Using Smartphone Inertial Measurement Unit for Analysis of Human Gait

Using Smartphone Inertial Measurement Unit for Analysis of Human Gait

Sergey Reginya (Nanoseti LTD, Petrozavodsk State University, Petrozavodsk, Russia), Alexander Yu. Meigal (Petrozavodsk State University, Petrozavodsk, Russia), Liudmila I. Gerasimova-Meigal (Petrozavodsk State University, Petrozavodsk, Russia), Kirill Prokhorov (Petrozavodsk State University, Petrozavodsk, Russia) and Alex Moschevikin (Nanoseti LTD, Petrozavodsk State University, Petrozavodsk, Russia)
DOI: 10.4018/IJERTCS.2019070107


The study was aimed at searching the characteristic features of a human gait during a conventionally used neurologic walking test with the help of sensors (3D accelerometer and gyroscope) in a smartphone mounted on the person's head. This allowed reducing the amount of analyzed data and saving time for analysis in comparison with motion video capture methods. It has been found that merely one inertial unit is good enough to detect the gait left-right asymmetry in healthy subjects. Several parameters were derived, one of them (acceleration shock) proved to be the most informative one. The gyroscope signal allowed detecting the characteristics of the gait in Parkinson's disease patients seen as the excess of the power spectrum density in all three axes. Therefore, this study was aimed to extract amplitude and spectral parameters from acceleration and rotation rate signals of smartphone-based IMU attached to the head during the long version of the TUG test in a group of neurologically healthy young subjects.
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1. 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).

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