Life Science Hardware and Software

There is a major concern growing in the medical community that the ratio of health workers to population size is decreasing. This means that the number of available doctors and medical professionals is starting to become too small to handle the number of people needing medical help. Technologies are therefore being created to help bridge the gap that is being created. These “Quality of Life Technologies” (QoLTs) have been developed to help monitor the health of people. While these technologies have been able to provide physiological support to individuals, the same could not be said for mental symptoms. If QoLTs could move into the realm of psychology and self-therapy, they could help improve the mood and quality of life for patients. A group of researchers from the Polytechnic University of Bucharest and the University of Lincoln recently published a paper that presents a machine learning approach for stress detection using wearable physiological amplifiers. To induce stress in participants, the researchers had them perform both a public speaking and cognitive task, which according to previous research these tasks caused the highest increase in measurable signals.

For their experimental setup, they used a BIOPAC BioNomadix BN-PPGED wireless transducer, hooked up to an MP150 data acquisition system, to record both EDA and PPG signals. They then used AcqKnowledge 4 software to extract both the PPG autocorrelation signal and Heart Rate Variability (HRV). Their results provided accurate stress detection in individuals. Their analysis marks a good starting point toward real-time mood detection, which could lead to people improving their quality of life. One way they could improve their experimental setup however, would be to use the BioNomadix Logger. This device allows for up to 24 hours of high quality data logging allowing the researchers to analyze a subject’s data from when they encountered stressful situations outside the lab.