Activity and Context Recognition with Opportunistic Sensor Configurations

Opportunity Challenge - Tasks

 

The challenge proposes four different tasks addressing different aspects of the activity recognition problem. We provide 18 labelled session from 4 subjects that can be used as training dataset as shown in the dataset description. In order to emulate realistic online conditions we provided data of the whole recording without any segmentation.

Four different tasks are considered:

For the first three tasks, we provide data from the motion jacket (5 IMUs), 12 bluetooth body-worn accelerometers and  2 inertial sensors placed on the feet.  For Task C we provide only the data from the motion jacket sensors. Nevertheless, for all tasks, participants are free to use only a subset of the provided sensors. 


Task A: Multimodal activity recognition: Modes of locomotion

The goal of this task is to classify modes of locomotion from body-worn sensors.

Classes

Stand Walk Sit Lie

 

Testing Dataset: Subjects 2,3 (ADL4, ADL5)

 


Task B1: Automatic segmentation

Typically, activity recognition methods are evaluated using recordings that has already been segmented into the different target classes. However, realistic deployments are required to detect when no relevant action is performed (i.e. null class). This task involves the location of specific time points when relevant actions begins and ends within a continuous recording.

The data for this task correspond to right-arm gestures performed in a daily activities scenario (see task B1 for a list of gestures). Labels denote when any of the considered gestures is being executed or not.

The full set of sensors are considered for this task including the motion jacket, 12 bluetooth body-worn accelerometers and  inertial sensors on the feet.

Classes

Null Activity

 

Testing Dataset: Subjects 2,3 (ADL4, ADL5)

 


Task B2: Multimodal activity recognition: Gestures

This task concerns recognition of right-arm gestures performed in a daily activities scenario, as described above. We provided unsegmented labeled data sets for gestures corresponding to the classes listed below.

The full set of sensors are considered for this task including the motion jacket, 12 bluetooth body-worn accelerometers and  inertial sensors on the feet.

Classes 

Null clean_Table open_Drawer1 close_Drawer1
open_Dishwasher close_Dishwasher open_Drawer2 close_Drawer2
open_Fridge close_Fridge open_Drawer3 close_Drawer3
  move_Cup open_Door1 close_Door1
    open_Door2 close_Door2

Testing Dataset: Subjects 2,3 (ADL4, ADL5)

 


Task C: Robustness to noise: Gestures

Realistic applications are prone to noise due to different factors. This task focuses on methods that are robust to sensor noise. To this end, rotational and additive noise has been added to the testing dataset. The classes to be recognized are the same as for Task B2.

For this task, only the motion jacket sensors are considered.

Testing Dataset: Subject 4 (ADL4, ADL5)

 


Back to Challenge description

Contact

Do not hesitate to contact the consortium.

About

We develop opportunistic activity recognition systems: goal-oriented sensor assemblies spontaneously arise and self-organize to achieve a common activity and context recognition. We develop algorithms and architectures underlying context recognition in opportunistic systems.

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