Analysis of Heart Rate Variability in Response to Serious Games in Elderly People.

As the proportion of elderly people continues to grow, so does the concern about age-related cognitive decline. Serious games have been developed for cognitive training or treatment, but measuring the activity of the autonomic nervous system (ANS) has not been taken to account. However, cognitive functioning has been known to be heavily influenced by the autonomic nervous system (ANS), and ANS activity can be quantified using heart rate variability (HRV). This paper aims to analyze the physiological response in normal elderly people as they play two types of serious games using HRV features from electrocardiography (ECG). A wearable device designed in-house was used to measure ECG, and the data from this device was pre-processed using digital signal processing techniques. Ten HRV features were extracted, including time-domain, nonlinear, and frequency-domain features. The experiment proceeds as follows: rest for three minutes, play a cognitive aptitude game, rest for another three minutes, followed by two reaction time games. Data from thirty older adults (age: 65.9 ± 7.34; male: 15, female: 15) were analyzed. The statistical results show that there was a significant difference in the HRV between the two types of games. From this, it can be concluded that the type of game has a significant effect on the ANS response. This can be further used in designing games for the elderly, either for training or mood management.

Using Flexible Curved Noncontact Active Electrodes to Monitor Long-Term Heart Rate Variability.

The purpose of this study is to utilize flexible curved noncontact active electrodes to develop a nonperception, long-term, and wireless heart rate monitoring system. This study also verified the functions and capabilities of the system and provided information on physiological parameters recorded during our tests. Our system was used in tandem with a commercially standard measurement system; both systems were used to measure ECG signals on 10 healthy subjects under the simulated home and office scenarios. We verified the R-peak measurement accuracy of our system and used T-tests to analyze the data collected by both systems; our system reached an average sensitivity value of 0.983 and an average positive predictive value of 0.991 over several different scenarios where R-peak measurements were also highly accurate. The R-R time intervals of our system were highly consistent with the standard system. The correlation coefficient calculated reached almost one, and the differences between the two systems mostly fell within the ±10 ms range. Further study of the HRV time-domain parameters under four different scenarios showed no significant differences in most HRV parameters compared to the measurements by the standard system. We also used our system to record long-term heart rate signals.

The Analytical Study in the Assessment of Body Segment Composition Using Electrical Impedance in Patients with Schizophrenia.

Bioelectrical impedance analysis technology was used tomeasure whole body parameters and electrical resistance and reactance of specific body regions in schizophrenia patients after long-term medication use. Patient data was compared to that of a control group of subjects to analyze metabolic differences. Our experimental group was composed of 60 schizophrenia patients, and the control group was composed of 30 normal subjects. T-test and ANOVA analyses were conducted to compared differences between groups, with confidencev intervals set at 95%. T-test results showed significant differences in body fat levels between groups (F=4.753, p=0.031<0.05). Additionally, we also found significant differences in upper body resistance and reactance (F=78.433, p=0.000<0.05, F=67.488, p=0.000<0.05), which could be related to accumulation of body fat. These results show correlation between schizophrenia and development of metabolic syndrome.

The non-contact heart rate measurement system for monitoring HRV.

A noncontact ECG monitoring and analysis system was developed using capacitive-coupled device integrated to a home sofa. Electrodes were placed on the backrest of a sofa separated from the body with only the chair covering and the user's clothing. The study also incorporates measurements using different fabric materials, and a pure cotton material was chosen to cover the chair's backrest. The material was chosen to improve the signal to noise ratio. The system is initially implemented on a home sofa and is able to measure non-contact ECG through thin cotton clothing and perform heart rate analysis to calculate the heart rate variability (HRV) parameters. It was also tested under different conditions and results from reading and sleeping exhibited a stable ECG. Subsequently, results from our calculated HRV were found to be identical to those of a commercially available HRV analyzer. However, HRV parameters are easily affected by motion artifacts generated during drinking or eating with the latter producing a more severe disturbance. Lastly, parameters measured are saved on a cloud database, providing users with a long-term monitoring and recording for physiological information.