Peripheral paced respiration

Moraveji, Neema; Olson, Ben; Nguyen, Truc; Saadat, Mahmoud; Khalighi, Yaser; Pea, Roy; Heer, Jeffrey

doi:10.1145/2047196.2047250

Cited by 73 publications

(22 citation statements)

References 11 publications

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“…We can speculate that interacting through interbodily resonance and mutual incorporation is essential when attempting to directly affect a participant's pre-cognitive physiological behaviour through an adaptive environment. Confirming this speculation is a study by Moraveji et al (2011) who showed such unaware behaviour in a study in which participants adjusted their respiratory rate to a peripheral on-screen feedback stimulus while focusing on their work. An indirect confirmation of workings of interbodily resonance and mutual incorporation can be gained from situations without such feedback-based manipulations in which participants did not change their behaviour.…”

Section: Interbodily Resonance Between Human and Environmentmentioning

confidence: 90%

Reciprocal Control in Adaptive Environments

et al. 2017

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Computing has become an established part of the built environment augmenting it to become adaptive. We generally assume that we control the adaptive environments we inhabit. Using an existing adaptive environment prototype, we conducted a controlled study testing how the reversal of control (where the environment attempts to influence the behaviour of the inhabitant) would affect participants. Most participants changed their respiratory behaviour in accordance with this environmental manipulation. Behavioural change occurred either consciously or unconsciously. We explain the two different paths leading participants to behavioural change: (i) we adapt the model of interbodily resonance, a process of bodily interaction observable between, for example, partners engaged in verbal dialogue, to describe the unconscious bodily response to subtle changes in the environment and (ii) we apply the model of secondary control, an adjustment of one's own expectations to maintain the pretence of control, to describe conscious cognitive adaptation to the changing environment. We also discuss potential applications of our findings in therapeutic and other settings. RESEARCH HIGHLIGHTS • We describe an experimental study of the control relationship in biofeedback environments. • Inhabitants on average synchronize their physiological behaviour when they lose control over the environment. • Inhabitant behaviour is explained through a form of interbodily resonance and secondary control. • We see potential therapeutic uses of combining biofeedback and automated control.

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Section: Interbodily Resonance Between Human and Environmentmentioning

confidence: 90%

Reciprocal Control in Adaptive Environments

et al. 2017

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“…The trainees follow the tutor's instruction to focus on and regulate their breathing pattern in some mind-body practice (Brown & Gerbarg, 2005;Pal & Velkumary, 2004), such as yoga, meditation, or mindfulness training. Recently, an increasing number of portable devices (Choi & Ishii, 2020;Reiner, 2008), mobile apps, and novel interactive systems (Ghandeharioun & Picard, 2017;Miri et al, 2020;Moraveji et al, 2011;Paredes & Chan, 2011;Paredes et al, 2018;Patibanda et al, 2017;Yu et al, 2015b; are developed for respiration training. Based on the guidance provided by these technology-mediated training systems, they can be mainly divided into two types: preset feedforward guidance systems and real-time biofeedback systems.…”

Section: Respiration Training and Breathing Guidancementioning

confidence: 99%

ViBreathe: Heart Rate Variability Enhanced Respiration Training for Workaday Stress Management via an Eyes-Free Tangible Interface

Hendriks

et al. 2021

International Journal of Human–Computer Interaction

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Slow breathing guiding applications increasingly emerge, showing promise for helping knowledge workers to better cope with workaday stress. However, standard breathing guidance is non-interactive, with rigid paces. Despite their effects being proved, they could cause respiratory fatigue, or lack of training motivation, especially for novice users. To explore new design possibilities, we investigate using heart rate variability (HRV) data to mediate breathing guidance, which results in two HRV-enhanced guidance modes: (i) responsive breathing guidance and (ii) adaptive breathing guidance. These guidance modes are implemented on a soft haptic interface named "ViBreathe". We conducted a user test (N = 24), and a one-week field deployment (N = 4) with knowledge workers, to understand the user experience of our design. The HRV-enhanced modes were generally experienced to reduce tiresome and improve engagement and comfort. And Vibreathe showed great potential for seamlessly weaving slow breathing practice into work routines. We thereby summarize related design insights and opportunities.

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“…4. Physiological level: under parasympathetic control, i.e., blood pressure, heart rate, heart rate variability, galvanic skin response (skin conductance), cortisol level, and pupil diameter, and under sympathetic and parasympathetic control, i.e., respiratory frequency [9,10].…”

Section: Range-anxiety Expressionmentioning

confidence: 99%

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

Musabini

Nguyen

Rouyer³

et al. 2020

MTI

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The electrification of vehicles is without a doubt one of the milestones of today’s automotive technology. Even though industry actors perceive it as a future standard, acceptance, and adoption of this kind of vehicles by the end user remain a huge challenge. One of the main issues is the range anxiety related to the electric vehicle’s remaining battery level. In the scope of the H2020 ADAS&ME project, we designed and developed an intelligent Human Machine Interface (HMI) to ease acceptance of Electric Vehicle (EV) technology. This HMI is mounted on a fake autonomous vehicle piloted by a hidden joystick (called Wizard of Oz (WoZ) driving). We examined 22 inexperienced EV drivers during a one-hour driving task tailored to generate range anxiety. According to our protocol, once the remaining battery level started to become critical after manual driving, the HMI proposed accurate coping techniques to inform the drivers how to reduce the power consumption of the vehicle. In the following steps of the protocol, the vehicle was totally out of battery, and the drivers had to experience an emergency stop. The first result of this paper was that an intelligent HMI could reduce the range anxiety of the driver by proposing adapted coping strategies (i.e., transmitting how to save energy when the vehicle approaches a traffic light). The second result was that such an HMI and automated driving to a safe spot could reduce the stress of the driver when an emergency stop is necessary.

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Peripheral paced respiration

Cited by 73 publications

References 11 publications

Reciprocal Control in Adaptive Environments

Reciprocal Control in Adaptive Environments

ViBreathe: Heart Rate Variability Enhanced Respiration Training for Workaday Stress Management via an Eyes-Free Tangible Interface

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

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