# Usability Test for an Over-Ground Walking Assistance Robotic Device Based on the Mecanum Wheel
## 1 Introduction  
Stroke is a neurological condition caused by the obstruction of cerebral blood flow or damage to cerebral blood vessels. As of 2022, more than 630,000 stroke cases have been reported in South Korea [1]. Post-stroke gait disturbances often include asymmetric gait patterns, impaired balance, and reduced weight-bearing on the paretic limb, leading to functional disability and limited ambulation [2, 3]. Gait deficits cannot be improved solely with pharmacological treatment; therefore, neurologically focused physical therapy is required [4]. Traditionally, therapists have employed manual assistance or body weight-supported treadmill training to improve gait. However, these methods have limitations, such as a high physical burden on therapists and insufficient precision or repeatability [5]. Robotic gait assistance systems have been actively developed [6]. Robot-assisted walking therapy has shown promise in aiding patients to walk longer distances and improvingendurance, balance, and confidence [7]. A recent Cochrane systematic review reported that combining electromechanical body weight-supported gait devices with physical therapy can significantly enhance the independent walking ability in patients with stroke [8]. Currently, the most commonly used robotic system in clinical practice is exoskeleton-assisted treadmill walking. However, treadmill-based walking does not fully replicate over-ground ambulation, particularly concerning speed adaptation, directional changes, or sensory stimuli from ground contact, factors essential for community reintegration following discharge [9; 10]. To address this, over-ground walking assistance robots with body weight support harnesses and motorized tracking capabilities have been developed [11]. One representative example is Andago, a commercial device featuring a mobile support frame and dynamic weight support system [12], which enables patients to walk while preventing falls and reducing physical stress. Previous studies have demonstrated its effectiveness in improving inter-joint coordination during walking in children with gait impairments [13], and reduced fear of falling of stroke patients [12]. Over-ground walking devices provide realistic gait training conditions that promote dynamic balance strategies, and the functional adaptability required for daily mobility [13]. These systems reduce the burden on therapists while enabling quantitative and repetitive training [6; 11]. Consistent with global trends, South Korea has actively developed robotic gait trainers. The over-ground walking assistive robot based on the mecanum wheel (OWAR-MW) is a Korean-developed prototype that offers the functionality of Andago with added omnidirectional mobility. The OWAR-MW recognizes user intentions, provides partial body weight support, and tracks movement in multiple directions using mecanum wheels, allowing forward, lateral, diagonal, and rotational movements. However, the OWAR-MW remains in the prototype stage, and prior to commercialization, its safety, operability, and usability must be rigorously evaluated using user-centered testing [14]. In addition, technical refinements should be guided by specific challenges identified through comprehensive user experience analysis [15]. [PERSON] et al. (2023) emphasized that the success of wearable systems in clinical ergonomics depends not only on accurate sensor feedback but also on non-intrusiveness, comfort, and intuitive operations, which contribute to high usability [16]. Similarly, in walking-assistive robots, patients' and clinicians' feedback on ease of use, stability, and comfort is essential for translating prototypes into clinically viable tools. Additionally, evaluating usability through structured assessments involving both patients and therapists can provide key insights for refining device design [17]. Therefore, this study assessed the usability of the OWAR-MW compared to commercial Andago through satisfaction surveys and in-depth interview.