Real-World Challenges for Long-Term Wearable Technologies

The use of wearable technologies and mobile digital health in neurosciences has shown great potential in advancing our understanding of brain function and improving the diagnosis and management of neurological conditions. However, there are significant challenges that must be addressed before these technologies can be widely adopted in clinical practice and in the real world. These challenges include data privacy and security, user acceptance and adoption, accuracy and reliability, and robustness against real-world artifacts and variability. Moreover, device miniaturization has improved the portability of wearable devices; however, it comes with reduced battery size, hence, there is an urge for wearable solutions that are energy efficient and consume low power to ensure a long battery lifetime. On the other hand, machine learning and deep learning algorithms have proven to be promising for accurate performance, but they are often too resource-demanding for small wearable solutions. The next-generation wearable devices should not only maintain an unobtrusive and non-stigmatizing form factor but must also be able to accurately extract useful information despite real-world artifacts and variability by leveraging advanced AI algorithms.

The goal of this research topic is to provide a comprehensive collection of the current research efforts in advancing neuroscience through wearable devices and overcoming the real-world challenges faced during this process. We welcome the submission of original research papers, reviews, mini-reviews, and opinion articles.

Potential interests include but are not limited to the following areas:

- Software/hardware co-design of accurate and efficient algorithms for wearable devices
- Robust algorithms against real-world non-ideality, e.g., artifact detection or artifact removal
- Sensor fusion for accurate digital biomarker and artifact detection/removal
- Real-time detection of a neurological condition requiring healthcare attention, e.g., epileptic seizures
- Wearable solutions for digital biomarkers in neurological conditions such as major depressive disorder
- Wearable solutions for mental state recognition