Understanding the Effects of Restraining Finger Coactivation in Mid-Air Typing: from a Neuromechanical Perspective

要旨

Typing in mid-air is often perceived as intuitive yet presents challenges due to finger coactivation, a neuromechanical phenomenon that involves involuntary finger movements stemming from the lack of physical constraints. Previous studies were used to examine and address the impacts of finger coactivation using algorithmic approaches. Alternatively, this paper explores the neuromechanical effects of finger coactivation on mid-air typing, aiming to deepen our understanding and provide valuable insights to improve these interactions. We utilized a wearable device that restrains finger coactivation as a prop to conduct two mid-air studies, including a rapid finger-tapping task and a ten-finger typing task. The results revealed that restraining coactivation not only reduced mispresses, which is a classic coactivated error always considered as harm caused by coactivation. Unexpectedly, the reduction of motor control errors and spelling errors, thinking as non-coactivated errors, also be observed. Additionally, the study evaluated the neural resources involved in motor execution using functional Near Infrared Spectroscopy (fNIRS), which tracked cortical arousal during mid-air typing. The findings demonstrated decreased activation in the primary motor cortex of the left hemisphere when coactivation was restrained, suggesting a diminished motor execution load. This reduction suggests that a portion of neural resources is conserved, which also potentially aligns with perceived lower mental workload and decreased frustration levels.

著者
Hechuan Zhang
University of Chinese Academy of Sciences, Beijing, China/Beijing, China
Xuewei Liang
Xi'an jiaotong university, Xi'an, China
Ying Lei
East China Normal University, Shanghai, China
Yanjun Chen
Institute of Software, Chinese Academy of Sciences, Beijing, China
Zhenxuan He
Institute of software, Chinese Academy of Sciences, Beijing, China
Yu Zhang
School of mechanical engineering, Xi'an, Shaanxi, China
Lihan Chen
Peking University, Peking, China
Hongnan Lin
Institute of Software, Chinese Academy of Sciences, Beijing, Beijing, China
Teng Han
Institute of Software, Chinese Academy of Sciences, Beijing, China
Feng Tian
Institute of software, Chinese Academy of Sciences, Beijing, China
論文URL

https://doi.org/10.1145/3654777.3676441

動画

会議: UIST 2024

ACM Symposium on User Interface Software and Technology

セッション: 1. Bodily Signals

Westin: Allegheny 1
4 件の発表
2024-10-15 19:40:00
2024-10-15 20:40:00