AI-based stroke prediction system using body motion biosignals during walking

• Published in: The Journal of supercomputing 2022-04, Vol.78 (6), p.8867-8889
• Main Authors: Yu, Jaehak, Park, Sejin, Ho, Chee Meng Benjamin, Kwon, Soon-Hyun, cho, Kang-Hee, Lee, Yang Sun
• Format: Article
• Language: English
• Subjects: Acceleration; Advancements in Mathematical Models for Adversarial Machine Learning; Algorithms; Compilers; Computer Science; Decision trees; Deep learning; Disabilities; Gait; Health care facilities; Human motion; Interpreters; Machine learning; Older people; Prediction models; Processor Architectures; Programming Languages; Sensors; Signs and symptoms; Walking
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-021-04209-1

As the world population continues to age, chronic diseases are on the rise. One of these diseases is stroke, which is a dangerous disease that can lead to many social and economic difficulties. Strokes can cause persistent neurological sequelae and physical disabilities; in some cases, motor function of the upper or lower body may be impaired, resulting in abnormal walking patterns or a loss of walking ability. These differences can be captured through walking patterns and gait information. There is therefore a need for research examining systems that can quickly detect signs of stroke based on human biosignals collected during daily life, such as motor function and the walking speed of the upper and lower limbs. By accurately predicting the early symptoms of stroke diseases, neural damage can be reduced with timely visits to medical institutions for treatment. In this paper, we designed and implemented a new AI-based system using real-time motion data for predicting stroke in the elderly. Initial data were collected from elderly Koreans while walking with attached wearable sensors. The sensors were placed on both shoulders and quadriceps. The data were processed, and we obtained a total of 12 motion attributes (angles and acceleration information). Predictive models using machine learning and deep learning algorithms were then constructed. The performance of the proposed system was verified with high prediction accuracies of 98.25% for the C4.5 decision tree model, 98.72% for RandomForest, 96.60% for XGBoost, and 98.99% for long short-term memory (LSTM). Hence, this paper provides a method for quickly and accurately predicting the early onset of stroke based on motion attribute values obtained while walking.