H
Hitesh Kumar
Engineers in the US have achieved a Custom Website Design and Development Services major breakthrough by developing a fully integrated wearable ultrasound system that can monitor deep tissue even when the wearer is in motion. The remarkable device, called wearable ‘ultrasonic-system-on-patch (USoP)’, has the potential to revolutionize cardiovascular monitoring and save lives. The groundbreaking research conducted by a team from the University of California San Diego is detailed in the May 22, 2023 issue of Nature Biotechnology.
Muyang Lin, a Ph.D. candidate in the Department of Nanoengineering at UC San Diego and the first author of the study, explained the significance of their work, stating, "This project gives a complete solution to wearable ultrasound technology—not only the wearable sensor but also the control electronics are made in wearable form factors. We made a truly wearable device that can sense deep tissue vital signs wirelessly."
The study was led by Sheng Xu, a professor of nanoengineering at UC San Diego's Jacobs School of Engineering and the corresponding author of the research. Building on their previous work in soft ultrasonic sensor design, the team developed an autonomous USoP that eliminates the need for tethering cables, which used to restrict the user's mobility. The USoP consists of a small, flexible control circuit that communicates wirelessly with an ultrasound transducer array, collecting and transmitting data. The team also incorporated a machine learning component to interpret the data and track the wearer's movements.
The lab's findings indicate that the ultrasonic system-on-patch allows continuous tracking of physiological signals from tissues as deep as 164mm. It can measure central blood pressure, heart rate, cardiac output, and other physiological signals for up to twelve hours at a time.
The USoP's ability to track physiological signals from deep tissues up to 164mm opens up new possibilities for monitoring cardiovascular health. It allows for the continuous measurement of essential parameters such as central blood pressure, heart rate, and cardiac output for extended periods, up to twelve hours at a time. This non-invasive approach to monitoring vital signs provides valuable insights into an individual's overall cardiovascular function and response to physical activity, enabling personalized training plans and early detection of potential cardiovascular issues.
The implications of the USoP reach beyond cardiovascular health. By leveraging the Internet of Medical Things (IoMT), the device wirelessly transmits collected physiological data to the cloud, where it can be analyzed, computed, and diagnosed by healthcare professionals. This connectivity allows for remote monitoring and real-time feedback, benefiting both patients and healthcare providers.
Lin expressed enthusiasm for cutting-edge IT consulting solutions the potential impact of the technology, saying, "This technology has lots of potential to save and improve lives. The sensor can evaluate cardiovascular function in motion. Abnormal values of blood pressure and cardiac output, at rest or during exercise, are hallmarks of heart failure. For healthy populations, our device can measure cardiovascular responses to exercise in real time and thus provide insights into the actual workout intensity exerted by each person, which can guide the formulation of personalized training plans."
In addition to its life-saving applications, the USoP represents a significant advancement in the Internet of Medical Things (IoMT). The IoMT refers to a network of medical devices connected to the internet, wirelessly transmitting physiological signals to the cloud for analysis, computing, and professional diagnosis. Lin stated, "At the very beginning of this project, we aimed to build a wireless blood pressure sensor. Later on, as we were making the circuit, designing the algorithm, and collecting clinical insights, we figured that this system could measure many more critical physiological parameters than blood pressure, such as cardiac output, arterial stiffness, expiratory volume, and more, all of which are essential parameters for daily health care or in-hospital monitoring."
One of the challenges faced by the team was the relative movement between the wearable ultrasonic sensor and the tissue target when the wearer is in motion. To address this, they developed a machine learning algorithm that automatically analyzes the received signals and selects the most appropriate channel to track the moving target. However, training the algorithm using data from one subject did not guarantee consistent and reliable results across different subjects.
To overcome this, Ziyang Zhang, a master's student in the Department of Computer Science and Engineering at UC San Diego and co-first author of the paper, explained, "We eventually made the machine learning model generalization work by applying an advanced adaptation algorithm. This algorithm can automatically minimize the domain distribution discrepancies between different subjects, which means the machine intelligence can be transferred from subject to subject. We can train the algorithm on one subject and apply it to many other new subjects with minimal retraining."
The development of the wearable ultrasonic-system-on-patch (USoP) marks a significant advancement in the field of healthcare technology. Traditional deep tissue monitoring methods often involve bulky and immobile equipment, making continuous monitoring challenging, especially when individuals are in motion. However, the USoP overcomes these limitations by providing a compact, flexible, and wireless solution that can seamlessly integrate into everyday wear.
Moreover, the integration of machine learning algorithms in the USoP system enhances its capabilities. The algorithm not only interprets the collected data but also addresses the challenge of relative movement between the wearable sensor and the tissue target during motion. By analyzing the received signals and intelligently selecting the most suitable channel, the USoP ensures accurate and consistent monitoring even when the wearer is on the move. The advanced adaptation algorithm developed by the research team enables the machine learning model to generalize across different subjects, minimizing the need for extensive retraining and facilitating widespread adoption of the technology.
The wearable ultrasonic sensor is now set to CMS development company in India undergo testing on larger populations and will be commercialized by Softsonics, LLC, bringing this cutting-edge technology one step closer to benefiting individuals worldwide. As the device undergoes further testing and refinement, it holds the potential to revolutionize healthcare monitoring, enhance preventive care, and improve overall well-being.
In the future, the continuous advancements in wearable technology, coupled with the integration of ultrasound sensors, machine learning algorithms, and cloud connectivity, may pave the way for even more sophisticated healthcare monitoring solutions. These developments have the potential to transform how individuals manage their health, enabling proactive and personalized care that can ultimately lead to better health outcomes and improved quality of life.
Muyang Lin, a Ph.D. candidate in the Department of Nanoengineering at UC San Diego and the first author of the study, explained the significance of their work, stating, "This project gives a complete solution to wearable ultrasound technology—not only the wearable sensor but also the control electronics are made in wearable form factors. We made a truly wearable device that can sense deep tissue vital signs wirelessly."
The study was led by Sheng Xu, a professor of nanoengineering at UC San Diego's Jacobs School of Engineering and the corresponding author of the research. Building on their previous work in soft ultrasonic sensor design, the team developed an autonomous USoP that eliminates the need for tethering cables, which used to restrict the user's mobility. The USoP consists of a small, flexible control circuit that communicates wirelessly with an ultrasound transducer array, collecting and transmitting data. The team also incorporated a machine learning component to interpret the data and track the wearer's movements.
The lab's findings indicate that the ultrasonic system-on-patch allows continuous tracking of physiological signals from tissues as deep as 164mm. It can measure central blood pressure, heart rate, cardiac output, and other physiological signals for up to twelve hours at a time.
The USoP's ability to track physiological signals from deep tissues up to 164mm opens up new possibilities for monitoring cardiovascular health. It allows for the continuous measurement of essential parameters such as central blood pressure, heart rate, and cardiac output for extended periods, up to twelve hours at a time. This non-invasive approach to monitoring vital signs provides valuable insights into an individual's overall cardiovascular function and response to physical activity, enabling personalized training plans and early detection of potential cardiovascular issues.
The implications of the USoP reach beyond cardiovascular health. By leveraging the Internet of Medical Things (IoMT), the device wirelessly transmits collected physiological data to the cloud, where it can be analyzed, computed, and diagnosed by healthcare professionals. This connectivity allows for remote monitoring and real-time feedback, benefiting both patients and healthcare providers.
Lin expressed enthusiasm for cutting-edge IT consulting solutions the potential impact of the technology, saying, "This technology has lots of potential to save and improve lives. The sensor can evaluate cardiovascular function in motion. Abnormal values of blood pressure and cardiac output, at rest or during exercise, are hallmarks of heart failure. For healthy populations, our device can measure cardiovascular responses to exercise in real time and thus provide insights into the actual workout intensity exerted by each person, which can guide the formulation of personalized training plans."
In addition to its life-saving applications, the USoP represents a significant advancement in the Internet of Medical Things (IoMT). The IoMT refers to a network of medical devices connected to the internet, wirelessly transmitting physiological signals to the cloud for analysis, computing, and professional diagnosis. Lin stated, "At the very beginning of this project, we aimed to build a wireless blood pressure sensor. Later on, as we were making the circuit, designing the algorithm, and collecting clinical insights, we figured that this system could measure many more critical physiological parameters than blood pressure, such as cardiac output, arterial stiffness, expiratory volume, and more, all of which are essential parameters for daily health care or in-hospital monitoring."
One of the challenges faced by the team was the relative movement between the wearable ultrasonic sensor and the tissue target when the wearer is in motion. To address this, they developed a machine learning algorithm that automatically analyzes the received signals and selects the most appropriate channel to track the moving target. However, training the algorithm using data from one subject did not guarantee consistent and reliable results across different subjects.
To overcome this, Ziyang Zhang, a master's student in the Department of Computer Science and Engineering at UC San Diego and co-first author of the paper, explained, "We eventually made the machine learning model generalization work by applying an advanced adaptation algorithm. This algorithm can automatically minimize the domain distribution discrepancies between different subjects, which means the machine intelligence can be transferred from subject to subject. We can train the algorithm on one subject and apply it to many other new subjects with minimal retraining."
The development of the wearable ultrasonic-system-on-patch (USoP) marks a significant advancement in the field of healthcare technology. Traditional deep tissue monitoring methods often involve bulky and immobile equipment, making continuous monitoring challenging, especially when individuals are in motion. However, the USoP overcomes these limitations by providing a compact, flexible, and wireless solution that can seamlessly integrate into everyday wear.
Moreover, the integration of machine learning algorithms in the USoP system enhances its capabilities. The algorithm not only interprets the collected data but also addresses the challenge of relative movement between the wearable sensor and the tissue target during motion. By analyzing the received signals and intelligently selecting the most suitable channel, the USoP ensures accurate and consistent monitoring even when the wearer is on the move. The advanced adaptation algorithm developed by the research team enables the machine learning model to generalize across different subjects, minimizing the need for extensive retraining and facilitating widespread adoption of the technology.
The wearable ultrasonic sensor is now set to CMS development company in India undergo testing on larger populations and will be commercialized by Softsonics, LLC, bringing this cutting-edge technology one step closer to benefiting individuals worldwide. As the device undergoes further testing and refinement, it holds the potential to revolutionize healthcare monitoring, enhance preventive care, and improve overall well-being.
In the future, the continuous advancements in wearable technology, coupled with the integration of ultrasound sensors, machine learning algorithms, and cloud connectivity, may pave the way for even more sophisticated healthcare monitoring solutions. These developments have the potential to transform how individuals manage their health, enabling proactive and personalized care that can ultimately lead to better health outcomes and improved quality of life.
