## 1 Introduction  
With the evolution of the technological world, remote sensing has secured an indispensable role in multitudinous fields. It enables researchers to get a huge amount of data to design smart applications, and in many cases, it does not even cause any disturbance in the environment. One of the remote applications that have caught the attention of researchers is remotely sensed human locomotion activity recognition (RS-HLAR) [1; 2; 3; 4; 5; 6; 7]. Some examples of human locomotion activities are walking, sitting, jogging, jumping, climbing stairs, coming down the stairs, standing, and running. The accurate recognition of these activities can assist the design process of a variety of applications that are related to smart homes [8; 9; 10; 11], life logging [12; 13; 14; 15; 16; 17], indoor localization [18], healthcare [19; 20; 21], rescue [22], fitness [23], surveillance [24], and entertainment [25]. As an exemplary scenario, let us consider that a person is in a huge and crowded shopping mall and his location is to be identified. This task that seems to be arduous can be easily taken care of, if we remotely track the person's locomotion activities and record them with their timestamps. When talking about remote devices, smartphones are second to none. Present-age smartphones have become very powerful by virtue of the sensors that are embedded in them [26]. Another quality that makes them eminent is that they are ubiquitous. With these features, smartphone technology proves to be one of the best means to design remote applications. Types of smartphone sensors that have been used for remotely sensed human locomotion activity recognition (RS-HLAR) include inertial sensors and proximity sensors [27; 28]. A typical inertial sensor that is used for HLAR is an accelerometer, which measures the force along the \(x\), \(y\), and \(z\)-axis [29; 30; 31]. However, while using the smartphone-embedded sensors, the significant issue of the position and orientation of the smartphone arises as every individual has a preference of carrying their smartphone either in their hand or in pocket or a bag. This issue is resolved by adding some assistive sensors to the framework. An accelerometer combined with a gyroscope that measures the angular velocity about the \(x\), \(y\), and \(z\) axes and a magnetometer that gives the magnetic field intensity in the \(x\), \(y\), and \(z\) directions resolves the position and orientation issue and makes the system user-independent [32]. In addition to these, other assistive sensors can also be added to the system to enhance its accuracy. Despite all these advances, several challenges are also faced in RS-HLAR. In the wireless transmission of the sensor signals, there is a large possibility of noise intrusion and even complete distortion of the signal in the worst case. In such cases, the identification and classification of the performed locomotion activity becomes burdensome. Another challenge is the compound and complex locomotion activities. These are the activities that are composed of two or more individual activities, e.g., a person has fallen when they were trying to sit on a chair. The proposed system tackles the mentioned challenges in an efficient way and produces better results than those of the available state-of-the-art (SOTA) methods. For this article, we propose a comprehensive and a robust model Smartphone Sensors Remote Data classification (SSRDC) for RS-HLAR while working in a remote sensing environment. We adopted three challenging datasets that provide real-life-depicting data of human locomotion activities. Every dataset provides different locomotion activities' data while using a distinct combination of various smartphone sensors including accelerometer, gyroscope, magnetometer, global positioning system (GPS), light sensor, and microphone. We introduced an efficient blend of features including Parseval's energy, time-domain statistics, and frequency-domain statistics.