## 1 Introduction  
The physical mobility of an individual is one of the indicators of quality of life. A decline in mobility due to aging or other physical/mental disability has been linked to a surge in chronic illness and mood changes, amongst others [1]. The bulk of the research touching on mobility has been directed towards older adults since loss of mobility is prevalent with age. Physical mobility challenges lead to dependence and costly healthcare attention [2, 3]. From this, it is extremely important to identify mobility problems as early as possible, to ensure remedial actions are taken early enough. This is usually achieved using gait analysis. Gait is a term to describe an individual's pattern of walking. A gait cycle can be categorized into two different phases, as shown in Figure 1. The sub-categories in stance phase is initial contact, loading response, mid stance, terminal stance, and pre-swing. Swing phase consists of initial swing, mid swing, and terminal swing. Hence, the gait cycle starts when the heel of one foot is in contact with the ground and ends when the heel of the same foot is again in contact with the ground. By observing the gait cycle, parameters such as step time, swing time, step length and width, weight distribution, cadence, etc., can be obtained and utilized for clinical analysis to identify and isolate gait disorders. To identify gait deviations or to make a diagnosis for appropriate therapy, acquiring the spatiotemporal gait parameters is necessary [4]. Stance time, swing time, and step length are several parameters that are frequently studied. By focusing on the detection of foot contact with the ground, also known as single-leg support, other spatiotemporal gait parameters are readily obtained. According to the authors in [5], gait disorders and falls are largely underdiagnosed and often receive inadequate evaluation. The causes of gait disorders may include neurological conditions, orthopedic problems, and medical conditions. In order to evaluate the gait disorders, careful clinical observations of gait-related neurological functions should be regularly analyzed, as early as possible. Axial motor symptoms are usually less well documented in medical reports than other parts of the neurological examination [5]. One of the commonly reported neurological gait disorder is freezing of gait (FoG). Tracking of the occurrence of the disorder has been shown to be a challenging task for clinicians [6; 7]. On the other hand, if the information leading to development of the disorder can be availed through self-reported devices or clinician reports, optimization of therapy, which has been proven to be beneficial, can be properly adopted. There are two approaches to evaluate the presence of gait disorders: gait testing using appropriate devices or by response-based evaluation by patients through questionnaires. Gait analysis using devices gives objective results to assist the clinician in making an accurate diagnosis. As an example, to predict the risk of fall and mobility performance in elderly adults, maximal step length has been reported to have this ability, and is easy to measure in clinical settings [8; 9]. It is reported that older adults with a history of falls use stepping responses more frequently than non-fallers. Particularly, when having to change leg or direction, a slowed voluntary stepping is observed [10; 11; 12]. With the problem at hand, accurate diagnosis is principally tied to the quality of the detection of gait parameters. As such, manufacturers have focused on the development of a cost-effective gait measurement device. In the 21 st century, democratization of healthcare and telemonitoring trends is pushing the needs for accessible/wearable measurement devices and approaches utilizing recent technological feats [13]. To this end, a lot of research has been focusing on developing a small and low-cost device that is reliable enough to measure the gait parameters accurately [14; 15; 16; 17].