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.
Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step
Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.
Research on Control and Planning of Soft-Wing UAV for Haze Removal
The existence of fog and haze has caused great pollution to the environment. The research of removing haze is a hot topic in recent years. In this paper, a special haze removal algorithm based on Soft- Wing UAVis proposed. In this algorithm, two track control strategies are designed based on the Soft-Wing UAV model transfer function. A SIMULINK platform based mathematical model of Soft-Wing UAV is built to testified effectiveness of the two track control strategies. An autonomous ergodic-planning algorithm with greedy strategy and foldback algorithm for Soft-Wing UAV is designed. Within the algorithm, the haze regional has been modelled, the cost matrix is constructed, and the planning and ergodic strategy is proposed. Finally, the experimental results validates the reliability of the algorithm in the application of haze removal with the proposed evaluation Index.