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Activity recognition datasets

The data

Name Scenario Original purpose Sensors Subjects Seg. / Cont. Static / periodic act. Sporadic act. Comments Authors Link
Skoda mini checkpoint 10 manipulative gestures performed in a car maintenance scenario Gesture recognition 20 3D acceleration sensors (60 attributes) 1 Segmented and continous recording in dataset - 10: write notes, open engine hood, close engine hood, check door gaps, open door, close door, open/close two doors, check trunk gap, open/close trunk, check steering wheel.
70 instances of each gesture.
EWSN 2008
Daniel Roggen, Piero Zappi
BodyAttack fitness 6 fitness activity classes, done mostly with the legs. Analyse effect of sensor displacement 10 3D accelerometers on the leg 1 C 6 activities: flick kicks; knee lifts; jumping jacks; superman jumps; high knee runs; feet back runs - ISWC 2009 Daniel Roggen, Kilian Foerster
HCI gestures 5 gestures performed freehand or guided against a blackboard Analyse effect of sensor displacement 8 3D acceleration sensors (24 attributes) 1 Segmented and continous recording in dataset - 5 gestures: triangle up, square, circle, infinity, triangle down.
10 instances of freehand, 60 instances of guided gestures
ISWC 2009 Daniel Roggen, Kilian Foerster
Daphnet Freezing of Gait Dataset in users with Parkinson's disease Gait recording of PD users with occasional freeze Detection of gait freeze 3 3D acceleration sensors (9 attributes) 10 C walk, freeze - - Daniel Roggen, Marc Baechlin, Meir Plotnik, Jeffrey M. Hausdorff, Nir Giladi
Also on the UCI ML repository
Opportunity Challenge Dataset of wearable, object, and ambient sensors recorded in a room simulating a studio flat where users performed early morning cleanup and breakfast activities. The dataset comprises freely executed “activities of daily living” (ADL) and more a constrained “drill” run. Reference benchmark dataset for human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc). Body-worn sensors: 7 inertial measurement units, 12 3D acceleration sensors, 4 3D localization information
Object sensors: 12 objects with 3D acceleration and 2D rate of turn
Ambient sensors: 13 switches and 8 3D acceleration sensors
4 C Modes of locomotion and postures 17 gestures in the Drill runs, larger number in the ADL runs Dataset publication
Challenge publication
Daniel Roggen and colleagues (see publications) Available on the UCI ML repository


  • Segmented: the recordings start and stop to comprise exactly one instance of an activity (e.g. one “drink” gesture, or one “walk”).
  • Continuous: the dataset contains a continous recording of the data delivered by the sensors, within which usually several activities take place.
  • Static/periodic activities: activities for which the sensor signals are usually static or periodic, such as when taking static postures (sit, lie, stand), during locomotion (walking, running, bicycling), or when performing some repetitive moves (e.g. jumping jack).
  • Sporadic activities: activities which are short lived and embedded in a null class, such as “drinking from a cup”, “toggling a light switch”.

Other dataset repositories

wiki/dataset.txt · Last modified: 2020/02/02 11:37 (external edit)