# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Pressure Sensor
#### 3.1.1 Pressure Sensor Types  
The most widely used approach is the chair with integrated pressure sensors due to its non-intrusive and privacy-preserving features, which may make people more receptive over time. The user has direct contact with the pressure sensors, creating a seamless interface. These sensors measure the distribution of pressure applied to a surface, such as a chair. They can be embedded in the seat and backrest to detect variations in pressure caused by different sitting postures. The research studies [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70] use pressure sensors to recognize different sitting postures. Force Sensing Resistors  
Force-resistive sensors, or force-sensitive resistors (FSRs), are electronic devices designed to detect and measure the force or pressure applied by changing their electrical resistance in response to the applied force. These are mainly constructed using substances that demonstrate a variation in electrical resistance; mechanical distortion is applied. The electrical resistance of the sensor is inversely proportional to the force applied, thus providing a continuous or analog output signal that corresponds to the force exerted on them. These analog output signals can be converted into digital values using analog-to-digital converters (ADCs) for further processing and analysis by microcontrollers or other electronic devices. Force-resistive sensors are simple, low-cost, and flexible but limited in accuracy, durability, and sensitivity to environmental factors such as temperature and humidity. Figure 2 shows the FSR 400, FSR 402, and FSR 406 models from Interlink with two wires. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Sensor Type** & **Function** & **Strengths** & **Limitations** & **Power Consumption** & **Cost** \\ \hline \multirow{4}{*}{Depth Cameras} & Capture 3D images to assess the spatial arrangement of the body & 3D spatial data, detailed analysis & Expensive, requires a clear line of sight & 3.4 W [49] & High [50]\$25.0 (Amazon) \\  & Communicate using radio waves between tags and readers & user comfort, scalability, and low maintenance & Lacks precision & Passive: \(<\)1 mW [51], Active: 10–100 mW & Low to moderate [52]\$0.9 (Amazon) \\  & Measure the distance to an object using sound waves. & Simple distance measurement, inexpensive & Low resolution, interference issues & 20–100 mW [53] & Low [54]\$4.50 (Spark fun) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 2: Interlink FSR models **(a)** FSR-400 **(b)** FSR-402 **(c)** FSR-406. Some of the studies making use of FSR for posture recognition are shown in Table 5.  
The textile pressure sensor is a type of sensor embedded into cloth or textile materials to detect and quantify pressure or force applied to the surface. The most common measurement principle for textile moisture sensors is a resistance measurement between two conductive electrodes. Polyester, cotton, or polypropylene are examples of insulating materials used to make the support cloth over which the electrodes are constructed. The fabric has relatively little electrical conductivity when it is \"dry\". [PERSON] et al. [76] employed 12 such sensors situated on the bottom rest and backrest of a chair. They applied decision trees, SVM, multilayer perceptron, naive Bayes, and k-NN algorithms to categorize five distinct sitting postures specific to wheelchairs. The study yielded a high accuracy rate of 99.51% with decision trees. Nevertheless, their approach utilized a greater number of sensors compared to our study and focused solely on detecting five wheelchair-specific postures. [PERSON] et al. [77] optimized the textile pressure sensor for low hysteresis and used it for classifying different sitting positions in chairs with an average recognition rate of 82%. The naive Bayes classifier was used to classify the data of nine subjects.