Motion Estimation and Motion Compensation
Conditions
Brief summary
Visual fatigue, a hallmark of CVS, is particularly prevalent among mobile gamers who engage in prolonged gaming sessions. The dynamic visual stimuli in modern games-characterized by rapid movements, high refresh rates, and intricate details-place substantial strain on the oculomotor system, leading to symptoms like eye discomfort, reduced concentration, and even cognitive fatigue. Studies have shown that gaming on smartphones can exacerbate visual fatigue. These effects are often compounded by suboptimal viewing conditions, such as improper lighting or prolonged near-work distances, which are common during mobile gaming. Given the growing popularity of mobile gaming, there is an urgent need to address visual fatigue in this context to enhance user comfort and prevent long-term ocular health issues. The investigators hypothesize that MEMC-enabled devices may reduce the physiological and cognitive burden of visual fatigue by improving motion clarity and minimizing oculomotor strain. To test this, the investigators comparing MEMC-enabled and standard gaming experiences, this study provides insights into differences in visual health indicators, cognitive load, and physiological responses. These findings aim to inform the development of display technologies and usage guidelines that promote healthier interactions with smartphones, particularly for reducing visual fatigue and prolonged gaming scenarios.
Interventions
DEVICEthe Motion Estimation and Motion Compensation
Motion Estimation and Motion Compensation (MEMC) is a video processing technology designed to enhance display quality by interpolating additional frames, thereby reducing motion blur and stuttering in fast-moving scenes. This is achieved by estimating motion between video frames and generating intermediate frames, effectively increasing the frame rate (e.g., from 30 FPS to 120 FPS) and display refresh rate (e.g., from 30 Hz to 120 Hz).
Sponsors
Zhongshan Ophthalmic Center, Sun Yat-sen University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
TRIPLE (Subject, Investigator, Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
25 Years to 36 Years
Healthy volunteers
Yes
Inclusion criteria
* volunteers aged from 25 years to 36 years who were familiar with smartphone use and proficient in playing the King of Glory game
Exclusion criteria
* comprised individuals who used eyedrops or other treatments within the previous week, had ocular diseases beyond dry eye, wore contact lenses within the past month, underwent eye surgery within six months, or were pregnant, breastfeeding, or had severe systemic diseases, psychosis, or dementia.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Visual Fatigue Questionnaire | 30 minutes | Participants completed the Visual Fatigue Subjective Scale questionnaire post each 30-minute gaming session (MEMC and non-MEMC). This 20-item scale assessed eye strain, headaches, perceived clarity, and general comfort, with responses analyzed to identify differences in subjective visual fatigue between conditions. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Actions Per Minute | 30 minutes | Task performance was evaluated using actions per minute (APM), defined as the number of finger interactions (taps, swipes) per minute. Participants performed tasks in a consistent gaming scenario, with data collected via screen recording and custom logging software. APM reflects interface fluidity. Metrics were compared between MEMC and non-MEMC conditions to assess technology impact. |
| Average Operation Time | 30 minutes | Task performance was evaluated using average operation time (AOT), the mean duration per interaction. Participants performed tasks in a consistent gaming scenario, with data collected via screen recording and custom logging software. AOT indicates interaction efficiency. Metrics were compared between MEMC and non-MEMC conditions to assess technology impact. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Electroencephalography (EEG) for Overall Brain Functional Connectivity | 30 minutes | EEG was recorded using the Biosemi Actiview 64-channel system (Kanjian Technique Co., Ltd., Guangzhou, China) in a dark, quiet room, with participants keeping eyes closed for 5-minute resting-state sessions pre- and post-gaming. The 64 channels included Fp1, Fz, Fp2, AF3, AF4, ..., O1, Oz, O2, M1, M2, CB1, CB2. Signals were filtered (0.5-45 Hz), artifacts removed via Independent Component Analysis (ICA), and sampled at 100 Hz, with bilateral mastoids as reference. Power spectral density (PSD) of theta (4-7 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-40 Hz) waves was calculated using discrete Fourier transform (DFT) for O1, Oz, and O2 electrodes. Average relative power and Pearson correlation coefficients for functional connectivity were computed. Pre- and post-exposure differences were statistically analyzed. |
| Blood Pressure | 30 minutes | BP was measured using a Kefu electronic blood pressure monitor (Model KF-65D) with the cuff on the right upper arm, 2-3 cm above the elbow, at heart level. Participants remained still during automatic measurements, with systolic/diastolic BP recorded pre- and post-gaming (0-5 min and 25-30 min) to assess physiological impact. |
| Heart Rate | 30 minutes | HR was measured using a Kefu electronic blood pressure monitor (Model KF-65D) with the cuff on the right upper arm, 2-3 cm above the elbow, at heart level. Participants remained still during automatic measurements, with HR recorded pre- and post-gaming (0-5 min and 25-30 min) to assess physiological impact. |
Countries
China
Outcome results
None listed