## 1 Introduction  
According to the 2015 EU Ageing Report, the percentage of citizens aged over 65 years is predicted to rise from 18% to 28% by 2060. One of the greatest challenges facing the healthcare of the aging population is frailty. In Europe, 15% of older people aged 65 and over are considered as frail [1]. Frailty is defined as a syndrome of decreased reserve and resistance to stressors, resulting from cumulative declines across multiple physiologic systems and causing vulnerability to adverse outcomes, such as worsening mobility, disability, incident falls, hospitalizations, and mortality [2]. There is much potential for frailty to be reversed, particularly in its early stages [3; 4; 5]. For that reason, the early identification and management of frailty is an important priority for both healthcare providers and healthcare policy makers [6; 7; 8].  
One of the most accepted definitions of frailty is the classification proposed by [PERSON] et al. using five criteria: weight loss, exhaustion, inactivity, slow gait speed, and weakness [2]. However, its use may have limited feasibility and reliability in a routine care setting [9; 10]. The criteria of weight loss, exhaustion, slowness, and low energy expenditure are usually self-reported measures and may be prone to bias [9; 11; 12]. An objective frailty screening tool may be more appropriate for routine assessment.  
Slow gait speed has been reported as the most easily identifiable feature of physical frailty [2; 13]. Moreover, slow gait speed is a good predictor of future physical disability, falls and fractures, requirement for a caregiver, hospitalizations, and death [13]. Gait speed evaluated by the 4-m walking test could be used for frailty screening [14]. Regarding recent studies, other temporal-spatial gait characteristics may have a strong association with frailty [15; 16]. A systematic review reported that frailty is associated with some temporal-spatial gait parameters beyond speed, including high stride time variability, reduced step length, and increased double support time [15]. However, most studies are conducted in the laboratory environment using camera systems [17; 18], force platforms [18], or computerized walkways [19] that, due to the high costs related to acquisition and application in everyday clinical practice, are not available in every healthcare institution. Therefore, more accessible alternatives could be used, such as wearable sensors, which are easier to use, more practical, and require fewer resources. There is growing scientific evidence that gait assessment using wearable sensors could be used for prefrailty and frailty screening [20; 21; 22; 23]. Simple and easy-to-use sensors (such as those incorporated into a smartphone) could be used to investigate gait at home and offer advantages in frailty evaluations where the full application or interpretation of Fried criteria is impracticable. There is currently a lack of data regarding which gait indicators are sensitive and specific for determining frailty and the appropriate thresholds for distinguishing between prefrailty and frailty.  
The purpose of this study was to examine the ability of wearable sensor-based assessments of gait to discriminate between frailty levels (robust, prefrail, and frail). Firstly, we sought to determine which sensor-derived gait parameters are able to discriminate between the three frailty levels. Secondly, we aimed to determine the cut-offs of the most sensitive gait parameters that separated the frailty levels. We hypothesized that we could separate the frailty groups (robust, prefrail, and frail) using a sensor-based assessment of gait. We also hypothesized that frailty is associated not only with slow gait speed, but also with longer stride time, stance phase, and double support time.