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A portable electrochemical aptasensor integrated with machine learning was developed for rapid and on-site detection of Staphylococcus aureus (S. aureus) in food and beverage samples. The aptasensor was fabricated using screen-printed carbon electrodes (SPCEs) modified with gold nanoparticles (AuNPs) and functionalized with an Iron-regulated Surface Determinant Protein A (IsdA)-specific aptamer for the detection of S. aureus. Approximately 2,000 cyclic voltammetry (CV) data points were collected for six different food and beverage matrices spiked with varying concentrations of S. aureus (1, 10, 500, and 1000 colony-forming unit (CFU)/mL). Each CV scan was repeated 10 times, linearly averaged, and baseline corrected before model input. Noise filtering and normalization were performed to ensure consistent feature representation across training and testing datasets. Machine learning models, including Convolutional Neural Networks (CNNs) and Transformer architectures, were applied to classify the samples. The CNN model demonstrated superior performance, with a test loss of 0.0402 and a test accuracy of 99.21%. In contrast, the Transformer model achieved a test loss of 0.2014 and an accuracy of 94.21%. To enhance usability, an Android application was developed using the Network Enabled Technologies (NET) framework, enabling real-time inference of bacterial concentration directly from CV data on mobile devices (e.g. smartphones). This system demonstrates potential for a rapid, accurate, and scalable solution for real-world food safety monitoring. 

Treadmill running is a common workout for individuals across fitness levels. In this paper, we propose mm-RunAssist, a first-of-its-kind mmWave-based system that enhances treadmill workouts by monitoring respiration waveforms, running rhythm (i.e., coordination between breathing and strides), and detecting fall-off events. Extracting respiration from moving subjects using RF signals is challenging due to dominant motion artifacts. While prior deep learning efforts use adversarial or contrastive learning to mitigate such artifacts, they have been evaluated primarily under low-intensity activities like walking. To address this gap, mm-RunAssist introduces a Dual-task Variational U-Net that shares latent representations between respiration and upper-body movement tracking. This dual-task setup, guided by belt and depth sensors during training, improves reconstruction under intense body motion. Our system not only recovers fine-grained respiratory patterns during running but also supports cadence analysis through arm swing tracking. Extensive experiments with three state-of-the-art baselines under various conditions demonstrate mm-RunAssist's robustness and accuracy in treadmill running scenarios. Results show that mm-RunAssist advances RF sensing by effectively extracting vital signs even during vigorous body movements, offering new capabilities for fitness monitoring and non-intrusive health assessment. 

Accurate and real-time gait analysis is essential for enhancing performance and reducing injury risks in treadmill running. In this paper, we introduce VibRun, an unobtrusive gait analysis system that estimates key physiological metrics, such as cadence, ground contact time, stride time, center of pressure, and plantar pressure distribution, through footstep vibrations captured by low-cost treadmill-mounted sensors. Leveraging advanced multi-task transformer models, our system offers a robust, real-time solution to monitor and analyze running biomechanics without requiring intrusive wearable devices. This approach enables seamless integration into virtual sports, gaming platforms, and immersive exercise environments, enhancing the running experience by providing personalized feedback. By offering precise biomechanical insights in real-time, VibRun paves the way for future applications in virtual sports, gamified fitness, and interactive training programs, empowering users to engage more effectively in their training sessions while improving overall performance and reducing injury risks. Extensive evaluations with 17 participants across varied treadmill running scenarios demonstrate VibRun's accuracy in real-time gait analysis. For instance, VibRun achieves a mean error of 28.8 ms in ground contact time and a distance of 13.66 mm in the center of pressure, among other measured metrics, highlighting its precise performance across multiple gait parameters.