Analysis of Heart Rate Variability and Game Performance in Normal and Cognitively Impaired Elderly Subjects Using Serious Games

被引:4
|
作者
Hou, Chun-Ju [1 ]
Chen, Yen-Ting [1 ]
Capilayan, Mycel A. [1 ]
Huang, Min-Wei [2 ]
Huang, Ji-Jer [1 ]
机构
[1] Southern Taiwan Univ Sci & Technol, Dept Elect Engn, 1 Nan Tai St, Tainan 710301, Taiwan
[2] Taichung Vet Gen Hosp, Dept Psychiat, Chiayi Branch, 600 Shixian Rd, Chiayi 60090, Taiwan
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 09期
关键词
elderly healthcare; cognitive decline; serious games; HRV; machine learning; ASSESSMENT MOCA; ASSOCIATION; DEMENTIA; DECLINE; SYSTEM; MEMORY; ADULTS; MEN; AGE;
D O I
10.3390/app12094164
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Cognitive decline is one of the primary concerns in the elderly population. Serious games have been used for different purposes related to elderly care, such as physical therapy, cognitive training and mood management. There has been scientific evidence regarding the relationship between cognition and the autonomic nervous system (ANS) through heart rate variability (HRV). This paper explores the changes in the ANS among elderly people of normal and impaired cognition through measured HRV. Forty-eight subjects were classified into two groups: normal cognition (NC) (n = 24) and mild cognitive impairment (MCI) (n = 24). The subjects went through the following experiment flow: rest for 3 min (Rest 1), play a cognitive aptitude game (Game 1), rest for another 3 min (Rest 2), then play two reaction-time games (Game 2&3). Ten HRV features were extracted from measured electrocardiography (ECG) signals. Based on statistical analysis, there was no significant difference on the HRV between the two groups, but the experiment sessions do have a significant effect. There was no significant interaction between sessions and cognitive status. This implies that the HRV between the two groups have no significant difference, and they will experience similar changes in their HRV regardless of their cognitive status. Based on the game performance, there was a significant difference between the two groups of elderly people. Tree-based pipeline optimization tool (TPOT) was used for generating a machine learning pipeline for classification. Classification accuracy of 68.75% was achieved using HRV features, but higher accuracies of 83.33% and 81.20% were achieved using game performance or both HRV and game performance features, respectively. These results show that HRV has the potential to be used for detection of mild cognition impairment, but game performance can yield better accuracy. Thus, serious games have the potential to be used for assessing cognitive decline among the elderly.
引用
收藏
页数:16
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