Effect of the Nintendo Ring Fit Adventure Exergame on Running Completion Time and Psychological Factors Among University Students Engaging in Distance Learning During the COVID-19 Pandemic: Randomized Controlled Trial.

BACKGROUND: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased. OBJECTIVE: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score). METHODS: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period. RESULTS: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)_walk, CFIPA_run, or sleep quality. CONCLUSIONS: RFA could either maintain or improve an individual's physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period. TRIAL REGISTRATION: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040.

CMOS camera based visible light communication (VLC) using grayscale value distribution and machine learning algorithm.

We demonstrate a visible light communication (VLC) system using light emitting diode (LED) backlight display panel and mobile-phone complementary-metal-oxide-semiconductor (CMOS) camera. The panel is primarily used for displaying advertisements. By modulating its backlight, dynamic contents (i.e. secondary information) can be transmitted wirelessly to users based on rolling shutter effect (RSE) of the CMOS camera. As different display content will be displayed on the panel, the VLC performance is significantly limited if the noise-ratio (NR) is too high. Here, we propose and demonstrate a CMOS RSE pattern demodulation scheme using grayscale value distribution (GVD) and machine learning algorithm (MLA) to significantly enhance the demodulation.

Using logistic regression classification for mitigating high noise-ratio advisement light-panel in rolling-shutter based visible light communications.

We propose and experimentally demonstrated a light-panel and image sensor based visible light communication (VLC) system using machine learning (ML) algorithm. The ML algorithm is compared with the traditional demodulation scheme and the experimental results show that even at very high noise-ratio (NR) light-panel display content, the proposed ML algorithm shows significant bit error rate (BER) improvement.

Visible light communication and positioning using positioning cells and machine learning algorithms.

We propose and experimentally demonstrate a practical visible light position (VLP) system using repeated unit cells and machine learning (ML) algorithms. ML is employed to increase the positioning accuracy. Algorithms of the 2nd-order regression ML model and the polynomial trilateral ML model are discussed. More than 80% of the measurement data have position error within 4 cm when using the 2nd-order regression ML model, while the position error is within 5 cm when using the polynomial trilateral ML model.