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The wrist-worn sensors with their internal coordinate frames. Additionally, the direction of positive rotations are indicated for each axis.

The wrist-worn sensors with their internal coordinate frames. Additionally, the direction of positive rotations are indicated for each axis.

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Automatic detection of intake gestures is a key element of automatic dietary monitoring. Several types of sensors, including inertial measurement units (IMU) and video cameras, have been used for this purpose. The common machine learning approaches make use of the labelled sensor data to automatically learn how to make detections. One characteristi...

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Context 1
... hands, and non-dominant hands to left hands; for left-handed participants data for both hands has been transformed to achieve this. We can simulate a recording on the opposite wrist (mirrored horizontally, e.g., transforming data from left wrist as if it had been recorded on right wrist). Due to the way the sensors are mounted on the wrist (see Fig. 3), the horizontal direction corresponds to the x axis. For accelerometer data, we estimate mirroring by flipping the sign of the x axis, and for gyroscope by flipping the signs of the y and z axis. Further, we also flip the signs of the x and y axis to compensate for the different sensor orientations on the wrists, yielding ...
Context 2
... non-dominant hands to left hands; for left-handed participants data for both hands has been transformed to achieve this. Specifically, we mirrored the data of left-handed participants to transform the data from the left wrist as if it has been recorded on the right wrist, and vice versa. Due to the way the sensors are mounted on the wrist (see Fig. 3), the horizontal direction corresponds to the x axis. For accelerometer data, we estimate mirroring by flipping the sign of the x axis, and for gyroscope by flipping the signs of the y and z axis. Further, we also flip the signs of the x and y axis to compensate for the different sensor orientations on the wrists, yielding ...