## 2 Literature Review  
Emotion recognition based on inertial sensor data and machine or deep learning entails collecting data on bodily movements and gestures associated with various emotions using sensors such as accelerometers and gyroscopes. These data are subsequently analysed by AI systems to precisely detect an individual's emotional state. There are various advantages of using inertial sensors and AI for emotion recognition over traditional approaches such as facial expression analysis or self-reporting. They can give a more objective and accurate assessment of emotions, even when people are attempting to conceal their emotions. Accurate assessment of the emotional state of a user is vital for improving machine-user interactions. Smart wearable devices are the right choice for emotional estimation because of ease of use, mobility, privacy, and the ubiquity of IMUs in these devices. In this context, [PERSON] et al. [27] investigated the use of smart wristbands with built-in accelerometers to identify three human emotions: neutral, angry, and happy. A population of 123 subjects was used to collect emotional data and traditional machine-learning classifiers were used for training and validation. They reported the best classification accuracy of 81.2%. The dataset proposed in this study includes only the data of Chinese subjects and data on only three emotions were collected. [PERSON] et al. [37] described a method for emotion recognition based on non-intrusive mobile sensing employing accelerometer and gyroscope sensor data supplied by cellphones. To monitor the players' sensor data while playing the game, an Android OS sensor log app was employed, and a prototype gaming software was created. Data from 40 participants were analysed and used to train classifiers for two emotions: happiness and impatience. The validation investigation found that the accuracy for joy is 87.90% and the accuracy for frustration is 89.45%. This study only deals with two types of emotions. [PERSON] et al. [38] designed a system for emotion recognition based on physiological inputs. Body sensors such as muscle pressure sensors, heartbeat sensors, accelerometers, and capacitive sensors were used to gather signals. There were four classes: happy, sad, furious, and neutral. The suggested approach achieved the following percentages of accuracy for emotional states: happy 80%, sad 70%, angry 90%, and neutral 100%. The study considered only three basic emotions. [PERSON] et al. [42] performed emotion recognition based on smart bands inertial sensors data. The dataset collected was based on 50 volunteers. The volunteers were given audio-visual stimuli to enter a state and then were requested to walk. The presented study only dealt with two classes--happy and sad. The accuracy achieved was 75%. [PERSON] and [PERSON] used sensor and feature-level fusion techniques based on body-worn inertial sensors attached to users' wrists and a pressure detection module put on the seat to construct a system that identifies and monitors in-seat actions [44]. They concentrated on four major emotional activities: frustration, sadness, curiosity, and happiness. Their findings revealed that integrating temporal and frequency-domain data from all sensors resulted in outstanding classification accuracies (about 99.0%). A complete system for ambient assisted living was proposed by [PERSON] et al. [8] to alter the environment of the living based on the emotions of the elderly. They developed a smart band to measure electrodermal activity, temperature, heart rate, and mobility. They proposed a neural network consisting of fully connected layers which were trained on 18 features to classify eight different emotions, i.e., happiness, contempt, anger, disgust, fear, sadness, neutral, and surprise. They reported 20% of misclassification cases. The study proposed collecting data on eight emotions but made use of multiple sensors including heart rate and temperature sensors which are usually not available in smart-wearable devices. The study by [PERSON] et al.