I’d like to explore areas in Medical Engineering E-Health and Sports & Fitness Persuasive Technology. Specifically I wish to explore future products for patient-driven health care using the “Internet of Things”. I’m interested in collecting personal and environmental data using connected daily objects, and visualize the big data through everyday objects rather than graphs of metrics. However, my ideal product will still use current technology of data processing and information visualization to give users suggestive information. Possible users can be health-conscious individuals or active individuals who care about performance and environmental qualities.
By reading Martha G. Russell’s “Adaptive mediated persuasion technologies”, I noticed that the effectiveness of persuasion technology depends on having the right information at the right time. It is why mobile has the best practices of persuasion for marketing and advertising in our information-rich lifestyle. My interest in E-health and Fitness Persuasive Technology is also derived from my concern of being too late to know the issues of my body. When we notice pain and illness, or seeing physical changes, the symptoms are already accumulated for a period of time that may or may not be cured by medical professionals. Therefore, I wish to explore the hardware sensors, software processing and data visualization for a quantified tracking of our body and environment, and to prevent accumulation or take actions when unfavorable metrics appear.
According to Melanie Swan’s “Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking”, health and health care are understood and realized differently today because the future will be one of patient power, patients engaged and taking control cover their own health and healthcare. That’s why “patient-driven” has been the one of most popular word in health care. As she describes, patient driven health care has an increased level of information flow, transparency, customization, collaboration and patient choice and responsibility-taking, as well as quantitative, predictive and preventive aspects. Therefore, I want to learn to use sensors for individuals to measure, track, and experiment with health metrics and collaborate with peers and doctors. As a result, individuals will be able to get Consumer Personalized Medicine. As Swan states, it is the further step of individual collecting and synthesizing their own data and using it to proactively manage their health. There have been practices such as 23andMe, which offers Direct-to-consumer personalized genomic testing. For environmental tracking, OpenSpime is developing Internect-connected geosensors to capture ongoing real-time readings of pollution and other air quality indicators and automatically log the information to a display built on GoogleMaps.
In order to achieve quantified tracking of our body and surrounding environment, the Internet of Things (IOT) and Wearable Computing have been the trend of health and personal fitness self-tracking devices. As Melanie Swan claims in her “Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self”, IOT is connecting real-world objects to the Internet with tiny sensors. There have been practices such as the Pebble Watch and the Fitbit wristband. However, they don’t sync in real-time but only when connected to a computer. She explains that the next-generation of these kinds of products appears to be a mix of an accelerometer, GSR sensor, temperature sensor and heart rate sensor. Thus, I expect to be able to experiment with these sensors for continuous monitoring and connected real-time data transmission.
As Swan states, ideally IOT devices will have real-time feedback and personalized recommendations, I wish to explore new ways of real-time feedback such as information visualization via daily objects. By that, I mean physically but not digitally on a watch or computer display. I want to have the quantified data to be visualized in a more creative way such as the Physical Notification Display lamp called “The Email Gauge” made by Tomorrow Lab. It is a live, physical display of the percentage of a user-defined term in their email box. It folds or expands depends on how much the user-defined category of emails received on that day. This makes me imagine coming home and instantly seeing my health/environment data in my everyday objects. If the size of my lamp, or the color of my light bulbs can sync with my sensors wirelessly in real-time and tell me whether there is an unfavorable trend in my daily life, I won’t bother to check out the data from my wristband on my computer or read through the metrics and explanations everyday. In contrast, I will be pleased and motivated to discover my health trends and be notified instantly and effortlessly.
Nevertheless, as Swan claims, data still need to translated in something human-usable to find out what action to take as a result of seeing the information. Therefore, when the physical notification display alerts a user to take an action, there should be personalized recommendations for behavioral change. Such as the Eatery application, which lets users keep a photo-based nutrition diary and offers calculation and suggestive information toward a healthier life. In conclusion, I look forward to exploring, and making IOT devices to track health or environmental data as well as learning to process and visualize it persuasively via everyday objects.
 Martha G. Russell. Adaptive mediated persuasion technologies, Proceeding Persuasive ‘11
 Melanie Swan. Emerging Pathient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking, Int. J. Environ. Res. Public Health 2009, 6. P492
 Melanie Swan. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self. J. Sens. Actuator Netw. 2012, 1, P218
 Melanie Swan. Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self. J. Sens. Actuator Netw. 2012, 1, P233