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Top1. Introduction
Free and safe moving provides an individual spatial mobility, communication, access to services, and employment. Therefore, locomotion is critical for personal independent living and, ultimately, for social adaptation. In contrast, impaired motility caused either by aging, traumas or neural-motor disorders diminishes the social adaptation and increases economic burden on society. Also, impaired walking and body imbalance often provoke falls and traumas (Montero-Odasso et al., 2005) with potentially lethal outcome. Therefore, prediction and further prevention of such motor deficits would be aimed to struggle the problem of social de-adaptation. As such, accurate description of human locomotion is likely to be helpful to mark the most informative features, which point on a motor disorder.
To date, two methods are generally accepted as gold-standard for the field of motion/gait analysis: 1) motion video-capture, and 2) dynamic (instrumented walkways) (Schlachetzki et al., 2017). These methods are acknowledged as accurate and reliable, and provide numerous metrics that allow fully describing the gait characteristics. However, these two methods are limited to either laboratory or hospital setting, are costly and rather complex in use (Galán-Mercant, Barón-López, Labajos-Manzanares, & Cuesta-Vargas, 2014; Schlachetzki et al., 2017).
In recent years, novel technologies such as mobile communications, cloud computing, advanced analytics, and the Internet of Things (Espay et al., 2016), foot pressure sensors (“smart shoes”), and wearable inertial sensors (3-axial accelerometer, gyroscope, magnetometer, electrogoniometer, inclinometer) (Fukui, Ifuku, Watanabe, Shimosaka, & Sato, 2015; Hubble, Naughton, Silburn, & Cole, 2015; Sprager & Juric, 2015; Vienne, Barrois, Buffat, Ricard, & Vidal, 2017; Anwary, & Vassallo, 2018) attract globally growing attention for gait event analysis due to their reliable accuracy and relatively low costs (Sprager et al., 2015). These sensors are often applied to characterize motion function in Parkinson's disease (PD) patients (Hubble et al., 2015; Vienne et al., 2017). Many smartphones are equipped with inertial measurement units (IMU) that usually include an accelerometer, a gyro sensor and a magnetometer. Therefore, smartphones are already applied to measure tremor, gait and movement characteristics in humans (Barrantes S. et al., 2017; Galán-Mercant, Barón-López, Labajos-Manzanares, & Cuesta-Vargas, 2014; Silsupadol, Teja, & Lugade, 2017; Bastas, Fleck, Peters, & Zelik, 2018; Proessl, Swanson, Rudroff, Fling, &Tracy, 2018).