# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 5 Integration with IoT, Alerts, and Energy Consumption
### Energy Consumption  
Energy consumption is an important aspect to consider when evaluating the overall feasibility and sustainability of posture recognition systems. Many factors contribute to the consumption, which includes type of sensor, number of sensors, data acquisition system, and computation complexity of the algorithm used. The type of sensors used in smart sensing chairs can significantly impact energy consumption. For instance, pressure sensors and textile pressure sensors may have different power requirements compared to more complex sensors such as image sensors or Inertial Measurement Units (IMUs). IMUs can be power-intensive when used in wearable devices requiring efficient power management strategies. Flex sensors typically consume very little power, making them suitable for battery-operated devices or systems where power efficiency is important, such as in wearable posture monitors. Passive RFID tags consume virtually no power, as they rely on the reader for energy, while active RFID tags, which have a battery, typically consume between 10 and 100 mW. RGB cameras and depth cameras (such as Kinect) are the most power-intensive. RGB cameras can consume significant power, ranging from 200 to 500 mW for low-resolution cameras to several watts (W) for high-definition or high-frame-rate cameras. Depth cameras, such as the Kinect, can consume significant power, typically between 2 and 10 W [129], depending on the resolution and frame rate. The choice of low-power sensors can help minimize energy usage while maintaining functionality. Also, these systems are designed for real-time posture monitoring, which may require continuous operation of sensors and data processing units. This continuous operation can lead to higher energy consumption compared to systems that operate intermittently. Implementing energy-efficient algorithms and sleep modes for sensors when not in use could help mitigate this issue. Also, a sparse sensing configuration [37; 48] consumes less energy compared to a sensor array, which typically contains a large number of sensors, and scanning them requires more energy [64; 65; 74; 83]. The choice of hardware also contributes to energy consumption, as most of the systems deploy microcontrollers for the acquisition of data from the sensors and technologies to detect and identify different sitting positions. The popular microcontroller choices for data collection include Arduino Mega [60; 64; 71; 81; 93; 94], STM32 [59; 75; 90], Atmel Mega 328 [61; 62; 83], ESP32 [74], and Odroid N\({}_{2}\)\({}^{+}\)[95]. These platforms typically use higher energy as they are not customized and thus contain peripherals that are never used but do contribute to power. Therefore, the use of customized hardware may contribute to the reduction in energy consumption [61]. The energy required for data processing and analysis is another consideration [62]. If the processing is completed locally on the chair, it may consume more energy than if data is sent to a cloud server for processing. Balancing local processing with cloud computing can optimize energy use while ensuring timely feedback for users. For smart sensing chairs that are portable or require battery power, energy consumption directly affects battery life. Efficient energy management strategies are essential to ensure that the chairs can operate for extended periods without frequent recharging, which is crucial for user convenience and sustainability. This can be achieved through duty cycle management [28] and by introducing the sleep mode [62]. The design of user feedback systems can also influence energy consumption. For example, using visual or auditory feedback may require additional power, while haptic feedback systems could be more energy efficient. The choice of feedback mechanisms should consider both user experience and energy efficiency.