Stress Recognition Using Photoplethysmogram Signal
This study proposed a novel method of stress recognition using photopletysmogram (PPG) signal. PPG devices are now widely used because it is convenient, low powered, cheap and also easy to handle due to its small size. A total of 5 subjects were involved in this study. The PPG signals were taken in resting condition using pulse oximetry. The subject then goes through a stressor test in order to record the physiological changes. The data were collected before and after the test was conducted and later extracted. These samples were then categorised using classification techniques to differentiate between normal and stress condition. Based on the experimentation results, the systolic peak value differences of normal and stress conditions are evident.Therefore, the outcome of this study suggest the reliability of implementing PPG signal for stress recognition.
Reduction of motion artifact of photoplethysmogram signal based on its frequency distribution
In this paper, we proposed and evaluated signal processing algorithms which could efficiently reduce motion artifacts contained in the photoplethysmogram (PPG) signal. Time-domain PPG signal was analyzed to find frequency distribution of the fundamental and harmonic components caused by the arterial blood flow and motion artifact. A new artificial PPG signal could be reconstructed by using only the fundamental and harmonic components caused by the arterial blood flow. This motion artifacts reduced signal was further analyzed in time domain to obtain its AC components by applying peak detection algorithm. The algorithms proposed in this study showed correct saturation of peripheral oxygen (SpO2) values for the PPG signal in which the fundamental and harmonic components were not overlapped.
Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep
Work should be a source of health, pride, and happiness, in the sense of enhancing motivation and strengthening personal development. Healthy and motivated employees perform better and remain loyal to the company for a longer time. But, when the person constantly experiences high workload over a longer period of time and is not able to recover, then work may lead to prolonged negative effects and might cause serious illnesses like chronic stress disease. In this work, we present a solution for assessing the stress experience of people, using features derived from smartphones and wearable chest belts. In particular, we use information from audio, physical activity, and communication data collected during workday and heart rate variability data collected at night during sleep to build multinomial logistic regression models. We evaluate our system in a real work environment and in daily-routine scenarios of 35 employees over a period of 4 months and apply the leave-one-day-out cross-validation method for each user individually to estimate the prediction accuracy. Using only smartphone features, we get an accuracy of 55 %, and using only heart rate variability features, we get an accuracy of 59 %. The combination of all features leads to a rate of 61 % for a three-stress level (low, moderate, and high perceived stress) classification problem.