The Influence of Input Method and Chinese Character Complexity on the Elderly Using Smartphones to Input Information – A Study from China

Abstract

The usage of smartphones holds paramount importance in enhancing the ability of elderly people to live independently. This research aims to explore the various scenarios in which Chinese elderly people employ smartphones for information input, and how input method and Chinese character complexity affect their information input process. To achieve this, we conducted semi-structured interviews to investigate typical situations where Chinese elderly individuals use smartphones to input information. Based on the survey results, we devised an experiment to assess the impact of the input method and Chinese character complexity on the elderly’s objective physical load, mental load, input performance, and subjective experience during the inputting process. The findings revealed that the choice of input method significantly affects the objective physical load. Distinct input method engaged different major muscle groups during the process. As the Chinese character complexity increased, the elderly’s input performance declined, with handwriting input being particularly affected. Thus, we proposed enhancing the current handwriting interface by allowing users to input complex Chinese characters through handwritten pinyin, and simplify the start steps of the handwriting function based on the pinyin input interface. This study contributes to the development of evidence-based guidelines for the aging design of smart mobile devices, thus enhancing the elderly’s overall experience in using such technology.

Keywords:

• Aging design
• sEMG
• eye tracking
• smartphones
• information input
• Chinese character

Acknowledgment

The authors would like to gratefully acknowledge the reviewers’ comments.

Authors contributions

The contribution of Jinchun Wu is consistent with that of Yixuan Liu.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The study was funded by the National Key R&D Program of China (Grant No. 2022YFF0607003) and National Natural Science Foundation of China (Grant No. 72271053; No. 71871056).

Notes on contributors

Yixuan Liu

Yixuan Liu is currently working as an EngD student in Industrial Design at Southeast University, China. His research interests include human-computer interaction, ergonomic research, aging design, and medical equipment design.

Jinchun Wu

Jinchun Wu is currently working as a PhD candidate in Industrial Design at Southeast University, China. His research interests include human-computer interaction, neurodesign, ergonomic research, creative design thinking, light ergonomics, and human factors of nuclear power plants.

Xiaoyu Wang

Xiaoyu Wang is an Associate Professor in the Mechanical School at Southeast University, China. He received a Ph.D. degree from the University of California, Los Angeles. His research interests lie in human intent inference and human-robot interaction.

Chen Pan

Chen Pan received his Bachelor’s degree in Industrial Design from Nanjing Forestry University in 2020. He is currently studying for a master’s degree at Southeast University. His research interests include human-computer interaction in interface and road lighting evaluation.

Zihe Chen

Zihe Chen is a current Master’s degree candidate at Southeast University, his research interests lie in Human-Computer Interaction, Product Design, and Interaction Design

Chengqi Xue

Chengqi Xue is a Professor in the Mechanical School at Southeast University, China. His research interests lie in human-computer interaction, neurodesign, and ergonomic research.