*5.2. Classification and Decision-Making*

For most human activity recognition studies, the classification to discriminate the di fferent types of activities using the extracted features is a critical step. Due to the complexity in human gesture, posture, and daily activities, how to descriminiate them with certain accuracy using di fferent mathematical models becomes a research focus. The classification can be classified into two categories:

1. Threshold-based method—Threshold can be eaisly used to obtain many simple gestures, postures, and motions. For example, fall detection, hand shaking, and static standing can be recognized using the acceleration threshold calculated using equation *aTH* = - *a*2 *x* + *a*2 *y* + *a*2 *z* , and walking or running can be recognized using Doppler frquency shift thresholds.


Due to the superior performance of machine learning and deep learning techniques, many tool libraries—such as MATLAB machine learning toolbox, pandas, and scikit-learn [105]—for di fferent development environments are available, which makes the implementation of the classification quite convenient. The di fferent machine learning techniques will continually be considered the dominant tool for classification for various sensor data based human activity recognition.
