Visual impairment is a widespread global health issue that affects millions of people across all ages and backgrounds. Timely intervention is essential for the effective management of eye diseases. Smartphones offer the possibility of continuously recording facial gestures during interaction with the device, whereby changes such as squinting of the eyes could indicate progressive vision loss. In this context, a mobile health application was developed to conduct a digital logMAR test while simultaneously capturing real-time facial features. A total of 37 participants took part in a controlled mobile eye test study. The facial landmarks recorded during the test were analyzed to identify patterns that can distinguish between sequences of letters that were read correctly, partially, or not at all. Specifically, explorative data analysis and receiver operating characteristic curves were employed to determine facial landmarks with high discriminative power in relation to reading ability. The predominant facial regions that showed the most significant change under reduced performance during the vision test were the nose, mouth, and cheeks. Notably, the characteristic maximum squinting of the cheeks stood out with an area under the curve of 0.82. The analysis showed the potential of tracking specific facial features for continuous and unobtrusive vision assessment. It motivates to integrate facial feature analysis into an everyday application such as a web browser and to conduct a study in a non-standardized environment on a larger scale.

We present a low-cost, fully reproducible software and hardware protocol for smartphone sensor battery cost tests. Our pipeline combines a rigorous hardware checklist and light-sealed enclosure, a software checklist for iOS devices, and a BatteryTest app to control sensor configurations and log battery state during tests. Methodologically, we applied this standardized protocol in 30 independent analyzed test runs using six iPhone 14 Pro and three iPhone 13 Pro devices, and compared battery-life outcomes across predefined sensor conditions (idle, TrueDepth, GPS, accelerometer, pedometer, gyroscope, and rear camera), sampling rates, and sensor-specific settings. Key findings include: (i) Baseline battery life was approximately 10% higher on the 14 Pro versus the 13 Pro models under idle conditions. Sensor activation substantially reduced battery life, with GPS and camera usage exhibiting the strongest impact. (ii) Software parameters matter: the sampling rate change from 27 s to 3 s led to significantly decreased battery life in several scenarios, while reducing the location accuracy in GPS tests increased battery life by up to 20 h on the 13 Pro devices. (iii) Cross-device-generation consistency is heterogeneous. The iPhone 14 Pro lasts up to 50% longer on GPS tests, yet drains about an hour faster than the 13 Pro in camera tests. This work introduces the first standardized, and fully reproducible protocol for quantifying sensor-specific battery consumption on iPhones, enabling consistent, comparable, and low-cost energy benchmarking across device generations.