The Use of Artificial Intelligence and Wearable IMUs in Medicine: A Systematic Review (Preprint)

Abstract

BACKGROUND Artificial Intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with Wearable Inertial Measurement Units (IMUs), AI could transform healthcare again by opening new opportunities in patient care and medical research. OBJECTIVE This systematic review aims to evaluate the integration of Artificial Intelligence (AI) with Wearable Inertial Measurement Units (IMUs) in healthcare, identifying current applications, challenges, and future opportunities. The focus will be on the types of AI models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research. METHODS This systematic review examines this synergy of AI and IMU data by employing a systematic methodology, following PRISMA guidelines, to explore three core questions: Which medical fields are most actively researching AI and IMU data? Which AI models are being utilized in the analysis of IMU data within these medical fields? and what are the characteristics of the datasets used for AI training in this fields? RESULTS The median dataset size is of 50 participants, which poses significant limitations for AI methods given their dependency on large datasets for effective training and generalization. Moreover, our analysis reveals the current dominance of Machine Learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for Deep Learning models in this area. Impressively, 93% of the studies employed Supervised Learning, revealing an underutilization of Unsupervised Learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. Additionally, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underutilization of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts. CONCLUSIONS In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field towards the adoption of more complex Deep Learning models, and the expansion of the application of AI on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research.