# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 1 Introduction
In the problem of robotic terrain classification, the performance of the proposed methods depends on the appropriate navigation strategy, machine vibration and obstacles in the path. Although these approaches work well, their accuracy is constrained due to several challenges. For example, in the case of visual sensor, the motion, occlusion and change of appearance in visual data due to varying illumination conditions cause degradation in performance. Similarly, the robotics- and autonomous off-driving-based approaches have trade-off in terms of accuracy, cost, and restrictive ambient conditions [3]. Thus, owing to these aforementioned issues, the problem of reliably classifying terrain types while maintaining the accuracy with a low-cost solution is a highly challenging task. Moreover, there is a need to explore the alternative approach against traditional terrain classifications. For instance, it is well known that human beings can capture information about terrain during walking by sensing it with their feet and by the sound of their footsteps [22]. The kinematic properties of the human motion pattern allow capturing the motion data for gait analysis, which in turn has been used as a reliable source for activity recognition [23] and estimating soft biometrics including gait-based age estimation [24; 25], gender classification [24; 26], emotion recognition [27] and human authentication/identification [28; 29]. Moreover, with the ubiquitous availability of modern devices such as smartphones and wearables are typically equipped with many sensors. For instance, on-board/embedded inertial measurement units (IMUs) consisting of tri-axial accelerometers, tri-axial gyroscopes and tri-axial magnetometers are able to provide the inertial data (i.e., 3D acceleration and angular velocities with acceptable low noise rate) at no additional cost. Furthermore, together with such sensors, these smart devices are usually equipped with powerful processors capable of performing high computational tasks and thus potentially can capture and analyze inertial data without compromising the normal use of the device. This has potentially created many opportunitiesto solve real-life problems [30] including soft biometrics classification [31], reconstruction of human motion [32], and measuring physical health and basic activities. Use of smart phone inertial data for terrain classification has not yet been extensively explored. To the best of our knowledge, there exist only a few methods that utilize inertial data of human gait for terrain classification [33; 34]. Table 1 highlights the main approaches in terrain classification. In this context, this paper proposes a novel terrain classification framework that uses tempo-spectral features extracted from inertial data collected with a smart phone. The extracted features are used to train machine learning classifiers (support vector machine and random forest) and predict terrain types. More precisely, the gait patterns of normal human walk over six different terrain types with variations in hardness and friction were recorded with inertial sensors. The following are the main contributions of the proposed approach:  
* We collected gait data of 40 healthy participants using body-mounted inertial sensors (embedded in smartphones) attached on two body locations i.e., chest and lower back.The data were collected on six different types of terrains: carpet, concrete floor, grass, asphalt, soil, and tiles (as explained in Section 2.4). The data can be freely accessed by sending email to the corresponding author. * We propose a set of 194 saptio-spectral hand-crafted features per stride, which can be used to train different supervised learning classifiers (random forest and support vector machine) and predict terrains. The prediction accuracy remained above 90% for terrains under different classes such as indoor-outdoor, hard-soft, and a combination of binary, ternary, quaternary, quinary and senary terrain classes (details in Sections 2.4 and 3).