. This is followed by a general discussion and roundup of the workshop.
The following describes the data collection process, structure of the sensor data for the ADL challenge and the ADLs which have been recorded.

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Data Collecting
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All data have been collected using a Samsung Gear S.
Data have been collected by one person for a period of about 6 month and are anotated with associated the ADL. Collection period per ADL is about 10 - 15 seconds.

More information for the collection app can be found here:
http://www.heartsome.de/de/android/gears-sensor.html

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Format of the ADL challenge CSV Files
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The files use the following naming scheme:
"gear_s-Cardmix-kle-1-22-12-2015-14-23-25.csv"  = (gear_s-ADL-USER-date-time.csv) erfasst. Each sensor value goes into a sperate line of the csv file. The number of sensor data per line depend on the sensor type, e.g. Gyrometer produces three measures (x, y, z).

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Eample of CSV file entries
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0;0;1461658501431;GST;true;3;-127;SideTu;Health;kle;true
1;1;1461658504659;GA;9.523433685302734;-1.7012970447540283;1.026520848274231;4.22322940826416;0.5051100254058838;-5.843994140625
2;2;1461658504659;GR;1.2207999229431152;0.122079998254776;1.2818399667739868
3;3;1461658504661;GA;9.456435203552246;-1.6701902151107788;1.0049854516983032;3.324984550476074;0.42897355556488037;-4.6924238204956055
4;4;1461658504661;GR;1.2207999229431152;0.122079998254776;1.2818399667739868
5;5;1461658504663;GA;9.518648147583008;-1.6989041566848755;1.0001999139785767;2.7097578048706055;0.32020771503448486;-3.757767677307129 
...
13;13;1461603439714;GM;-20.1;11.27;-40.91
...
24;24;1461658504787;GP;956.47;503.27597950145986;1013.25
...
27;27;1450984138750;GP;977.86;310.2601659259367;1013.25
...
42;42;1460529178642;GL;382
...
117;117;1461438013295;GS;RUNNING;11.7;2.8;1.14;0.07;1;0;1
...
828;828;1460625607549;GB;0.76
...
8664;8664;1461659054658;GW;MOVE_RIGHT;0;1360
...
9540;9540;1449469597000;GO;49.395884;10.890007;440;0;19;0;0.009746288099471525
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Column description
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1: current measurement number
2: current measurement number
3: time of measurement in milliseconds
4: sensor (G for Gear S; A=Acceleration, R=Gyroscope/Rotation, M=Magnetometer, P=Pressure, S=Stpe counter, H=Pulse, L=Light, W hand gesture, B battery)

The following columns and number of columns are sensor dependent:

GST: signature for the file (first line of file)
	if number of columns = 10: ignore value, ignore value, ignore value, ADL recorded, user name, ignore value
	if number of columns = 11: ignore value, ignore value, ignore value, ADL recorded, super ADL, user name, ignore value
	
GA: x,y,z acceleration, x,y,z acceleration without gravity
GR: x,y,z rotation values (gyrometer)
GM: x,y,z magnetometer values
GP: pressure in Millibar;height;calibrated pressure
GL: brightness in lux
GO: GPS coordinates -  altitude,longitue,altitude above sealevel,ignore remaining values
GW: hand gestures - gesture, gesture acceleration, gesture duration
GB: battery loading in %

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ADL code description
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The following shows the names of the various ADLs collected:

CardMix         - mixing of playing cards
Comb            - Combing
Drink           - Drinking
Drive           - Driving
Eat, Eat1, Eat2 - Eating (should be combined into one class Eat)
EShave          - Electric shaving
ETooth          - Electric tooth cleaning
Hairdry         - Hairdrying
Other, Oth9     - non classified ADLs
Run             - Running
Shave           - shaving
SideTu          - Side tumble
Sit             - Sitting
Standup         - Standig up
TakTab          - Ingesting a tablette
Tooth           - Tooth cleaning (can be combined with ETooth ADL)
Tumble          - Tumbling
Wash            - Washing
Write           - Writing






