By Stephen Beech via SWNS
A new wearable “smart” sensor patch can detect health symptoms early via a smartphone.
The innovative device is able to spot an abnormal heart rhythm as well as coughs and falls, say Japanese scientists.
They explained that the sensors generate large amounts of data, and that data must be processed to be understood.
The field of computing dealing with processing the data on the sensor or a device that the sensor is connected to – rather than at a remote server on the cloud – is called edge computing.
The research team – led by Professor Kuniharu Takei at Hokkaido University and Associate Professor Kohei Nakajima at The University of Tokyo – says edge computing is a “key” element in wearable sensor technology.
They have made a flexible wearable sensor patch and developed edge computing software that is capable of detecting arrhythmia, coughs and falls in volunteers.
The sensor uses a smartphone as the edge computing device.
Takei said: “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.”
The team created sensors that monitor cardiac activity via electrocardiogram (ECG), respiration, skin temperature, and humidity caused by perspiration.
After confirming their suitability for long-term use, the sensors were integrated into a flexible film that adheres to human skin.
The sensor patch also included a Bluetooth module to connect to a smartphone.
The team first tested the capability of the sensor patch to detect physiological changes in three volunteers, who wore it on their chests.
The sensor patch, described in the journal Device, was used to monitor vital signs in the volunteers under wet-bulb globe temperatures – used to determine the likelihood of heat stress- of 22°C and over 29°C.
Takei said: “Although our test group was small, we could observe their vital signs change during time-series monitoring at high temperatures.
“This observation may eventually lead to the identifying symptoms of early-stage heat stress.”
The team developed a machine learning program to process the recorded data to detect other symptoms such as heart arrhythmia, coughing and falls.
Nakajima said: “In addition to performing the analysis on a computer, we also designed an edge computing application for smartphones that could perform the same analysis.
“We achieved prediction accuracy of over 80%.”
Takei added: “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.”
