An artificial intelligence model for smart post-stroke assessment using wearable sensors

Wearable sensors provide a recording capability of the body segments' motion. By collecting and analyzing the segments' movements, automating the post-stroke assessment process would be feasible and reliable. Post-stroke classification of impaired or non-impaired body parts is the first step of automating the assessment process. It is recently achievable by utilizing enhancements in supervised machine learning (ML) using wearable sensors. Many classification-based approaches have numerous complexity/quality trade-offs with no optimal solution for all datasets of various structures, properties, distributions, and sizes. Ensemble machine learning provides reliable classification systems for diagnostic purposes. However, there are no criteria for identifying the baseline models in the aggregate (parallel or sequential aggregation), which significantly impacts the complexity and accuracy of the designed ensemble. In this paper, we propose an efficient post-stroke diagnosis system using the notion of multi-level ensemble learning, the Multi-Level Meta Learner (MLML) ensemble algorithm with heterogeneous or homogeneous baseline classifiers using Xsens wearable sensor collected dataset. The MLML outperforms the baseline models with comparable performance while generating a list of optimal candidate heterogeneous baseline classifiers. This list can provide only potential positive combinations, significantly improving the performance and reducing the complexity of building an ensemble. The linear acceleration and angular velocity derived from the wearable sensors are utilized. Experimental results show that the MLML has enhanced the accuracy of classifying the impaired hands from non-impaired hands with an improvement of up to 95.64% and 91.2% for boosting and Bagging learning, respectively.