Smart humidity sensor monitors human behavior through respiratory patterns

A cutting-edge humidity sensing system has been unveiled, capable of monitoring human behaviors in real-time through the detection of respiratory patterns. This breakthrough technology integrates a highly sensitive humidity sensor with a thermistor and micro-heater, enabling exceptional accuracy in behavior recognition.

By using porous nanoforests as the sensing material, the system exhibits remarkable sensitivity, stability, and gas selectivity. With the added power of machine learning algorithms, the system achieves an impressive 96.2% accuracy in identifying human behaviors, positioning it as a game-changer in health care, smart homes, and everyday health monitoring.

Human behavior recognition has become increasingly vital across various domains, from health care to smart home automation. Traditional methods, such as video analysis and wearable devices, often face privacy concerns, environmental limitations, and the need for multiple sensors. Respiration, a key physiological signal, changes with different physical conditions, making it a promising metric for behavior recognition.

However, current humidity sensors fall short in terms of sensitivity and stability, particularly when detecting subtle respiratory shifts, such as rapid or weak breathing. This gap has highlighted the urgent need for advanced sensors capable of accurately tracking and analyzing human behavior in real-time.

In an exciting development, researchers from the Institute of Microelectronics of the Chinese Academy of Sciences have introduced a novel humidity sensing system, detailed in Microsystems & Nanoengineering on January 22, 2025.

This system incorporates a thermistor and micro-heater with porous nanoforests as the sensing material, achieving an impressive 96.2% accuracy in recognizing human behaviors through respiration monitoring. The integration of machine learning further enhances the system’s ability to provide real-time analysis, setting the stage for transformative applications in health care and smart home technologies.

At the heart of this research is the innovative humidity sensor utilizing porous nanoforests (NFs). The sensor operates within a humidity range of 60–90% relative humidity (RH) and boasts a sensitivity of 0.56 pF/%RH. A micro-heater enhances its sensitivity by 5.8 times, enabling the detection of even the faintest humidity changes in exhaled air. The inclusion of a thermistor allows for precise temperature monitoring, ensuring long-term stability and accuracy. With a rapid response time of just 2.2 seconds, along with excellent gas selectivity, the sensor is ideally suited for monitoring respiratory activity.

Behavior recognition is driven by a convolutional neural network (CNN) that analyzes the sensor’s humidity, temperature, and time data. By converting these one-dimensional signals into three-dimensional maps, the system can classify nine common behaviors, such as walking, sleeping, and exercising, with a high degree of accuracy (96.2%). Integrated into a mask, the sensor continuously collects respiratory data, which is wirelessly transmitted to smartphones or computers for analysis. This seamless fusion of hardware and software demonstrates the system’s immense potential for practical use in health care and daily life.

Dr. Haiyang Mao, the lead researcher of the study, emphasized the significance of this breakthrough: “This innovative humidity sensing system represents a significant leap forward in real-time behavior recognition. By combining advanced sensor technology with machine learning, we’ve created a reliable and highly accurate tool for monitoring human behaviors, which has profound implications for both health care and smart home technologies.”

The potential applications of this intelligent humidity sensing system are vast. In health care, it could be used to monitor patients with respiratory conditions or those needing to track physical activity levels. In smart homes, it could enhance comfort and safety by automatically adjusting appliances based on occupants’ behaviors.

Furthermore, the system’s ability to detect subtle changes in respiration may also provide valuable insights into emotional states, such as anxiety or stress, opening new pathways for mental health monitoring. With its impressive accuracy and real-time capabilities, this system is set to be a cornerstone of future health electronics and intelligent living.