Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

Bar Crawl: Detecting Heavy Drinking Data Set
Download: Data Folder, Data Set Description

Abstract: Accelerometer and transdermal alcohol content data from a college bar crawl. Used to predict heavy drinking episodes via mobile data.

Data Set Characteristics:  

Multivariate, Time-Series

Number of Instances:

14057567

Area:

Life

Attribute Characteristics:

Real

Number of Attributes:

3

Date Donated

2020-02-24

Associated Tasks:

Classification, Regression

Missing Values?

N/A

Number of Web Hits:

40262


Source:

(a) Owner of database
Jackson A Killian (jkillian '@' g.harvard.edu, Harvard University); Danielle R Madden (University of Southern California); John Clapp (University of Southern California)
(b) Donor of database
Jackson A Killian (jkillian '@' g.harvard.edu, Harvard University); Danielle R Madden (University of Southern California); John Clapp (University of Southern California)
(c) Date collected
May 2017
(d) Date submitted
Jan 2020


Data Set Information:

Relevant Information:
All data is fully anonymized.

Data was originally collected from 19 participants, but the TAC readings of 6 participants were deemed unusable by SCRAM [1]. The data included is from the remaining 13 participants.

Accelerometer data was collected from smartphones at a sampling rate of 40Hz (file: all_accelerometer_data_pids_13.csv). The file contains 5 columns: a timestamp, a participant ID, and a sample from each axis of the accelerometer. Data was collected from a mix of 11 iPhones and 2 Android phones as noted in phone_types.csv. TAC data was collected using SCRAM [2] ankle bracelets and was collected at 30 minute intervals. The raw TAC readings are in the raw_tac directory. TAC readings which are more readily usable for processing are in clean_tac directory and have two columns: a timestamp and TAC reading. The cleaned TAC readings: (1) were processed with a zero-phase low-pass filter to smooth noise without shifting phase; (2) were shifted backwards by 45 minutes so the labels more closely match the true intoxication of the participant (since alcohol takes about 45 minutes to exit through the skin.) Please see the above referenced study for more details on how the data was processed ([Web Link]).

1 - [Web Link]
2 - J. Robert Zettl. The determination of blood alcohol concentration by transdermal measurement. [Web Link], 2002.

Number of Instances:
Accelerometer readings: 14,057,567
TAC readings: 715
Participants: 13

Number of Attributes:
- Time series: 3 axes of accelerometer data (columns x, y, z in all_accelerometer_data_pids_13.csv)
- Static: 1 phone-type feature (in phone_types.csv)
- Target: 1 time series of TAC for each of the 13 participants (in clean_tac directory).

For Each Attribute:
(Main)
all_accelerometer_data_pids_13.csv:
time: integer, unix timestamp, milliseconds
pid: symbolic, 13 categories listed in pids.txt
x: continuous, time-series
y: continuous, time-series
z: continuous, time-series
clean_tac/*.csv:
timestamp: integer, unix timestamp, seconds
TAC_Reading: continuous, time-series
phone_type.csv:
pid: symbolic, 13 categories listed in pids.txt
phonetype: symbolic, 2 categories (iPhone, Android)

(Other)
raw/*.xlsx:
TAC Level: continuous, time-series
IR Voltage: continuous, time-series
Temperature: continuous, time-series
Time: datetime
Date: datetime

Missing Attribute Values:
None

Target Distribution:
TAC is measured in g/dl where 0.08 is the legal limit for intoxication while driving
Mean TAC: 0.065 +/- 0.182
Max TAC: 0.443
TAC Inner Quartiles: 0.002, 0.029, 0.092
Mean Time-to-last-drink: 16.1 +/- 6.9 hrs


Attribute Information:

Provide information about each attribute in your data set.


Relevant Papers:

Past Usage:
(a) Complete reference of article where it was described/used:
Killian, J.A., Passino, K.M., Nandi, A., Madden, D.R. and Clapp, J., Learning to Detect Heavy Drinking Episodes Using Smartphone Accelerometer Data. In Proceedings of the 4th International Workshop on Knowledge Discovery in Healthcare Data co-located with the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) (pp. 35-42). [Web Link]
(b) Indication of what attribute(s) were being predicted
Features: Three-axis time series accelerometer data
Target: Time series transdermal alcohol content (TAC) data (real-time measure of intoxication)
(c) Indication of study's results
The study decomposed each time series into 10 second windows and performed binary classification to predict if windows corresponded to an intoxicated participant (TAC >= 0.08) or sober participant (TAC < 0.08). The study tested several models and achieved a test accuracy of 77.5% with a random forest.



Citation Request:

When using this dataset, please cite: Killian, J.A., Passino, K.M., Nandi, A., Madden, D.R. and Clapp, J., Learning to Detect Heavy Drinking Episodes Using Smartphone Accelerometer Data. In Proceedings of the 4th International Workshop on Knowledge Discovery in Healthcare Data co-located with the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019) (pp. 35-42). [Web Link]


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML