Data Labeling for Participatory Sensing Using Geature Recognition with Smartwatches

Supervised activity recognition algorithms require labeled data to train classification models. Labeling an activity can be performed trough observation, in controlled conditions, or thru self-labeling. The two first approaches are intrusive, which makes the task tedious for the person performing the activity, as well as for the one tagging the activity. This paper proposes a technique for activity labeling using subtle gestures that are simple to execute, and that can be sensed and recognized using smartwatches. The signals obtained by the inertial sensor in a smartwatch are used to train classification algorithms in order to identify the gesture. We obtained data from 15 participants who executed 6 proposed gestures in 3 different positions. 208 characteristics were computed from the accelerometer and gyroscope signals and were used to train two classification algorithms to detect the six proposed gestures. The results obtained achieve a precision of 81% for the 6 subtle gestures, and 91% when using only the first 3 gestures

Data Labeling for Participatory Sensing Using Geature Recognition with Smartwatches

Supervised activity recognition algorithms require labeled data to train classification models. Labeling an activity can be performed trough observation, in controlled conditions, or thru self-labeling. The two first approaches are intrusive, which makes the task tedious for the person performing the activity, as well as for the one tagging the activity. This paper proposes a technique for activity labeling using subtle gestures that are simple to execute, and that can be sensed and recognized using smartwatches. The signals obtained by the inertial sensor in a smartwatch are used to train classification algorithms in order to identify the gesture. We obtained data from 15 participants who executed 6 proposed gestures in 3 different positions. 208 characteristics were computed from the accelerometer and gyroscope signals and were used to train two classification algorithms to detect the six proposed gestures. The results obtained achieve a precision of 81% for the 6 subtle gestures, and 91% when using only the first 3 gestures