## 1 Introduction  
Static and dynamic balance involves complex processes integrated by the central nervous system (CNS) through vestibular, visual, and proprioceptive systems, which coordinate adjustments in order to maintain control of the center of mass (COM) within the base of support during static [1] or dynamic balance [2]. These mechanisms can be affected by changes in the visual, vestibular, or proprioceptive systems [3], such as those that occur in the aging process in conditions such as sarcopenia and osteoporosis [4], which contribute to a loss of body support. Currently, there is an interest in methods to quantify balance quantitatively in clinical settings. Therefore, microelectromechanical systems (MEMS) of cell phones have been employed. MEMS are miniature sensors, such as accelerometers and gyroscopes, that obtain inertial variation data, allowing the registration of body movement during static balance or motor execution. They are embedded in modern smartphones, allowing users to access registered data using existing applications [5]. Such applications have been used in papers on balance control [6], gait changes detection in people with Parkinson's disease [7], and the assessment of risk of falls in the elderly [8].  
During the data acquisition, interferences may occur generating signal noise (i.e., the vibration generated by a car when passing near the experiment location, even the subject's respiratory chest movement, among others). Noises are unwanted signals that interfere with or distort the desired signal, which can generate erroneous readings, making the results uncertain and inaccurate [9]. Noise will always be present in any data acquisition; however, it is possible to minimize its presence by using some kind of filter to allow for a more consistent data assessment. Therefore, the filters were created to minimize the influence of noise on the recorded data. [PERSON] (1903-1987) and [PERSON] (1894-1964) created the basis for estimation theories that were later used to develop the theory of data prediction, filtering, and smoothing [10]. Since then, filters have been used to smooth the presence of noise, making data more reliable by reducing the signal present at unwanted frequencies [11].  
Figure 1 presents the raw and filtered data of a healthy male volunteer performing a timed up-and-go test (i.e., commonly used to assess the risk of falls in the elderly population). The accelerometry data were recorded using a triaxial sensor (MetaMotionCTM, MBIENTLAB-INC) attached to the volunteer's lumbar region. In this image, the raw data (A), the Butterworth filter (B), and the Kalman filter (C) are displayed.  
These filtering processes are better described in papers using surface electromyography, force platform, and kinematic recordings, and still need to be better described when using mobile devices. Knowing the existing filters and standardizing the signal processing is important because it enables scientific reproducibility. This survey would help researchers and clinicians who are interested in working with the signals from sensors embedded in smartphones, since knowing the signal processing is an important step in obtaining reliable results. The objective of this paper was to perform a systematic review of the filters used in data recorded from smartphone applications for the assessment of static and dynamic balance.  
Figure 1: Timed up-and-go test inertial recordings: (**A**) raw data (without filters), (**B**) data with the Butterworth filtering process, and (**C**) data with the Kalman filtering process.