# Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
## 2 Related Work
### Traditional Health Care Techniques  
Traditional health care techniques are classified into three types: computer vision-based, wearable device-based, and ambient sensor-based techniques [16; 17]. The computer vision-based techniques use low price RGB cameras and depth cameras to assess the state of a patient [18]. [PERSON] et al. introduced the RGB camera-based fall detection system, which is a device-free technique [19]. The fall detection system accomplished very good detection performance with a detection rate of greater than 96%. [PERSON] et al. proposed a vision-based fall detection technique that deals with occlusion issues by using head tracking [17]. [PERSON] et al. suggested a camera-based heart rate monitoring technique using subtle heart techniques caused by a pulse [20]. The experimental assessment showed that the detected heart rate was nearly the same as the heart rate measured by an electrocardiogram device. The computer vision-based techniques invade users' privacy, which is critical for monitoring in private places. The computer vision-based techniques also receive environmental limitations; there can be blind spots in the NLOS environment.  
The wearable device technique utilizes wearable sensors, for example, the gyroscope, accelerometer, and light intensity sensor, to detect the patient's safety state while preserving the patient's privacy. [PERSON] et al. introduced a portable fall-detection device connected to the neck of a vest [21]. [PERSON] et al. introduced a fall detection technique utilizing an inertial sensor and barometer [22]. These techniques compute the root-mean-square accelerometer data and/or other features and detect the large impact. An obvious weakness of the wearable device technique is a deterioration in the comfort of patients when performing movements after surgery. Particularly patients with dementia could also forget to wear the wearable devices.  
The ambient sensor-based techniques use sensors such as vibration, pressure, sound, and radar embedded in an environment to recognize the activities of humans. [PERSON] et al. introduced a fall detection system that depends on vibration and sound generated by a fall event [23]. However, the ambient sensor-based techniques suffer from high false detections because of noise such as everyday object falls [24]. Radar-based device free techniques have also been introduced [25; 26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviation** & **Description** & **Abbreviation** & **Description** \\ \hline ADC & Analog to Digital Converter & LOS & Line of Sight \\ AI & Artificial Intelligence & LPA & Log Periodic Antenna \\ CP & Cyclic Prefix & LPF & Low Pass Filter \\ DAC & Analog Conversion & LSVM & Linear Support Vector Machine \\ DAC & Digital to Analog Converter & NLOS & Non-Line-of Sight \\ DC & Direct Current & OFDM & Orthogonal Frequency Division \\  & & & Multiplexing \\ DDC & Digital down Converter & PCA & Principal Component Analysis \\ DUC & Digital up Conversion & QPSK & Quadrature Phase Shift Keying \\ EBT & Ensemble Boosted Trees & RF & Radio Frequency \\ FDT & Fine Decision Tree & RGB & Red Green Blue \\ FFT & Fast Fourier Transform & RSS & Received Signal Strength \\ FKNN & Fine K-Nearest Neighbor & TA & Transmit Amplification \\ IFFT & Inverse Fast Fourier Transform & ToF & Time of Flight \\ ISCOS & International Spinal Cord & USRP & Universal Software-Defined \\  & Society & & Radio Peripheral \\ ISI & Inter Symbol Interference & WCSI & Wireless Channel State \\ LDA & Linear Discriminant Analysis & WHO & World Health Organization \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of used abbreviations.  
The radar-based techniques need a line-of-sight (LOS) environment, i.e., no occlusion is allowed between the radar and a patient, which extremely restricts physical deployment