Respiratory diseases, such as pneumonia, asthma, and chronic obstructive pulmonary disease (COPD), are major global health concerns. Digital stethoscopes, which help doctors remotely monitor lung sounds, face limitations in data storage, transmission, and computational capabilities. This challenge focuses on advancing respiratory sound compression algorithms and event detection models to improve the efficiency of digital stethoscopes. The aim is to design algorithms that can automatically compress respiratory sounds while maintaining high quality and detect critical events like wheezes or crackles. This would enhance early diagnosis and streamline healthcare delivery, especially in remote settings.

Body-Coupled Communication (BCC) utilizes the human body as a medium for data transmission, offering significant advantages in health monitoring applications. The challenge track focuses on designing a fully digital transceiver based on Edge-Coded Signaling (ECS), which allows for communication over a single wire, ideal for devices with limited IO capabilities. Participants will develop efficient, low-power transceivers that adhere to strict timing and hardware constraints, with applications in wearable devices for continuous health monitoring. This challenge aims to push the limits of communication systems that operate seamlessly within the body's network.

Pediatric leukemia remains a significant health challenge, with early diagnosis being crucial for effective treatment. This challenge invites participants to develop advanced AI and machine learning algorithms to improve the accuracy of pediatric leukemia diagnosis. By leveraging clinical data such as blood tests and genetic markers, participants will work to create automated systems that can rapidly identify leukemia subtypes, enhancing diagnostic efficiency and accuracy. The goal is to improve outcomes for children by reducing diagnostic delays and supporting clinicians with reliable decision-making tools.

Motor control, particularly in individuals with paralysis or neurodegenerative diseases, remains a challenging frontier in neuroengineering. This challenge track focuses on the development of advanced neural decoding techniques to control motor functions through Brain-Machine Interfaces (BMIs) in non-human primates. Participants will design systems capable of interpreting neural signals to drive prosthetics, robotic limbs, or assistive devices, offering hope for patients with spinal cord injuries or conditions like ALS. The challenge aims to refine neural decoders, explore new algorithms, and contribute to the scalable deployment of BMIs in clinical settings.