## 1 Introduction  
Osteoporosis is a medical condition distinguished by a reduction in bone mineral density. It can be coupled with a decline in muscle mass and an increase in the deposition of adipose tissue. These physiological alterations can influence an individual's gait and equilibrium, increasing their vulnerability to falls and fractures [1]. The decrease in bone density in osteoporosis causes injury to the hip joint by exposing it to heightened stress levels. This phenomenon is notably prevalent in postmenopausal females. The increased stress makes seemingly healthy hip joints susceptible to osteoarthritis through perturbations to the typical patterns of gait and balance [2]. While conventional methods for assessing gait disturbances and fear of falling in persons with bone diseases exist, they often necessitate manual patient evaluations, which can be cumbersome for both the patient and the clinician. For instance, the dual cognitive task test can gauge gait abnormalities in individuals afflicted with osteoporosis and a fear of falling [3], while the ten-meter test assesses the impact of training programs on balance, gait velocity, and muscle strength in people with osteoporosis [4]. However, these manual assessments mandate specialized testing facilities and trained therapists to administer them, rendering them impractical in various settings. To address these challenges, alternative automated systems have emerged to conduct gait analysis and assessments [5]. These systems encompass a wide array of technologies, including motion capture systems [6; 7], and video cameras [8]. While undeniably effective in suitable contexts with the requisite infrastructure and trained personnel, these methods are not universally accessible or convenient. Another avenue involves the utilization of sensors, such as inertial measurement units (IMUs) [9], wearable sensors [10], or insole foot pressure sensors and accelerometers [11; 12]. These sensors offer the advantage of portability and can be employed in diverse environments. However, they necessitate attachment to specific body locations, which may not always be convenient or comfortable for the user. A promising solution to these challenges lies in the integration of gait-monitoring sensors into mobility aids commonly used by individuals, such as rollators [13; 14; 15], and canes [16; 17]. This innovative approach ensures that gait analysis is accessible irrespective of location or time, without requiring specific body attachments. Nonetheless, it is essential to note that this method may yield approximated gait information and, consequently, is susceptible to a margin of error compared to more intricate systems [18]. Moreover, the current focus primarily gravitates towards rollators, often equipped with wheel encoders to estimate the distance covered. However, an often-overlooked segment of the population consists of users with pronounced instability, rendering rollators impractical and even hazardous during routine activities or gait monitoring. Consequently, non-wheeled walkers emerge as a pertinent alternative in facilitating gait analysis for individuals grappling with high instability and an aversion to the risks associated with traditional rollators. Numerous studies have been conducted to analyze gait through different techniques. The four common techniques including the use of a cane, robotic walker, pick up standard walker, and smart walker/rollator are presented in Table 1.