# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 1 Introduction  
Terrain classification is an active area of research having a wide range of applications, e.g., outdoor terrain navigation, the recommendation of floor types for health care environments, sports flooring, consumer suggestion systems and autonomous driving [1; 2]. In the literature, several terrain classification approaches have been proposed based on different types of data (e.g., visual data, acoustic, physical touch, etc.) typically acquired using optical cameras, 3D laser scanners and on-board sensors mounted over humanoid robots [3; 4; 5], autonomous off-road driving vehicles [6; 7; 8] and aerial platforms [9]. The traditional camera-based approaches employ visual features to distinguish different terrain types. One of the earliest approaches was proposed by [PERSON] et al. [10] who performed terrain classification using automatic texture measure. [PERSON] et al. [11] also presented an algorithm that used visual features captured during the human walk. Similarly, [PERSON] et al. employed aerial image data for terrain classification to support off-road navigation of the ground vehicle using low-rank sparse representation. [PERSON] et al. [12] also used imagery data using aerial vehicle for ground robotnavigation. With the aim of terrain classification, [PERSON] et al. [6] classified different soil types using geographic object-based analysis on images. [PERSON] et al. [7] fused color information together with 3D scans obtained from LiDAR to perform terrain classification. Similarly, [PERSON] et al. [13] employed fused data acquired from a suite of sensors consisting of microphone, accelerometer, gyroscope, infrared and motor current to train a feedforward neural network for terrain classification. From a robotics perspective, [PERSON] et al. [3] proposed a small legged robot that used an array of miniature capacitive tactile sensors to directly measure ground reaction forces (GRF) and used them to classify terrains. [PERSON] et al. [14] also sensed ground forces using force/torque sensor for biomimetic hexapod robots walking on unstructured terrains. Similarly, [PERSON] et al. [4] described a tactile probe for surface classification utilizing mobile robots, with single-axis accelerometer. [PERSON] et al. [5] also addressed the issue of terrain perception and classification using noisy range data acquired via laser scanning and terrain mapping module in humanoid robots. [PERSON] et al. [15] performed a robotic acoustic-based terrain classification that exploited the sound waves originating from the terrain-vehicle interaction to build a spectrogram image that is fed to a convolution neural network to learn deep features for subsequent terrain classification. [PERSON] et al. [16] framed the problem of terrain classification as a semantic segmentation problem in which they employed deeplab to visually classify terrain type and then registered it with the slope angles and wheel slip data to generate a prediction model for Mars Rover mission. Similarly, for planetary missions, [PERSON] et al. [17] also analyzed the vibration patterns obtained via terrain-vehicle vibration to distinguish different terrain types. [PERSON] et al. [18] proposed a method for terrain classification based on a combination of speed-up robust features for an autonomous multi-legged walking robot. In the context of autonomous off-road driving, the terrain classification problem has been actively studied in the last decade. For instance, [PERSON] et al. [1] presented a technique for terrain classification and obstacle detection for autonomous navigation using single-axis lidar and a stereo pair of sensors. [PERSON] et al. [19] and [PERSON] et al. [20] also proposed terrain classification based on the vibrations generated by autonomous ground vehicles using a laser stripe based structured light sensor. Further, [PERSON] et al. [21] also performed terrain classification using rapid training of a classifier for autonomous air and ground robots.