Several research states the principle behind HAR context is the activity recognition chain (ARC) possibly interpreted from the sensor data based on human activity recognition (K. Schwab, 2018). An ARC is known to be a machine learning technique, which basically depends on the pattern recognition and a sequence of signal processing techniques that are equipped in identifying specific activities.
Basically, accelerometer and gyroscopes are certain wearable sensors that are implemented in determining human activities. Since accelerometers promote linear acceleration measurement, it cannot promote actions, which involve joint rotations. Gyroscope sensors help in measuring rotational motion. Therefore, both the accelerometer and gyroscopes together serve the best results. Both sensors are integrated with the single wearable inertial mobile unit (IMU). Surface electromyography (sEMG) is another wearable sensor, which provides myoelectric signals based on muscular activities. Due to this purpose, sEMG plays an important part in activity recognition.