Life Science Hardware and Software

Stress has negative effects on the health of the body and the health of economies. When a person is feeling stress, the body releases certain chemicals that lower the immune system, increasing the likelihood that illnesses will form in the body. Stress can also cause negative effects in the workplace, as when workers take time off and seek treatment, the companies they work for lose money as well. The Mental Health Foundation estimates that around 12 million adults living in the United Kingdom visit their General Practitioner every year with concerns of their mental health that have been brought on by stress. Due to these illnesses, an estimated 13.3 million work days are lost every year. The World Health Organization estimates that around 8.4 million GBP (10.5 million USD) are lost by UK businesses due to these health concerns. Moreover, the average wait time to get treatment is 3-6 months. Because of these factors, over the last decade studies performed on stress and mental health have increased in popularity. Researchers, Mozos, Oscar Martinez, et al. aimed to detect stressful behaviors by having their participants wear noninvasive physiological monitoring systems to find what activities elicited stress. These experiments were performed in a laboratory setting, using the TSST (Tier Social Stress Test) to manage levels of anxiety in each participant in a controlled situation; this popular method was used in over 4,000 settings over the last decade. The total sample size the researchers used was 18, male and female volunteers from the School of Psychology at the University of Lincoln in the UK. The social task presented was for the subject to prepare a presentation for a mock job interview, and tasked to speak continuously for five minutes. When subjects paused the first time, the experimenter would tell them their remaining time and ask them to continue. The second time the participant paused they would be asked a set of predefined interview questions. For the cognition task, the experimenter asked the participant to count down from 1022 in sets of 13, for five minutes. If the participant made a mistake, they were asked to start from 1022 again. Using the wearable, dual-signal BioNomadix, researchers wirelessly recorded electrodermal activity (EDA) and pulse plethysmograms (PPG), then analyzed the data to gather the Heart Rate Variability (HRV) between different tasks the subjects were doing, and extrapolate the stress the subjects were feeling in those conditions. The goal of the researchers was to show that using wearable monitoring systems can help detect stress, and with variations, can be applied outside of the lab, and into everyday interactions to get a firmer grip on what is causing anxiety and stress in millions of people.

One of the foundational concepts making virtual reality (VR) video games of high interest is the game’s ability to transport the user to a flow state. The visual and auditory stimuli presented are highly immersive, leading the user to focus entirely on the game, often losing track of time. Flow states are characterized by this lack of time awareness, as participants are able to match their abilities with the demands of the game (i.e., the game is not too easy or too hard). But what qualities reflect a flow state as it is happening in the moment? Researchers from Shandong University tested five first-level physiological functions to determine their efficacy in reflecting a flow state, including EMG, EDA, EEG, respiratory rate, and cardiovascular activity. Thirty-six students participated in a VR game while having their physiological responses monitored. Prior to the experiment, the researchers placed BIOPAC’s wearable dual-signal BioNomadix transmitters with appropriate electrodes on the participants to wirelessly record Respiration and ECG data. Participants were then seated in the designated gaming chair for five minutes to record baseline responses. After this, they played the game for six minutes, followed by a questionnaire about flow experience. The results showed that the five physiological functions, as a whole, indicated flow state, though respiratory rate was most effective. The authors note that, as a physiologic arousal marker, respiratory rate best predicted flow state.

Having physiologic indicators of a flow state not only assists future research, but also provides a method for real-time feedback on the efficacy of the game. The authors note that with better biometric data comes the opportunity to provide a better gaming experience, with real-time adjustments. If the physiologic responses and adjustments could be integrated with the gaming software, games would be far more realistic.

The negative effects of stress on the body have been widely studied. Stress can be defined as a situation that is causing the current state, or homeostasis, under pressure to change. The human body’s nervous system reacts to stress by changing the amount produced of certain biomarkers. For example, when heart rate elevates, blood pressure rises and the human body reacts and secretes hormones (epinephrine, cortisol, etc.). Experimenters tested the change in the production of specific biomarkers of people with different training backgrounds to understand how acute psychological stress affects their physiological responses. The three group classifications were sedentary subjects, endurance athletes, and strength athletes.
 
EDA (skin conductance), ECG (EKG), and breathing frequency were measured continuously; BP and cortisol were measured after each experiment segment. EDA, ECG, and breathing frequency were measured during the acute psychological stress test using the BIOPAC MP150 data acquisition unit connected to wireless biopotential amplifiers and recorded on BIOPAC’s AcqKnowledge software.

Psychological stress was induced in participants using a Stroop color-word test and math problems. These problems were presented in a slide show where the subjects had a limited amount of time to solve for the correct answers. The researchers found numerous differences in changes in the biomarkers measured in response to the acute psychological stress activities between the three groups. On average, athletes’ cortisol levels changed differently when compared to the sedentary group. Also, skin conductance was shown to have higher levels in the sedentary group than in the athletes. The athletes also had a higher recovery level for systolic blood pressure, which was observed to decrease over the test for the sedentary group.
 
The participants reported to have experienced psychological stress over the course of the activities and this was reinforced by the change in values of the biomarkers measured. This experiment showed that people with different training backgrounds had different responses to psychological stress for related biomarkers. The experimenters concluded that people with different training backgrounds react differently in their changes of certain biomarkers to psychological stress.

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.

Emotional reactions influence, and may help predict, our decisions and offer valuable information for communication and neuromarketing researchers, but emotion is difficult to measure explicitly. Emotional responses are complex phenomena consisting of multiple components, including evaluation/appraisal, subjective feeling, expression, and physiological reaction. This mix of components is difficult to measure. Researchers can interview or survey participants about their feelings—typical measures include traditional Likert-type questions, open-ended questions, or pictorial scales—but self-reporting doesn’t easily convey true or complete emotional response. Self-reporting is further complicated by the fact that participations often choose different terms to describe their feelings or respond that they feel nothing. Blending self-assessment with physiological changes that reflect visceral responses provides an unfiltered representation of emotion.