Overview of Wearable IoT Devices for Coronavirus Detection

Imagine a wearable gadget capable of identifying early signs of a Coronavirus infection. Such technology could revolutionize the detection and monitoring of cases globally. Think of a device similar to an Apple Watch or Fitbit, designed specifically to recognize Covid-19 symptoms. While this concept is feasible, most existing detection methods rely on thermal scanners, which are not always accurate due to various factors that can influence body temperature aside from the virus.

This wearable could potentially link to the cloud, aggregating symptom data from numerous Covid-19 patients exhibiting similar signs. Such a system might encourage users to seek medical advice promptly, thus addressing the virus's effects more effectively.

Recent research (May 2020) from Northwestern University and Shirley Ryan AbilityLab led to the development of a sensory device resembling a band-aid, placed on the neck. This device monitors cough, fever (temperature), and respiratory activity. It employs specific algorithms tailored to detect early signs and symptoms of the virus.

This compact device is designed to rest in the suprasternal space, the visible hollow at the base of the neck. Its purpose is to track coughing patterns, chest movements (which indicate breathing difficulties), respiratory vibrations, heart rate, and body temperature, including the presence of fever.

Data is sent wirelessly to a HIPAA-compliant cloud, where AI algorithms generate visual reports to facilitate rapid, remote monitoring of Coronavirus symptoms.

The devices were developed at Northwestern using algorithms crafted by scientists at Shirley Ryan AbilityLab. Currently, they are being utilized in a study involving Coronavirus patients and the healthcare professionals who care for them.

To date, 25 active Coronavirus patients have been equipped with this device for over two weeks, yielding more than one terabyte of data, equivalent to roughly 1500 hours of monitoring. These findings have enabled the AI algorithms to refine the detection and monitoring processes.

> “We anticipate that the advanced algorithms we are developing will extract COVID-like signs and symptoms from the raw data insights and symptoms even before individuals may perceive them,” > — Arun Jayaraman, leading research scientist

This innovative approach enhances patient monitoring from hospital to home, allowing doctors to access incoming data through a cloud interface, thereby improving the likelihood of detecting Coronavirus symptoms before individuals become aware of them. The device may identify symptoms earlier than frontline workers, enabling healthcare providers to implement precautionary measures and arrange further testing as needed.

How Does It Function? What Alternatives Exist?

While the research provides insights into the device's capabilities, it lacks clarity on the technical specifics. Although they assert HIPAA compliance for the collected data, there remains a degree of uncertainty regarding the sensor's operational mechanics.

Initially designed for stroke patients, the sensor was developed through collaboration between researchers at Northwestern and Jayaraman’s Labs. The sensors work by accurately measuring vibrations from the throat and chest, filtering out background noise to alleviate privacy concerns.

However, details regarding the sensor's construction remain vague. Are they employing a prebuilt sensor like the MLX90614, an infrared thermistor used for non-contact temperature measurement?

Many temperature scanning devices for detecting Coronavirus are built around this type of sensor, which identifies average temperature fluctuations often seen in infected individuals.

This sensor could easily be programmed into a wearable device to monitor temperature, connecting via a data logger to a cloud database that cross-references patient data for accurate results. Easier said than done, right?

To be fair, the device primarily relies on temperature, respiratory, and heart rate data insights. Since the specifics of "how" it operates are not disclosed, we can infer that the vast amount of data collected likely includes temperature readings, rather than a broader range of sensory inputs.

Another research effort by engineers from the University of Florida has yielded a wearable sensor utilizing machine learning models for temperature-based Coronavirus detection. They claim that their approach surpasses traditional forehead thermal scanners by employing machine learning to enhance symptom detection and monitoring.

Exploring ISFET Technology for Coronavirus Testing

In my exploration of various Coronavirus testing methods, I discovered research utilizing Ion Sensitive Field Effect Transistor (ISFET) sensors. This technique involves taking a saliva or nasal sample with a cotton swab and placing it on a protein patch. If the sample contains a Coronavirus strain, it interacts with the proteins, generating an electrical charge detectable by the sensor, signaling infection. Users would then receive instructions to consult medical services.

These technologies are often referred to as "lab-on-a-chip" (LoC) systems, which aim to miniaturize laboratory tests that once required bulky equipment down to the size of a computer chip or microscope slide. Currently, LoC tests are undergoing clinical trials, capable of performing tests in under an hour, albeit requiring physical samples.

The application of this technology can be enhanced by integrating a data logger and Wi-Fi module, allowing results to be uploaded to a cloud environment (such as Azure IoT Sphere) via an SD card, facilitating real-time updates and monitoring in collaboration with healthcare professionals.

While still conceptual, ongoing research aims to develop wearable devices with such capabilities, presenting a more robust solution compared to temperature-based detection methods.

Utilizing Respiratory and Heart Rate Sensors for Coronavirus Symptom Detection

Another notable sensor, the MAX30102, serves as a high-sensitivity pulse oximeter and heart rate sensor for wearable health devices. This sensor, combined with an IR temperature sensor, can be integrated with a data logger and Wi-Fi module to transmit collected data to the cloud for analysis. This capability would enable real-time monitoring and analysis of Coronavirus symptoms on a global scale.

Such advancements could revolutionize testing for healthcare professionals by minimizing their exposure to the virus from suspected patients. Additionally, Swiss researchers have developed a high-performance biosensor capable of detecting Coronavirus in the air, employing innovative methods to identify and report symptoms.

With continuous advancements in machine learning, IoT technology, and cloud computing, it may not be long before commercially available, FDA-approved Coronavirus detection and monitoring devices become a reality.

Disclaimer: The average time for research and development may vary, but we anticipate seeing wearable devices become commercially available on a global scale between 2020 and 2025.

Until then, remember to follow safety protocols, stay safe, and take care!