Evaluation of the Text Input Performance of Touch-Based Smartwatches with Different Hand Postures and in Different Body Movements

Most of the previous studies on smartwatch input methods were conducted in a stable experimental environment (while the user was sitting and using comfortable hand postures), and there have been few studies on the characteristics of input methods using different hand postures and in different body movements. This study evaluated smartwatch performance with different hand postures and in different body movements and compared the result with the ZoomBoard performance. We collected 16 participants' smartwatch input data and analyzed their tapping behavior with four hand postures (index finger support [IS], no finger support, thumb and middle finger support [TMS], and thumb support) and in two different body movements (walking and standing). We studied the input accuracy and speed with different hand postures and the distribution of the touchpoints. The results indicated that IS has the lowest error rate and fastest performance when the user is standing, and TMS when the user is walking. Also, the different hand postures had different touchpoint distributions. Therefore, this paper provides design considerations for future research on human-computer interaction on ultra-small screens, especially smartwatches.