**6. Discussions**

Driven by the related sensing, communication, and data processing techniques, HAR has undergone a rapid progress and is extended to widespread fields of applications. This section summarizes the state-ofthe-art, the underlying technical challenges, and the future trend.

### *6.1. Performance of the State-of-the-Art*

Based on the above investigation, it is a critical issue to determine how to select the appropriate sensing techniques and mathematical methods for HAR applications. It is found that the technical solutions are highly dependent on the particular HAR tasks. For accurate indoor human activity recognition, optical depth sensors with PCA, HMM, SVM, KNN, and CNN are commonly seen to obtain an accuracy over 95%, and combination of UWB and IMU can also find applications with accuracy over 90%. For wearable outdoor applications of consumer electronics, sports analysis, and physical rehabilitation, a single IMU is a competitive choice, which normally uses Kalman filter or a complementary filter for noise cancellation and uses SVM or CNN for decision-making. The accuracy for walking and running step counting and other motion recognition is normally over 90%. Then, for medical care, physical rehabilitation or bio-feedback, thin film force sensors and EMG sensors are commonly used to obtain information of local areas of body parts. Deep learning is the most prevalent choices, which may result in an accuracy at about 90%. According to the analysis in Sections 4 and 5, each sensing technique and mathematical method has its own pros and cons and suitable application scenarios. The combination of two or more sensing techniques or mathematical methods may overcome the limitations of one technique by taking advantage of the others.
