Method for discovering spatial arm positions with depth sensor data at low-performance devices

Abstract

A method of arm aiming direction estimation for low performance Internet of Things devices is proposed. It uses Human Pose Estimation (HPE) algorithms for retrieving human skeleton key points. Having these key points, arm aiming directions model is calculated. Two well-known HPE methods (PoseNet and OpenPose) are examined. These algorithms have been tested and compared by the average angle of error. The system includes a Raspberry Pi 4B singleboard computer and an Intel RealSense D435i depth sensor. The developed approach may be utilized in “smart home” gesture control systems.

Keywords

• human pose estimation
• human computer interaction
• human machine interaction
• depth maps
• internet of things
• gesture control