Stress is a naturally occurring psychological response and identifiable by several body signs. We propose a novel way to discriminate acute stress and relaxation, using movement and posture characteristics of the foot. Based on data collected from 23 participants performing tasks that induced stress and relaxation, we developed several machine learning models to construct the validity of our method. We tested our models in another study with 11 additional participants. The results demonstrated replicability with an overall accuracy of 87%. To also demonstrate external validity, we conducted a field study with 10 participants, performing their usual everyday office tasks over a working day. The results showed substantial robustness. We describe ten significant features in detail to enable an easy replication of our models.
Tickling is a type of sensation that is associated with laughter, smiling, or other similar reactions. Psychology research has shown that tickling and laughter can significantly relieve stress. Although several tickling artifacts have been suggested in prior work, limited knowledge is available if those artifacts could evoke laughter. In this article, we aim at filling this gap by designing and developing a novel foot-tickling mechanism that can evoke laughter. We first developed an actuator that can create tickling sensations along the sole of the foot utilising magnet-driven brushes. Then, we conducted two studies to identify the most ticklish locations of the foot’s sole and stimulation patterns that can evoke laughter. In a follow-up study with a new set of participants, we confirmed that the identified stimuli could evoke laughter. From the participants’ feedback, we derived several applications that such a simulation could be useful. Finally, we embedded our actuators into a flexible insole, demonstrating the potential of a wearable tickling insole.
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