# Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of Sensors** & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{Methods based on cameras} & \(\blacklozenge\) & High accuracy and robustness & \(\blacklozenge\) & Limited application scenarios \\  & \(\blacklozenge\) & Non-contact and comfortable & \(\blacklozenge\) & Difficult to use in a dark environment \\  & \(\blacklozenge\) & Avoid manual use & \(\blacklozenge\) & Privacy issues \\ \hline \multirow{4}{*}{Methods based on wearable devices} & \(\blacklozenge\) & Privacy protection & \(\blacklozenge\) & Inconvenient \\  & \(\blacklozenge\) & Easy to collect data & \(\blacklozenge\) & Uncomfortable \\ \cline{1-1}  & \(\blacklozenge\) & Various types of sensors & \(\blacklozenge\) & Limited battery capacity \\ \cline{1-1}  & & can be chosen & & Difficult for the elderly to use \\ \hline \multirow{4}{*}{Methods based on millimeter-wave radars} & \(\blacklozenge\) & Privacy protection & \\  & \(\blacklozenge\) & Non-contact and comfortable & \(\blacklozenge\) & Difficult to collect data \\ \cline{1-1}  & & comfortable & \(\blacklozenge\) & Easily affected by noise \\ \cline{1-1}  & \(\blacklozenge\) & Avoid manual use & \(\blacklozenge\) & Limited location of \\ \cline{1-1}  & \(\blacklozenge\) & Not affected by the light & & installation \\ \cline{1-1}  & & condition & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of different sensors. and magnetometer data features. The results showed that the method had a better performance on the recognition of falls than similar methods. In 2018, [PERSON] et al. [23] extracted information and direction by combining different sensors, and then used thresholds and machine learning to recognize falls with 91.1% accuracy. Unlike visual methods, wearable devices pay more attention to privacy protection and will not be disturbed in a dark environment. However, wearable devices need to be worn, which reduces comfort and usability and is challenging to apply to older adults. In addition, the limitations of the battery capacity of wearable devices makes it difficult for them to work for an extended period. To address these disadvantages, [PERSON] and [PERSON] [24] used sensors in smartphones for HAR. However, this method still has some limitations for the elderly who are either not familiar with or do not have smartphones. With the development of radar sensors, there has been an emergence of HAR using millimeter-wave radar data [25]. Compared with other methods, radar data can better protect personal privacy and is more comfortable for users. The key to using radar to recognize human activities is to extract and identify the features of the micro-Doppler signal generated when the elderly move. In 2011, [PERSON] et al. [26] extracted time-frequency features of activities through the mel frequency cepstrum coefficient (MFCC) and used support vector machine (SVM) and k-nearest neighbor (KNN) to recognize activities with 78.25% accuracy for SVM and 77.15% accuracy for KNN. However, the limit of supervised learning is that it can only extract features artificially and cannot transfer learning. Deep learning does not require complex feature extraction and has good learning and recognition ability for high-dimensional data. [PERSON] et al. [27] and [PERSON] et al. [28] used distance spectrums and time series of radar data combined with CNN for HAR. In 2020, [PERSON] and [PERSON] [29] used spectrograms as input of CNN to distinguish falling and non-falling. In the same year, [PERSON] et al. [30] and [PERSON] et al. [31] used multiple radar sensors for HAR to solve the problem that a single radar sensor could only be used in a small range. [PERSON] et al. [32] proposed a long short-term memory network (LSTM)) to solve the problem of gradient vanishing and gradient explosion. [PERSON] et al. [33] used an improved LSTM model based on a recurrent neutral network (RNN) combined with deep CNN.