Wearable sensors are devices designed to be worn on the body that measure various physiological states. As part of the Internet of Things (IoT), these sensors hold significant potential for health monitoring. They produce substantial amounts of data, which must be processed for meaningful interpretation. The area of computing focused on processing this data locally on the sensor or a connected device, instead of relying on a remote cloud server, is known as edge computing. This approach is essential for the advancement of wearable sensor technology. Researchers have now employed edge computing on smartphones to analyze data from a multimodal flexible wearable sensor patch to detect arrhythmia, coughs, and falls.

A research team from Hokkaido University (Hokkaido, Japan) has created a flexible multimodal wearable sensor patch and developed edge computing software capable of identifying arrhythmia, coughs, and falls in volunteers. This innovative sensor, which utilizes a smartphone as the edge computing device, is detailed in a paper published in the journal Device. The patch is equipped with sensors that monitor cardiac activity through electrocardiogram (ECG), as well as respiration, skin temperature, and humidity due to perspiration. After confirming their long-term usability, the sensors were integrated into a flexible film that adheres to the skin. Additionally, the sensor patch contains a Bluetooth module for connection to a smartphone.

The team initially evaluated the sensor patch's ability to detect physiological changes in three volunteers who wore it on their chests. The patch was used to monitor vital signs in these individuals at wet-bulb globe temperatures (which assess heat stress risk) of 22°C and above 29°C. While the sample size was limited, the researchers were able to observe significant changes in vital signs during time-series monitoring at elevated temperatures. This could potentially aid in identifying symptoms of early-stage heat stress. To further enhance their findings, the team developed a machine learning program to analyze the recorded data for additional symptoms, including heart arrhythmia, coughing, and falls. Besides conducting the analysis on a computer, they also created an edge computing application for smartphones that achieved similar analytical results, with a prediction accuracy exceeding 80%.

“Our goal in this study was to design a multimodal sensor patch that could process and interpret data using edge computing, and detect early stages of disease during daily life,” said Professor Kuniharu Takei from Hokkaido University. “The significant advance of this study is the integration of multimodal flexible sensors, real-time machine learning data analyses, and remote vital monitoring using a smartphone. One drawback of our system is that training could not be carried out on the smartphone, and had to be done on the computer; however, this can be solved by simplifying the data processing.” 

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