Figure skating is a physically demanding and expensive sport, and beginners often lack enough accessible support to practice safely and improve efficiently. Skate Sensor addresses this problem through a wearable sensing system paired with a mobile application that helps users analyze skating techniques and access educational resources [1]. The project combines two shin-mounted sensor pods, a Flutter mobile app, and machine learning models developed with Python and PyTorch [2]. Together, these components collect motion data, synchronize left and right leg activity, classify techniques, and evaluate whether an attempt matches successful motion patterns. Several implementation challenges had to be considered, including sensor synchronization, limited training data, and memory use within the app’s media features. These issues were addressed through timeline alignment, expanded data collection strategies, and more efficient loading of visual content. Experimental design focused on model accuracy and the impact of informational screens. Overall, Skate Sensor demonstrates strong potential as a practical tool for safer, more informed, and more independent beginner training.
Optical camera communication (OCC) is considered as a key enabler of optical wireless communication technology. In OCC, light-emitting diodes (LEDs) serve as the transmitter and rolling shutter (RS) cameras as the receiver for high-speed communication. However, the received luminance from the LED is critically important for reliable data retrieval in OCC, which faces challenges due to the inherent nature of data collection. Smartphones, typically equipped with RS cameras, represent one of the most promising platforms for the commercial deployment of OCC. To ensure system reliability, various methods have been proposed to address the diversity in pixel illumination values captured by smartphone cameras. Furthermore, AI-based approaches that make RS cameras compatible with low-speed mobile scenarios and enhance overall system performance also introduce additional system complexity. In this paper, we provide a systematic review of the state-of-the-art methods for data retrieval in smartphone camera-based OCC. In particular, we provide specific challenges due to important factors, such as communication distance variation and the blooming effect. Furthermore, we discuss recent advancements, especially promising AI applications in OCC. Finally, we outline open research directions on smartphone camera-based OCC.
Manual inspection of photovoltaic systems is expensive, hazardous, and prone to inconsistency. This paper presents SunScout, a mobile application for offline solar-panel image management and on-device fault classification. The current mobile release organizes drone, gallery, and camera captures into reusable datasets and analyzes each stored asset with a fine-tuned EfficientNetB0 classifier deployed through ONNX Runtime. On the cleaned 1,575-image dataset, a frozen MobileNetV2 baseline reached 90.79% validation accuracy, while the proposed EfficientNetB0 model achieved 94.29%, a 3.50 percentage point improvement. Together, these results show that accurate solar fault analysis can be delivered on a consumer smartphone without requiring a persistent network connection.
Neurological and psychiatric conditions including Alzheimer's disease, PTSD, depression, and schizophrenia affect hundreds of millions of people globally, yet existing pharmaceutical treatments are expensive, inconsistent, and out of reach for many. This paper proposes frequency-based music therapy, delivered through an AI-powered mobile application called Querey, as a clinically grounded and non-invasive alternative. Querey is built around three components: a state-based discovery survey, an adaptive AI coach, and a mood stimulation engine rooted in brainwave entrainment and BPM science [9]. Challenges included music licensing constraints, navigating App Store deployment as a first-time developer, and maintaining data accuracy across the personalization pipeline. Experiments showed a mean satisfaction score of 7.5 out of 10 for BPM accuracy, with calming prescriptions outperforming energizing ones, and a twelve-week clinical trial comparing Querey against live therapy and passive listening across diagnosed populations [1]. When technology is designed around how the brain actually works, the results speak for themselves.
This project develops a Unity-based simulation platform that helps students and beginner designers analyze vehicle aerodynamics without expensive wind tunnel testing [6]. Air resistance plays an important role in vehicle performance, but traditional aerodynamic testing methods require costly equipment and complex professional software. To address this problem, the project introduces an interactive simulation environment where users can upload 3D car models, select or upload maps, and simulate airflow conditions [7]. The system is built around three main components: Properties, CFD simulation, and Map. These components work together to control inputs, generate aerodynamic visualization, and manage user interaction. The CFD system uses a simplified real-time approach based on dynamic pressure and precomputed surface responses to provide stable and interactive feedback. During development, challenges such as path design, model scaling, and collision detection were addressed through physics adjustments and automated systems. Experimental evaluation compared the system against ANSYS Fluent, showing an average error of 5.5%, indicating that the platform provides reasonably accurate results while maintaining high performance and accessibility.
