New methods for stress assessment were developed in the last years as a result of an unprecedented evolution in consumer electronics and miniaturization. Others were made possible from a better understanding of stress and its effects on the Human being at several levels: physiological, behavioral or physical. The diversity of alternatives, asshown in this section, allows for solutions to be used in specific scenarios with increased accuracy and commodity (e.g. driving vehicles, working at the computer). In this section we analyze in detail the characteristics of each of these new methods and in Section 6 we provide a critical analysis and a comparison between them.
Wearables
One of the latest trends in stress management is being fostered by wearable devices. Indeed, in the last years there was a major development in consumer electronics, with devices being used for acquiring physiological signs.
Smartphones
The evolution witnessed in the field of smartphones in the last years also led to the emergence of a new paradigm: wellness mobiles. Technological developments make it possible for health-care professionals to have access to comprehensive real-time patient data. Likewise, users can also continuously track their health on the go, build a comprehensive history and receive real-time advice or warning.
Indeed, mobile phones have a growing number and variety of sensors that can nowadays be leveraged to produce, in the near future, what can be called as personal wellness dashboards: devices with the ability to measure our heart rate or body temperature and quickly analyze our state of health. This may make personal health care cost-effective, decreasing the use of emergency care.
Some mobile apps take advantage, to some extent, of the sensors currently present in smartphones.
Although, in many cases, some of these apps lack proven scientific validity, their low cost and their availability makes them easily reach a significant number of users.
The majority of existing apps use the smartphones’ builtin sensors. Azumio’s Stress Check uses the camera and light features of the smartphone to measure heart rate. A similar approach is followed by other apps (e.g. StressViewer). There is also a significant number of apps dedicated not to measuring stress but to decreasing or coping with it, namely through breathing exercises, with visual or sound aids. Stress Releaser is one such app. Another example is DeStressify, that is based on music and specific exercises. There are also apps that use specific hardware, such as PIP Relax and Race, which is based on an electrodermal activity sensor. In this specific app, the user takes part in a race where victory is achieved only by out-relaxing the opponents. A generally competitive activity is thus changed into a relaxing one, with real-time biofeedback. Similar apps exist for this specific hardware. DroidJacket requires the use of VitalJacket – a shirt that embeds an electrocardiogram sensor, allowing a continuous monitoring of the patient.
The work described in also uses a specific sensor platform (Personal Biomonitoring System), in parallel with the smartphone, to monitor the level of stress of the smartphone user.
Other smartphone-based approaches are based on the changes in the speech production process, that happen during stress. To this end, these applications use the microphones embedded in the mobile phones. StressSense is one of such applications, based on a classifier that can robustly identify stress across multiple individuals in diverse acoustic environments. There are also authors who look at the behavior of smartphone users for stress indicators. Although not in a conclusive manner, in the authors find significant differences in location traces, visible bluethooth devices and phone call patterns when comparing stressful with stress free periods.
Computer Vision
Many different image sources can be used to monitor stress, the most frequently used being the Human face. Although cultural differences can intensify facial expression of emotions, there is considerable scientific evidence that emotions are communicated in distinct facial displays across cultures, age and gender. These approaches can be classified as two-dimensional or three-dimensional. Their main difference is that the first tries to recognize features directly from a two-dimensional decomposition/transformation of the image, and is generally not sensible to rotations and translations of the face.
The authors apply optical computer recognition algorithms to detect facial changes due to low and highstressor performance demands, with the aim to develop an approach suitable to be used by astronauts. This approach takes as input images from the whole face. On a similar approach but on a different field of application, Gao et al. present a system for detecting stress from facial expressions
in car drivers, on the other hand, consider only pupil diameter (together with physiological signals), to assess stress. To this end, they make use of a specific camera-based eye-tracking system. Speech and Other Linguistic Features
This section describes approaches for stress assessment based on vocal cues such as speed, rhythm or intonation. Interestingly, the variability introduced by stress or emotion can severely reduce speech recognition accuracy. Thus, the importance of techniques for detecting or assessing the presence of stress to improve the robustness of speech recognition systems.
The present a hierarchical framework, which consists of three layers of classifiers, for automatic stress detection in English speech utterances: a linguistic classifier, an acoustic classifier and an AdaBoost classifier. The paper presents accuracy rates higher than 90%. In a related approach, Imoto et al. address sentence-level stress detection of English for Computer-Assisted Language Learning by Japanese students. Stress models are set up by considering syllable structure and position of the syllable in a phrase, providing diagnostic information for students.