Engineering Issues in Physiological Computing

Abstract

Prototypes of physiological computing systems have appeared in countless fields, but few have made the leap from research to widespread use. This is due to several practical problems that can be roughly divided into four major categories: hardware, signal processing, psychophysiological inference, and feedback loop design. This chapter explores these issues from an engineering point of view, discussing major weaknesses and suggesting directions for potential solutions. Specifically, some of the topics covered are: unobtrusiveness and robustness of the hardware, real-time signal processing capability, different approaches to design and validation of a psychophysiological classifier, and the desired complexity of the feedback rules. The chapter also briefly discusses the challenge of finding an appropriate practical application for physiological computing, then ends with a summary of recommendations for future research.

Measuring Trust with Psychophysiological Signals: A Systematic Mapping Study of Approaches Used

“…The nature and choice of data analysis techniques used to analyze the data to infer trust in studies assessing trust with psychophysiological signals, such as ensemble (the combination of two or more machine learning algorithms), dynamic (single machine learning algorithm), or static (a basic statistical test for the patterns, significance, or relationships between variables) methods [17,35,36].…”

“…However, the use of psychophysiological signals for assessing trust can be elusive and multifaceted [17]. For instance, there is no one-to-one mapping between trust states and psychophysiological signals.…”

“…The most frequently used and maximum number of combined psychophysiological signals used in prior studies assessing trust with psychophysiological signals, as the trade-off between the accuracy and obtrusiveness of physiological sensors could influence the researcher's choice of physiological sensors and, ultimately, the results [17,40].…”

A Psychophysiological Model of Firearms Training in Police Officers: A Virtual Reality Experiment for Biocybernetic Adaptation

“…This is important since it allows differentiating between the physiological signature of being wearing the headset and sensors as well as being standing holding the controllers and the cognitive and cardiovascular cost of shooting. In other words, this provides a certain level of context awareness, so the adaptive system would be able to create more personalize modulations (Novak, 2014). Finally, the adaptive rule created using the BL Engine tool (see Figure 7) shows how to move from the theoretical model to a real software implementation by: (i) combining both cardiovascular and neurophysiological features into a clearly defined and transparent adaptive rule, (ii) allowing the modifications of the rule and values in real-time, so speeding up threshold's adjustment for realtime adaptation, and (iii) fully integrating the VR simulator with the BL Engine, so a bidirectional communication will take place enabling research on biofeedback visualization strategies (Kuikkaniemi et al, 2010).…”

“…Including physiological sensing technologies in novel immersive and interactive technologies such as augmented or VR has been posed as the new generation of gaming interfaces (Pope et al, 2014). This integration is far from being simple and straightforward since including the human in the loop carries several complexities widely discussed (Novak, 2014). Nevertheless, current advances in sensing technologies, game engine software, and hardware accessibility have allowed the development of very sophisticated biocybernetically adaptive systems empowered with the immersivity and realism of 52 (Kosunen, 2018;.…”