The use of technologies to enhance human and animal perception has been explored in pioneering research about artificial life and biohybrid systems. These attempts have revealed that augmented sensing abilities can emerge from new interactions between individuals within or across species. Nevertheless, the diverse effects of different augmented capabilities have been less examined and compared. In this work, we built a human–fish biohybrid system that enhanced the vision of the ornamental fish by projecting human participants onto the arena background. In contrast, human participants were equipped with a mixed-reality device, which visualized individual fish trails (representing situation-oriented perceptions) and emotions (representing communication-oriented perceptions). We investigated the impacts of the two enhanced perceptions on the human side and documented the perceived effects from three aspects. First, both augmented perceptions considerably increase participants’ attention toward ornamental fish, and the impact of emotion recognition is more potent than trail sense. Secondly, the frequency of human–fish interactions increases with the equipped perceptions. The mood recognition ability on the human side can indirectly promote the recorded positive mood of fish. Thirdly, most participants mentioned that they felt closer to those fish which had mood recognition ability, even if we added some mistakes in the accuracy of mood recognition. In contrast, the addition of trail sensing ability does not lead to a similar effect on the mental bond. These findings reveal several aspects of different perceived effects between the enhancements of communication-oriented and situation-oriented perceptions.

https://iopscience.iop.org/article/10.1088/1748-3190/aca308/pdf

The perceived effects of augmented trail sensing and mood recognition abilities in a human–fish biohybrid system

Abstract Smart wearable items are becoming more well recognized and are steadily making their way into people’s lives as a result of the ongoing advancement of technology and people’s growing concern for their health. In this work, we investigate the stimulation of physiological signals and the level of happiness indicated by people’s emotions using the linkage of smart gadgets and biological data. To reduce motion artifacts from wearable PPGs, we first suggest a sparse representation-based approach. To address the issue of poor model generalization brought on by individual signal differences (inter- and intra-individual) in human ECG data, a wearable ECG individual signal difference reduction technique based on Balanced Distribution Domain Adaptive (BDA) is also suggested. In addition to making a significant contribution to the advancement of intelligent control technology, medicine, and other fields, it provides an effective baseline for research on the satisfaction level of group music and dancing based on physiological signals. 

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Qian Wang

Papers

The perceived effects of augmented trail sensing and mood recognition abilities in a human–fish biohybrid system

Efficient Modeling and Estimation of Group Music and Dance Happiness via Intelligent Wearable Devices