PHIL: an abstract visualization of your immune system Sander Biesmans Jeroen Hol Joost Liebregts Ineke Neutelings Anika van der Sanden Pepijn Verburg Department of Industrial Design Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands a.g.p.biesmans@student.tue.nl j.hol@student.tue.nl j.m.f.liebregts@student.tue.nl
Abstract We believe in a life wherein you can be your best self. Stress and diseases are a common part of life. However, the decisions you make can influence your immune system and increase the risk of feeling stressed and being more vulnerable to diseases. Our application PHIL uses your available data to visualize your immune system by looking at your behavior patterns over time. In this way we want to give the user an abstract visualization tool to reflect and act on how their lifestyle influence his/her immune system.
i.m.p.Neutelings@tue.nl m.j.v.d.sanden@student.tue.nl p.verburg@student.tue.nl
Author Keywords Data visualization, immune system, health app. ACM Classification Keywords H.5.2.[Information Interfaces and Presentation (e.g. HCI)]: Data mapping; D.2.12 [Software Engineering]: Interoperability Introduction Stress and diseases are a common part of life, and can have a contingence impact on life. The mutual relationships between stress, diseases and the immune system are complex as these influence each other in both a negative and
positive manner.1 However, a strong immune system decreases the chance of getting ill and can reduce the effects of stress. We believe in a life wherein everybody can be their best self by giving insight into their personal immune system. Interpretation by user Nowadays it is possible to measure and collect a lot of data, and most of the healthcare applications at the moment try to support you by giving biased suggestions based on this limited data. Every person is unique and since not all relevant data is collected, it is difficult to make grounded decisions and suggestions. Thus the interpretation of the data must happen by the user, and not by the application. Multidimensional data Many healthcare applications show onedimensional data that is often difficult to interpret as it does not provide the user with all the relevant information in a clear way. The most interesting things are often the relationships between parameters as these provide deeper insight into the data. This means that multidimensional data is preferred over onedimensional data. Relative data Differences in the data show interesting moments for examination, and can be visualized with absolute and relative data. As a person can be born with a weaker immune system than any other person, it is not interesting to classify this person on an absolute scale as this does not
provide any meaningful insights. It is much more interesting to look at the relative data of this single person. Our vision is thus that absolute numbers are obsolete. Visual compositions Many data visualizations use line graphics wherein the timeline is represented on one of the axis. The goal of these data visualization are often to show correlations between data changes over time. Changes in the data can also be presented by comparing visual compositions with a time interval. Visual compositions enable more freedom with multidimensional data visualizations as the data visualization moves away from the strict and structured line graphic. Forced hyperlapse and future prediction In most healthcare applications it is possible to look into the data of the past. From our perspective not many application users do this often. However, looking into the past can provide valuable insights into patterns that develop over time. We think it is valuable for the user to look into their past data regularly to look for interesting patterns. To make the user aware of the effect of their behavior on the future, we provide them with an estimation of their future immune system. In summary, we want to give people a tool to visualize their data related to their immune system, to let them reflect and think about their behavioral influences.
Figure 1
Overview of different features of PHIL
PHIL Our vision on data visualization is presented in our concept PHIL, see figure 1. PHIL is an application for the smartphone that provides the user an overview of his/her immune system over time, with the possibility to explore different aspects of his/her immune system in more detail. Parameters To determine different parameters that are of influence on the immune system, we did a literature study. The parameters we found were compared to the information from the StudentLife9 Dataset, and concluded in four aspects for the first prototype: 1. length and quality of sleep2,3,4 2. physical exercise5 3. disorganized behavior6 4. stress level7,8
Other aspects that can have an influence on your immune system are a.o. weather circumstances and eating behavior. In the future, data about these aspects can be collected (for example through wearable sensors or smartphones) and used within the application. Data visualization / Front End We chose to make one visual overview of the immune system, which is influenced by different parameters. This visual provides you with a quick interpretation of the current state of your immune system. The bigger the total image, the healthier your immune system is. Every aspect has an influence on the total height.
When opening the application, the user gets an overview of the transformation of their immune system over time, using a hyperlapse. This hyperlapse goes through all generated data, and a time indication is displayed at the bottom of the screen, which will help the user to put the visualization in time perspective. By showing this hyperlapse every time the application is opened, the user is forced to look at the development of the immune system every day. When approaching the current state of the immune system, the hyperlapse will slow down and stop on today for a short moment. To give the user a prediction of what happens to the immune system when the user continues the current behaviour, the hyperlapse will shortly bounce a few days into the future. After the prediction, the hyperlapse stops on today, which then is the home screen. In this home screen the user can tap on each aspect to get a more detailed view of the parameters. The user can explore this specific parameter over time, using a parameter-specific hyperlapse, or compare his immune system with the general immune system of a community of people. The image of the communities’ immune system will be placed on top of the image of the user’s immune system in a different color. The user can explore the community by adjusting the parameters linked to the characteristics of the comparison group (age, location, level of activity). The user can search for the different keywords suggested by the application if he/she wants to know more. These keywords are dataspecific, and will be different for each user. The
application searches for patterns and compares these to literature research, to give suggestions on information that could be relevant to the user to look into. Back End PHIL queries data from a MySQL-version of the real life StudentLife Dataset. [9] To save the queried data, the following steps are made: i) a range between two dates is generated as reference timestamp, ii) PHIL only asks for the data of the wanted user’s ID number and the reference timestamps, and iii) PHIL requests all the data we want using a MySQL-query. The back end outputs 4 immune system aspects on a scale from 1 to 5, respectively bad to good, to the data visualization/front end. The calculation of these 4 aspects will be discussed below. Length and quality of sleep PHIL queries ‘Sleep duration’ on a scale from 1-19 hours, and ‘Sleep rate’ on a scale from 1-4; 1 = very good, 2 = fairly good, 3 = fairly bad, 4 = very bad. The best way would be to calculate the mean of sleep time for each person, and to use this as reference for sleep variation. However, for this application the sleep variation is calculated by taking 8 hours of sleep per day as reference. The following calculation is based on the designers’ intuition.
Physical exercise PHIL queries ‘Exercise time’ and ‘Walking time’ on a scale from 1-5; 1 = no, 2 = < 30 min, 3 = 30-60 min, 4 = 60-90 min, 5 = > 90 min. A Pythagoras calculation is used wherein ‘Exercise time’ and ‘Walking time’ represent the two straight sides which are transformed by a weight factor; respectively 0.9 and 0.1. This calculation is based on the designers’ intuition.
Disorganized behavior PHIL queries ‘Disorganized level’ on a scale from 1-5; 1 = not at all, 5 = extremely. The back end inverts these values. Stress level PHIL queries ‘Stress level’ on a scale from 1-5; 1 = little stressed, 2 = definitely stressed, 3 = stressed out, 4 = feeling good, 5 = feeling great. Stress level is mapped as 3, 2, 1, 4, 5 and is outputted, respectively as a number from 1-5. For the future prediction we look into the data from 4 days ago to the current moment. With this data PHIL retrieves a linear extrapolation formula with which PHIL extrapolate values till the day after tomorrow.
Discussion We presented PHIL, a tool for visualizing the user’s immune system to enable reflection of the user’s behavior. PHIL is still a work in progress and a non-evaluated prototype. We expect that everyday users could benefit from PHIL by improving their behavior and immune system, so that they can become their best self everyday. PHIL is not a real doctor. So it is possible that if the user is ill, PHIL indicates that he/she is healthy. Also, the good and bad influences on your immune system are now based on research of the general human body. Every person is unique, thus if something is actually good or bad can differ per person. In the future, PHIL can be expanded with more parameters to give a personalized and grounded indication. For now, PHIL tries to give the user a glimpse of his/her immune system. However, the immune system is complex and the effect of each parameter can be different than presented with PHIL. We invite the research community to investigate parameters and their influence on the immune system. The keywords are presented to give the user a starting point for research about his/her own behavior. Thus the keywords are meant as suggestions and not as conclusions. More user research is needed to find out how people deal with this issue. In the future, the data visualization can be used as live wallpaper on your desktop. In this way the user gets unconsciously an impression of their immune system visualization without actively opening the application.
Acknowledgements
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Herbert, T.B., and Cohen, S. (1993). Stress and immunity in humans: a metaanalytic review. American Psychosomatic Society.
8.
O’Leary, A. (1990). Stress, emotion, and human immune function. Psychological Bulletin. 1990 Nov; 108(3): 363–382.
9.
Wang, Rui, et al. “StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones.” In Proceedings of the ACM Conference on Ubiquitous Computing. 2014. Website: http://studentlife.cs.dartmouth.edu/datas et.html
The analysis in this paper relies on the data collected for the StudentLife Dataset [1].
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