Wearable devices have long been praised as the future of healthcare, fitness tracking and athletics. However, the reliability of these devices has been hampered by the presence of motion artifacts, which greatly diminish the quality of data collected.
Wearable devices have long been praised as the future of healthcare, fitness tracking and athletics. However, the reliability of these devices has been hampered by the presence of motion artifacts, which greatly diminish the quality of data collected.
The Tanenbaum Institute for Science in Sport (TISS) and the Data Sciences Institute (DSI) at the University of Toronto are pleased to award a Catalyst Grant for research in the Development of Convolutional Neural Network for Motion Artifact Mitigation in Wearable PPG Devices. Co-led by Professors Daniel Franklin (Institute of Biomedical Engineering, Temerty Faculty of Medicine, University of Toronto) and Chris McIntosh (Department of Medical Biophysics, Temerty Faculty of Medicine and University Health Network), the project aims to revolutionize athletics and sports medicine by integrating novel sensors with advanced machine learning algorithms.
The researchers propose a novel approach to overcome motion artifacts in wearable devices by enabling real-time motion artifact cancellation in optical wearables. This includes the development of a multimodal sensor coupled with deep learning models. The sensor will combine force and multiwavelength optical measurements to capture relative motion at the sensor interface, addressing a critical limitation of current wearable devices.
“Conventional wearable devices capture global motion, but our approach focuses on capturing relative motion at the sensor-skin interface, which is crucial for accurate data interpretation. Wearable technologies offer a unique glimpse into patient function and biology outside of episodes of care. If AI is the present, wearables with AI are the next frontier,” says Professor Daniel Franklin.
The research project will progress through several phases, including controlled lab experiments and real-world examples of motion. By collecting a novel multi-modal motion artifact dataset, the team aims to develop a robust algorithm for real-time optical motion artifact cancellation.
“We are thrilled to partner with the DSI to award this seed grant. This project has the potential to significantly advance wearable health monitoring technologies for applications to the healthcare, fitness, and sports sectors,” says Ira Jacobs, director of the Tanenbaum Institute for Science in Sport and professor at the Faculty of Kinesiology and Physical Education (KPE).
The implications of this research extend beyond healthcare into consumer health, sports, and athletics. By enhancing the usability and interpretability of wearable device datasets, the project promises to advance remote health management and athletic performance tracking.
“We envision a future where wearable devices provide more accurate and actionable insights, leading to improved patient care and athletic performance,” added Professor McIntosh.
The TISS and DSI partner to co-sponsor Catalyst Grants focused on innovative and novel data science in sport and sport analytics.