## 1 Introduction  
Humans have access to a wide range of verbal and nonverbal cues from nature with which to communicate their emotions. Our emotions greatly influence our social relationships. Emotions are complex mental states that comprise subjective experience, pathological and physiologic responses, and other factors [1; 2]. They are consistent responses to events based on some stimuli, and the intensity of response varies from human to human. Human Emotion Recognition (HER) has several exciting application areas such as human-computer interaction to better understand and respond to human-centric events, affect-sensitive systems for autism therapy, chat-bots in call centres, in the entertainment industry, and so on. Such applications can potentially alter the way we interact with computers in our daily lives [3; 4; 5]. Humans can express emotions both verbally and non-verbally. The expression of verbal emotions is limited and typically indicated by known words, idioms, or phrases, whereas nonverbal emotions can be shown more diversely. Nonverbal emotions can be revealed through physiological indications, facial expressions, speech, gestures, etc. For many decades, psychologists and scientists have extensively explored the field of human sentiment analysis to understand human emotions [6; 7; 8; 9]. On the one hand, psychologists probe human emotions from the perspective of social psychology as \"emotions naturally emerge out of the core principles of evolutionary psychology\" [10]. However, scientists from other fields, such as the medical sciences, hardware design engineering, computer science, etc., investigate emotions to comprehend how the brain functions, to create new, improved hardware, and to create intelligent systems to assess, forecast, and identify human affective states. With the rapid advancement of consumer electronics, it is now feasible to capture human emotions using various technologies (including smart devices). This includes, but is not limited to, vision sensors to capture facial expressions or record human gait in the form of videos [11; 12], speech-based emotions recognition [13; 14; 15], electroencephalograms (EEG) to record the electrical activity of the brain [16; 17], and wearable inertial sensors to capture human gait data under different affective states [16]. Facial expressions, being the most prominent artefact of intimate emotions, have been extensively explored [18]. Although facial expression-based emotions prediction has improved significantly over time, environment and occlusion-invariant facial expression-based emotions recognition systems are still very challenging, especially given the COVID-19 situation where it is recommended to wear face masks. Moreover, recording facial data raises serious privacy concerns and hence cannot be used in practical applications. Furthermore, facial expressions are not trustworthy indicators of emotion since they may be manipulated and are not generated as a result of a particular feeling [19]. Wearable inertial sensors, such as smart fitness bands, smartwatches, smart in-soles, etc., have turned into a key enabling technology for a wide variety of applications. They are low cost, do not impose any environmental constraints, and have been widely used in human gait analysis, healthcare and health monitoring, and sports activities [20; 21; 22; 23; 24]. The inertial gait data collected via wearable gadgets have been analysed to better understand human motion from a variety of perspectives such as estimating soft-biometrics such as gender, age, and height [25; 26], predicting emotions [19; 27; 28], classification of terrains and ground surfaces [29], human activity recognition [30; 31], cognitive impairment and fall detection [32; 33; 34], and even person re-identification [35; 36]. Body posture and movement are the most independent forms of expressing human emotions, as opposed to facial expressions, which may be altered and hence faked. Much research suggests that the kinematics of walking can convey emotional states [19; 37; 38; 39; 40; 41].