Detection Error Contaminated by Outliers to Classify Density Profiles Dependent on the Relative Speed Between a MIMO Sensor and a Human Hand

Abstract

The detection of human hand intrusion is crucial for improving the productivity of human-robot collaboration. Recent trends to safety-related sensors have adopted the concept of radar-based human presence detection. Some of them have already been commercialized. However, it has not yet been elucidated whether radars can sufficiently detect human hand intrusion and satisfy the required safety integrity level. In this study, we present outlier and error profiles obtained by detecting human hand intrusion based on the motion and speeds of the human hand and radar sensor. Our experiments indicate that slow hand movements require further studies from the viewpoint of safety. In addition, we suggest a new noise model demonstrating random noise and temporary outliers based on the recorded noise profiles of actual human participants. The obtained outlier and error profiles can be utilized as an outlier judgement criterion of the sensing in the specific case of the radar.