In this paper, I present a breathing product, Breath Time, and investigate the effect of breathing exercises compared to apps. The breathing time is a product that changes volume to 3d by moving out of the current solution, 2d display environment. As a breathing exercise product, it currently has several modes, to relax, to sleep and to use the pranayama breathing method.
After checking the breathing pattern, the interview was conducted to get opinions on the product. Through the experiment, I identified that the effect of the breathing exercises of the product was similar to the applications. But in terms of the breathing itself, both the apps and the product had a major problem with breathing that was difficult to follow exactly as the breathing instructions.
Interview data was also collected and analyzed and I looked at its potential as a product on the desk.
Introduction
Background
MARS, Mobile App Rating Scale, (Mani et al, 2015) meditation app rating, I focused on level of engagement and PB (Program Based) practice.
Design and Research Aim
Process
GUIs fall short of embracing the richness of human senses and skills that humans have developed through a lifetime of interaction with the physical world. Called Ishii Hiroshi.(Ishii, 1997) At this point I recognized the limitation of 2D display and tried to convert the solution to 3d for more involvement. After research, findings will be applied to the next version of prototype to solve other design problems.
Literature Review
Inducing Deep Breathing
This is a study which tried to see the reaction of changing the light and volume by receiving three kinds of biosignals. (Figure 6) Some of these experiments were said to be relaxed and the rest said that there were opposite reactions.( Schnädelbach et al., 2012). Depending on the person's breathing, the volume of the tent shape changed, making a heartbeat sound along the heartbeat, changing the light and changing the graphics that reflect the tent shape according to EDA. Breath Coach: A smart in-home breathing training system with bio-feedback via VR games Attempts to help breathing in virtual reality continue, apart from practical products and tangibles.
7 The researchers saw in this product the possibility of VR games and biofeedback at home. As a VR test of the breathing experience, it could be explained more with a 3D world, but it has the limitation of graphics, as does the 2D display. There have been attempts to move the product using biofeedback or bring it into virtual reality using biofeedback for breathing training.
If the mental modality of human breathing was a change in surface area in 2d, it would be a change in volume in 3d.
Shape Chage Effect
The rotation angle is their way of visualizing data that can be seen as an area.
Summary
Breath Time: Development of product
Design of Breath Time
In this study, we used the first instructional product scenario as a design studio application, and for further usage potential, I generated the experiment to see the potential as an on-desk product in the discussion section. Based on the power and mechanism from the previous one, I tried to figure out the minimum value of torque to make it work. After drawing the FBD (Free body diagram), I could not find the actual value of tension or other friction due to lack of information about the material or the tension itself.
The previous motor power was 30 N, and it was enough to generate the whole system, so the wire tension on the motor side was 28.1 N. It means that if I use the 1.7 cm pulley, radius, then the motor torque should be around 48 N.cm so that i can easily generate. For under torque 10N.cm, the motor stopped, and for almost 29N.cm is loaded the mechanism will work when the size of the pulley was 2cm radius.
Implementation
Once assembled, wire was run through the tube and tied to the end of the Hoberman bulb. The motor bracket is designed to fit the size of a motor-shaped rectangular shape without one side. These were attached with the bolt and nut using the main body mounting section.
The potential meter was also attached with a bolt and nut, which it had in itself. (figure 16). It was programmed that the position of the servo motor was 0 at first use. Each breath consists of (1) inhale for 5 seconds, exhale for 5 seconds, (2) inhale for 4 seconds, hold for 7 seconds, exhale for 8 seconds, (3) inhale for 7 seconds, hold for 4 seconds exhale for 8 seconds, hold for 4 seconds.
From above, how the position of the servo motor is programmed depending on the time interval. It is also possible to enter functions such as Sin and cos, log functions etc. 14 The software is coded so that when the spinner rotates properly, the mode changes.
Design Study
Finally, the BREATHING consists of 7 seconds of inspiration 4 seconds of breathing, 8 seconds of breathing 4 seconds of breathing. The above three applications were selected because of the breathing method used to relax, the breathing method used to provide good sleep and the breathing method used in yoga. I conducted an in-lab study with three steps, questionnaire, after the app and the product with the same program each and interview.
The purpose of 'apps and product tracking' is to identify if there was a difference between the apps and the programmed product in terms of breath data. Finally, I conducted a semi-structured interview to understand the differences between the two in terms of their experience and the differences between them. In the questionnaire, I asked the participants about the habit of deep breathing, whether they normally take deep breaths in their daily life, and if they do, when and why they do that deep breathing.
I also wanted to figure out the use of a breathing app or product that helps with deep breathing or diaphragmatic breathing. If a person with the same normal breathing pattern sees and watches two different breathing instructions, they can compare the product and the application. To reduce the order effect and the learning effect, the order of breath tracking was created in a Latin square.
I deployed the application and the product with the same program, with at least one order between each other. Experiment with the breathing sequence to get rid of the sequence effect and the learning effect, using the Latin square. Participants were asked to breathe deeply according to the instructions provided by the applications and products while wearing a belt, six times for each breathing method. While the autopsy was being performed, breathing data were collected via the belt every 0.1 second.
Before each trial of this section, I had checked the breath data each time before the deep breath. In the interview section, it was conducted as a semi-structured interview asking the changes that occur in the direction of breathing, for example they were asked "Was it difficult to follow the product or natural when switching from exhalation to inspiration or retention?".
Result
Among the six breathing datasets, three datasets were selected according to the learning effect and the minimum value. 21 In the set of two participant graphs, the range of sensor value data is variable due to the change in sensor sensitivity due to belt tightness. Our interest in the data is not the actual sensor value, but the waveform of the graph, as it looks like as a function of the time variable.
Analysis
The data ranges of the app and the product (P10) were unified and through this we can compare the similarity between two generated graphs. In other words, when the modified data increased while the inspiration caused by the applications, the modified data increased while the inspiration caused by the product also. Similarly, when data is reduced while viewing an app, the product's data is also reduced.
And when we compare the relationship between the coefficient and the meaning of the Pearson coefficient, it can be said that a strong linear relationship is shown. I could conclude that deep breathing from the app and deep breathing from the product were similar. The degree of difference emerged due to the reaction time and breathing ability of each participant.
The rest of the time they tried to inhale and exhale more, which felt like holding. Our first interest in the interview was the difference between the apps and the product in the aspect of breathing that could not be obtained from the breathing data, the difficulty in tracking the app and the product, the difference in breathing in details, etc. I also tried to find out the weaknesses of the product and the possibilities of use.
It has potentials with various possibilities not only for deep breathing but also for other purposes. I would rather use this as a mindfulness product.” P8 commented, “I was more immersed because of the stereoscopic effect.” The possibility of tabletop use existed in terms of respiratory products that have a volumetric change.
Through this it turned out that the product had a similar effect, but the breathing itself compared to the original program has a different cycle. This was caused by human nature in breathing and reaction time to recognize changes.
Discussion
Breathing Instruction
During this section, I can also set the system that corrects the error time and reflects in the next cycle.
3D Volumetric Change, Skeleta vs. Covered
Time-based Instruction
30 I have to check the effect of abstract information in aspect of breathing and on desktop product each. But in aspect of a desktop product, I have to think the context of its use and their natural behavior of breathing, so that I can program or set a product for better use.
Potential Use as an On-desk Product
Limitation
Conclusion
Summary and Findings
Expected Contribution
Future Study
PMID 24640415
2018 International Joint Conference and International Symposium on Pervasive and Ubiquitous Computing and Wearable Computing (pp. 468-471).
Acknowledgement
Appendix
First Design of Breath Time and Initial Prototype
Due to mechanism limits and the time limitation caused by mechanism, it was revised to the breath time that is proposed in this paper.
Data of particpants
