Gamification and virtual reality (VR) are increasingly being explored for their potential to enhance mindful practices and well-being. We further explore the potential of gamification and VR for breath awareness and mindfulness, and contribute Stairway to Heaven, a VR artifact that combines gamification with respiratory sensor biofeedback to cultivate mindful awareness of breathing. In our mixed-method study with 21 participants, we evaluated the usability and effectiveness of our artifact in promoting breathing frequencies between 4 and 10 breaths per minute (BPM). We integrate breath-driven teleportation as a virtual locomotion technique (VLT) using respiratory biofeedback to gamify progression through a virtual wilderness. Additionally, we supplement our design with a mindfulness audio guide. The results of our user study showcase the potential of combining actionable gamification and VR, guided mindfulness, and breath-driven VLT to foster slow breathing self-regulation successfully.

Unsupervised physical rehabilitation traditionally has used motion tracking to determine correct exercise execution. However, motion tracking is not representative of the assessment of physical therapists, which focus on muscle engagement. In this paper, we investigate if monitoring and visualizing muscle engagement during unsupervised physical rehabilitation improves the execution accuracy of therapeutic exercises by showing users whether they target the right muscle groups. To accomplish this, we use wearable electrical impedance tomography (EIT) to monitor muscle engagement and visualize the current state on a virtual muscle-skeleton avatar. We use additional optical motion tracking to also monitor the user’s movement. We conducted a user study with 10 participants that compares exercise execution while seeing muscle + motion data vs. motion data only, and also presented the recorded data to a group of physical therapists for post-rehabilitation analysis. The results indicate that monitoring and visualizing muscle engagement can improve both the therapeutic exercise accuracy during rehabilitation, and post-rehabilitation evaluation for physical therapists.

In this paper, we propose EIT-kit, an electrical impedance tomography toolkit for designing and fabricating health and motion sensing devices. EIT-kit contains (1) an extension to a 3D editor for personalizing the form factor of electrode arrays and electrode distribution, (2) a customized EIT sensing motherboard for performing the measurements, (3) a microcontroller library that automates signal calibration and facilitates data collection, and (4) an image reconstruction library for mobile devices for interpolating and visualizing the measured data. Together, these EIT-kit components allow for applications that require 2- or 4-terminal setups, up to 64 electrodes, and single or multiple (up to four) electrode arrays simultaneously.