INTRODUCTION

Stroke has been the leading cause of long-term or permanent disabilities in adults. One of the common impairments left after stroke is weak limbs. These weak limbs or trunk can further hinder patients from walking and from returning to their functional status. The application of clinical assessment scales on stroke patients has posed a significant impact on rehabilitation. These items that patients perform poorly are the aspects that they need further training. Besides, the difference between sequential tests is a simple method to monitor patients’ training effects. Although most of these scales are easy to be administered in clinical settings, clinical experts cannot get all movement data from a single test. Along with these neuromuscular testing results, the kinematic analysis provides reliable and numeric data for clinical consultants to understand how neuromotor disorder affected stroke patients’ abilities. Today, mechanical engineers can embed many sensors into a small module, suitable for wearable devices, to gain movement data from human motion due to the advance of microelectromechanical systems. To establish reliable and accurate algorithms for estimating walking kinematics from these compact devices has been one of the major preoccupations of medical device research in the past decades [13]. Although these devices have applied to human motion analysis over the years, only recently has some researchers point how these devices can help clinical assessment scales [4].

Walking tests in stroke patients can give clinical experts many significant functional statuses and even the prognostic predictors. It is a feasible, intuitive, and reliable method in clinical units to obtain functional recovery status in post-stroke patients [59]. Over a few decades, there has been much research documenting how sarcopenia affects walking speeds. In short, walking speeds are one of the criteria to diagnose sarcopenia. However, walking speeds among stroke patients are also affected by weak limbs. It is, therefore, straightforward to conclude that stroke patients have a higher risk of getting sarcopenia [10]. Several methods have been proposed to obtain kinematics through wearable devices nowadays [11]. They are promising but challenging, such as obtaining accurate spatiotemporal data, integrating with several sensors, and other transmission issues[12]. Applying wearable devices, primarily composed of inertial measurement units in patients with neurological disorders, exist in some estimating errors in spite of their excellent performance in healthy participants [13]. One of the debating issues is integrating errors from inertial measurement units. Saremi and Marehbian note that walking speed and temporal data is less reliable in stroke with slow walking speeds (<0.5 m/s) [14].

To sum up, the walking speed test is a clinically useful tool to evaluate patients’ functional status but there still exist kinematics that has impact on walking speeds. By using IMUs, there still exists challenges. There have been several advantages and disadvantages when either adopting wearable devices alone or clinical assessment scales alone. Another attractive topic is where and how many wearables needed to obtain enough kinematics for clinical application. Integrating wearable devices with clinical assessment scales should be the intuitive and workable method to overcome those shortages. The purpose of this research focuses on setting up a wearable system and managing to integrate clinical walk tests. This hybrid method is supposed to acquire kinematics among walkable stroke patients through wearable devices and to determinate the most critical location to provide additional data during walking tests.