Optimizing Parameter Settings in Target Predictor for Pointing Tasks

The idea of target prediction for pointing tasks seems to hold promise for human computer interaction. However, few studies have studied the impacts of target predictor's inner parameter settings on the human performance. In this paper, therefore, we perform an experiment to investigate the effectiveness of target prediction for pointing tasks under different parameter setting conditions. We found that parameter settings for the predictor can be optimized to improve performance. The optimal combination was sampling rate (Hz) at the level of 40 and sample size (N) at the level of 12. Our study could provide HCI researchers with some valuable insights into the subject of target prediction for pointing tasks.