DOI: 10.5061/dryad.jdfn2z3nm
title: Activity recognition from in-the-wild smartwatches (ArWISE)

description: The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is
 based on sensor data and activity labels collected from smart watches as
 part of several studies for a total of 854 participants across 20 cohorts.
 The sensor data consisted of 10Hz accelerometer, gyroscope, and location
 information that has been processed into anonymized features computed from
 one minute windows of data: local time, date, and day of week; mean and
 standard deviation of yaw, pitch, roll, x/y/z/total rotation rate,
 x/y/z/total acceleration, speed, course, distance from home, and bearing
 from home. The activity label is one of eat, errands, exercise, hobby,
 housework, hygiene, relax, sleep, socialize, travel, work, other. There
 are 470M data points total, of which 37M are labeled.
description: We introduce ArWISE (Activity recognition from in-the-Wild
 SmartwatchEs), a dataset containing labeled and unlabeled data collected
 by Apple Watches. ArWISE represents readings collected from 20 studies in
 2 countries over 8 years. Data Collection
 Data collection followed a consistent protocol for each study.
 Participants were given an Apple Watch to wear each day on their
 non-dominant arm. While they wore the watch, a custom app collected 3d
 accelerometer and gyroscope readings at 10Hz. Additionally, the app
 collected the person’s location every minute or when the magnitude of the
 acceleration vector exceeded a threshold. At random
 times throughout each day, the smartwatch prompted the participant to
 select an activity from a scroll-down list that best described their
 current activity. The distribution of user-provided labels across 12
 activity categories are Eat (6.5%), Errands (3.7%), Exercise (4.7%), Hobby
 (1.1%), Housework (19.7%), Hygiene (1.9%), Other (3.1%), Relax (37.7%),
 Sleep (3.0%), Socialize (3.7%), Travel (5.6%), Work (9.1%). The label was
 applied to five minutes of sensor readings ending at the time of the
 participant’s response. Additionally, an external
 annotator provided labels for a much greater density of data collected for
 cohorts 7 and 18. This person used a tool that visualized 3D movement
 data, a map of visited locations, and time stamps, at arbitrary time
 frames. While the data collection mechanism was the
 same for all study cohorts, other parameters varied. These include the
 number of participants, participant demographics, length of data
 collection, and other clinical variables that were collected. A summary of
 study cohort parameters is given in Table 1, where HOA=healthy older
 adult, SCD=subjective cognitive decline, and MCI=mild cognitive
 impairment. Table 1. ArWISE
 Cohorts. 
 <strong>Cohort</strong>
 <strong>Sample</strong>
 <strong>Study/participant
 characteristics</strong> 
 <em>1</em> 
 4 Younger
 adults, self-reported activities 
 <em>2</em>
 185 
 HOA/SCD/MCI<sup>a</sup>, English and Spanish
 self-reported activities 
 <em>3</em> 
 56 Younger
 adults, no activity labels 
 <em>4</em> 
 46 
 HOA/SCD/MCI, self-reported activities 
 
 <em>5</em> 
 10 Older adult pairs,
 no activity labels 
 <em>6</em> 
 35 HOA/SCD/MCI, no
 activity labels 
 <em>7</em> 
 37 HOA/SCD/MCI,
 self-reported activities and expert-annotated activities
 
 <em>8</em> 
 9 Younger adults,
 self-reported activities 
 <em>9</em> 
 15 Younger
 adults, self-reported activities 
 <em>10</em>
 13 
 Younger adults, self-reported activities 
 
 <em>11</em> 
 3 Younger adults,
 self-reported activities 
 <em>12</em> 
 18 Younger
 adults, self-reported activities 
 <em>13</em>
 10 
 Younger adults, self-reported activities 
 
 <em>14</em> 
 22 Younger adults,
 self-reported activities 
 <em>15</em> 
 21 
 HOA/SCD/MCI, no activity labels 
 <em>16</em>
 6 
 Younger adults, self-reported activities 
 
 <em>17</em> 
 103 HOA/SCD/MCI,
 self-reported activities 
 <em>18</em> 
 16 
 HOA/SCD/MCI, self-reported activities and expert-annotated
 activities 
 <em>19</em> 
 16 HOA/SCD/MCI,
 self-reported activities 
 <em>20</em> 
 229 
 HOA/SCD/MCI, no activity labels 
 Dataset Characteristics
 The ArWISE dataset is unique among the resources that are
 typically available for human activity recognition. Some of the
 most-analyzed datasets reflect movement categories based on data that are
 collected in controlled settings [1], [2]. However, more recent wearable
 sensor datasets represent activities observed in uncontrolled settings.
 Although 150 participants are monitored for only 24 hours with
 movement-only sensors, Capture-24 [3] includes labels for functional
 activities of household chores, sports, and sleep in real-world settings.
 ExtraSensory [4] monitors a smaller set of 60 participants with up to 20
 seconds of movement and location readings but provides diverse activity
 and location. The UK Biobank [5] offers 7 days of accelerometry data for
 100,000+ participants and Intuition [6] longitudinally observes 23,004
 participants, though no ground-truth labels are provided for these
 data. The ArWISE dataset contains 37,578,059 labeled
 points from 503 participants across 15 cohorts and 469,881,358 total
 points for 854 participants across 20 cohorts. Each point represents one
 minute of data. ArWISE offers unique benefits for HAR analysis, including
 a large set of participants, functional activity labels, longitudinal
 observations, and consistency in the data collection mechanism.
 Data Preprocessing Our functional activity
 recognition models consider both raw time series data and engineered
 features. Table 2 summarizes the features that are available for both
 cases. Table 2. ArWISE raw and engineered
 data features. 
 <strong>Type</strong>
 <strong>Category</strong>
 <strong>Feature</strong> 
 Raw (10Hz) 
 time date and
 time Raw
 (10Hz) motion 
 yaw, pitch, roll, rotation rate (x,y,z), acceleration
 (x,y,z) Raw
 (10Hz) location 
 latitude, longitude, altitude, course, speed
 Engineered (1 min)
 time time of
 day (radians, sin, cos), day of week 
 Engineered (1 min) 
 motion mean &
 stdev (each raw movement variable), mean &
 stdev (rotation vector magnitude, acceleration vector magnitude)
 Engineered (1 min)
 location 
 mean & stdev (course, speed) mean
 & stdev (distance from home, latitude distance from home,
 longitude distance from home) mode & stdev
 (bearing from home) 
 Class label activity
 eat, errands, exercise, hobby, housework, hygiene, relax, sleep,
 socialize, travel, work, other 
 We imputed missing values (with mode for location
 and median for other features) and dropped data points where there was not
 a complete minute of sensor readings leading up to the label. We also
 normalized each feature separately. For the engineered
 features, we aggregated values over one minute leading up to the user (or
 expert) label. Time of day was represented as a set of sinusoidal features
 to maintain the periodic nature. We did not use raw location values here,
 to preserve user privacy and because the values do not easily generalize
 between individuals. Instead, we defined a person’s home as the location
 visited most often at the beginning of each day. We then extracted the
 Haversine distance and trigonometric bearing from the person’s home
 location.
 <strong>References</strong> [1]
 O. Napoli <em>et al.</em>, “A benchmark for domain adaptation
 and generalization in smartphone-based human activity recognition,”
 <em>Scientific Data</em>, vol. 11, p. 1192, 2024.
 [2] A. Reiss, D. Stricker, and G. Hendeby, “Towards robust
 activity recognition for everyday life: Methods and evaluation,” in
 <em>Pervasive Computing Technologies for Healthcare</em>,
 2013, pp. 25–32. [3] S. Chan <em>et
 al.</em>, “CAPTURE-24: A large dataset of wrist-worn activity
 tracker data collected in the wild for human activity recognition,”
 <em>Nature Scientific Data</em>, vol. 11, p. 1135,
 2024. [4] Y. Vaizman, K. Ellis, and G. Lanckriet,
 “Recognizing detailed human context in the wild from smartphones and
 smartwatches,” <em>IEEE Pervasive Computing</em>, vol. 16, no.
 4, pp. 62–74, 2017. [5] C. Sudlow, J. Gallacher, N.
 Allen, V. Beral, P. Burton, and J. Danesh, “UK Biobank: An open access
 resource for identifying the causes of a wide range of complex diseases of
 middle and old age,” <em>PLoS Medicine</em>, vol. 12, no. 3,
 p. 1001779, 2015. [6] P. M. Butler, J. Yang, R. Brown,
 M. Hobbs, and A. Becker, “Smartwatch- and smartphone-based remote
 assessment of brain health and detection of mild cognitive
 impairment,” <em>Nature Medicine</em>, 2025, doi:
 https://doi.org/10.1038/s41591-024-03475-9.
description: # Activity recognition from in-the-wild smartwatches (ArWISE)
 [https://doi.org/10.5061/dryad.jdfn2z3nm](https://doi.org/10.5061/dryad.jdfn2z3nm) ## Description of the data and file structure CSV files for each participant's data are organized into zip files by cohort (c01-c20). Cohorts c02, c03, and c20 are split into multiple parts to adhere to the 10G limit per file. For cohorts c03 and c05, participants alternated between two watches (day and night); these are split across two files with w1 and w2 designations (note that file `c03.p053.w2.csv` is missing; no data was collected for that watch). The CSV files all include a header with feature names. All features are floats, except for the two time stamps that define the start and end times of the window used to generate each point, and the activity label. ### Features `stamp_start`, `stamp_end` Timestamps indicating the start and end of time window for computing features. The timestamp follows the ISO 8601 standard, but without the timezone. Times are in user's local time. `yaw_mean`, `pitch_mean`, `roll_mean` Mean of yaw/pitch/roll angle across window in radians. `rotation_rate_x_mean`, `rotation_rate_y_mean`, `rotation_rate_z_mean` Mean of rotation rate in around x/y/z axis in radians per second. `user_acceleration_x_mean`, `user_acceleration_y_mean`, `user_acceleration_z_mean` Mean of user acceleration (gravity removed) along x/y/z axis across window in meters per second^2. `yaw_std`, `pitch_std`, `roll_std` Standard deviation of yaw/pitch/roll angle across window in radians. `rotation_rate_x_std`, `rotation_rate_y_std`, `rotation_rate_z_std` Standard deviation of rotation rate in around x/y/z axis in radians per second. `user_acceleration_x_std`, `user_acceleration_y_std`, `user_acceleration_z_std` Standard deviation of user acceleration (gravity removed) along x/y/z axis across window in meters per second^2. `rotation_magnitude_mean` Mean of magnitude of rotation rate across window in radians per second. `acceleration_magnitude_mean` Mean of magnitude of user acceleration across window in meters per second^2. `rotation_magnitude_std` Standard deviation of magnitude of rotation rate across window in radians per second. `acceleration_magnitude_std` Standard deviation of magnitude of user acceleration across window in meters per second^2. `speed_mean`, `speed_std` Mean and standard deviation of speed across window in meters per second. Speed value of -1 indicates GPS speed unavailable. `course_mode`, `course_std` Mode and standard deviation of course across window in degrees. Course value of -1 indicates GPS course unavailable. `distance_from_home_mean`, `distance_from_home_std` Mean and standard deviation across window of Haversine distance from home in meters. `distance_from_home_latitude_mean`, `distance_from_home_latitude_std` Mean and standard deviation across window of distance from home along latitude in degrees. `distance_from_home_longitude_mean`, `distance_from_home_longitude_std` Mean and standard deviation across window of distance from home along longitude in degrees. `bearing_from_home_mode`, `bearing_from_home_std` Mode and standard deviation across window of bearing from home in degrees. `time_of_day_radians` Time of day in radians of end of window expressed as 2 * pi * (seconds since midnight) / (seconds in day). `time_of_day_sin` Equals `sin(time_of_day_radians)`. `time_of_day_cos` Equals `sin(time_of_day_radians)`. `day_of_week` Numeric equivalent of day of week for end of window, where Monday=0, Tuesday=1, ..., Sunday=6. `activity_label` Activity occurring during time window. One of 12 possible string values: Eat, Errands, Exercise, Hobby, Housework, Hygiene, Relax, Sleep, Socialize, Travel, Work, Other. Missing value indicates no ground truth label available for the time window. ### Missing data Individual feature values may be missing due to sensor limitations or no ground truth activity label provided by the user. Missing values are indicated by empty strings in the CSV file. Empty strings are used in order to be consistent with the code that uses the Python Pandas library, whose default behavior is to read (`read_csv`) and write (`to_csv`) empty strings to indicate missing data. ## Code/software Code used to generate datasets and for generating train/test datasets is available at [https://github.com/WSU-CASAS/ArWISE](https://github.com/WSU-CASAS/ArWISE).