Abstract
Wearable Accelerometer Layout Optimization for Activity Recognition Based on Swarm Intelligence and User Preference
Highlights
The recognition of human activity has drawn much attention from multiple disciplines and applied scenarios
Decreasing the space limitations and enabling the subject to move in a wider range, based on the inertial measurement unit (IMU), many studies have been conducted to develop wearable Human activity recognition (HAR) systems [11,12,13]
Prior studies related to the sensor positions normally concentrated on the position specific HAR system or the transfer learning across the different sensor positions [20, 21]
Summary
The recognition of human activity has drawn much attention from multiple disciplines and applied scenarios. Decreasing the space limitations and enabling the subject to move in a wider range, based on the inertial measurement unit (IMU), many studies have been conducted to develop wearable HAR systems [11,12,13]. Increasing attention in the HAR field has been focused on off-the-shelf commercial electronic products embedded with accelerometers, such as smartwatches, smartphones, etc These types of systems only provide a limited interface for users and require the devices to be attached to designated locations on the body, such as the wrist or the trouser pocket. The examined sensor positions still maintain the less number and fails to generate the optimal sensor positions under the different conditions
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