Wednesday, March 12, 2025

 

Smart humidity sensor transforms human behavior recognition





Aerospace Information Research Institute, Chinese Academy of Sciences
The behavior recognition system based on the novel humidity sensor and its potential applications in smart healthcare. 

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The behavior recognition system based on the novel humidity sensor and its potential applications in smart healthcare.

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Credit: Microsystems & Nanoengineering




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 healthcare, smart homes, and everyday health monitoring.

Human behavior recognition has become increasingly vital across various domains, from healthcare 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 (DOI: 10.1038/s41378-024-00863-6) 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 healthcare 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 healthcare 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 healthcare and smart home technologies."

The potential applications of this intelligent humidity sensing system are vast. In healthcare, 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.

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References

DOI

10.1038/s41378-024-00863-6

Original Source URL

https://doi.org/10.1038/s41378-024-00863-6

Funding information

This work was supported by National Natural Science Foundation of China (Grant Nos. 62474192 and 62201567), Youth Innovation Promotion Association, Chinese Academy of Sciences (Grant Nos. 2022048 and 2022117), State Key Laboratory of Dynamic Test jointly built by Province and Ministry Open Fund (Grant No. 2022-SYSJJ-07).

About Microsystems & Nanoengineering

Microsystems & Nanoengineering is an online-only, open access international journal devoted to publishing original research results and reviews on all aspects of Micro and Nano Electro Mechanical Systems from fundamental to applied research. The journal is published by Springer Nature in partnership with the Aerospace Information Research Institute, Chinese Academy of Sciences, supported by the State Key Laboratory of Transducer Technology.

 

MSU researchers use unique approaches to study plants in future conditions



Michigan State University
Measuring photosynthesis 

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Yuan Xu, postdoctoral researcher in the Sharkey lab, uses a LICOR to measure the rate of photosynthesis in a plant. 

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Credit: Kara Headley




As major changes continue for our planet’s climate, scientists are concerned about how plants will grow and adapt.

Researchers in the MSU-DOE Plant Research Laboratory, or PRL, Sharkey lab are studying changes in plant metabolism that occur when plants are grown in high light, high CO2 (HLHC) conditions.

They found that under these conditions, plants photosynthesize more, which can lead to larger plants, and potentially larger crop yields. However, there are tradeoffs; scientists also found that plants lose carbon under these conditions, which they need to make food. This study was published in Scientific Reports. 

Environmental conditions are predicted to continue changing in two major ways. First, atmospheric carbon dioxide is projected to continue increasing. Second, a phenomenon known as global brightening is changing light levels as more solar radiation makes its way to the ground than in previous decades.

Scientists predict these conditions will impact plant metabolism, or the internal mechanisms in plants that allow them to live and grow.

“Our work demonstrates that it’s very important to study photosynthesis and the carbon metabolism in plants,” said Yuan Xu, postdoctoral researcher in the Sharkey lab and first author on the study. “Especially when we think about conditions for the future based on predictions. If you want to do bioengineering in the future, to make a plant that can better adapt to these future conditions, you need to focus on these areas.”

This study revealed two major findings: under these future conditions, plants increase their rate of photosynthesis, but their rate of respiration in the light remains consistent with current conditions.

Increasing the rate of photosynthesis means the plant can make more sucrose and starch, the food it needs to survive.

“Most carbon fixed in photosynthesis becomes either starch [to use later] – like putting money in your bank account – or sucrose (table sugar) to use now – like buying an ice cream cone,” said Thomas D. Sharkey, University Distinguished Professor in the PRL. “The most surprising observation was that extra carbon at high light and high carbon dioxide went much more to starch (76% increase) rather than sucrose (41% increase). This may help plants become more resilient because they will have extra carbon for growth or defense.” Sharkey is also in the Department of Biochemistry & Molecular Biology and the Plant Resilience Institute.

Increased rates of photosynthesis may also lead to larger plants, as the plant is making more food for itself. This can potentially lead to larger yields of the crops we eat.

But a remaining issue is that plants also lose some carbon during photosynthesis – carbon which the plant could be using to make food. During a process known as respiration in light, or RL, CO2 is released by the plant.

This study found that RL remains constant in current and future conditions. What this means is the rate at which CO2 is released by the plant through the RL pathway is the same, despite the increase in photosynthesis.

Innovative methods

The researchers used a unique technique to measure RL. Typically, RL is measured using gas exchange methods such as the Laisk or Kok methods. However, these methods only work in low light conditions.

“That’s why this study is unique,” Xu explained. “We used a new approach to measure the RL in the high light condition that cannot be measured using the old method.”

Xu used a method known as isotopically nonstationary metabolic flux analysis, or INST-MFA, to look at gas exchange in HLHC conditions. This method is tried and true in bacteria and fungi studies but has only been used in a handful of plant studies over the past decade.

The Sharkey lab will continue to use innovative research methods to study respiration in the light. Understanding this process will allow a better understanding of carbon dioxide uptake and release in the future.

“We know from this study that many things are changed [under future conditions] including photosynthesis, photorespiration and respiration in the light,” Xu said. “This gave a hint for future work.”

This work was funded by the Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences at the U.S. Department of Energy, under Grants  DE-FOA-0001650 and DE-FG02-91ER20021, and MSU AgBioResearch.

By Kara Headley