Regular observation and recording of the changes in body appearance are essential for the process of the treatment of plastic surgery and dermatology, especially aesthetic surgery. Usually, physicians treat patients with medical interviews, pictures of the patient's faces before and after their treatment, anatomical data that including size, location, and color of the affected skin. However, it is difficult to capture the affected area under the same conditions every time because the captured range varies depending on the imaging angle and distance. There is a need to record three-dimensional shape of face parts such as cheek, nose, eye, and chin. Therefore, in this study, the face shape and the skin color were measured using the infrared depth camera and the RGB camera built in the smartphone three-dimensionally. We measured before and after modulating the shape and color of the face, and then, the change in volume and the change in skin pigment of skin color was calculated and visualized. This method makes it possible to analyze the skin shape and color independently of the viewing angle and the illumination direction. In this study, the depth sensor built in the smartphone showed the potential to monitor changes in facial shape and skin color. In the future, it is expected to contribute to the development of telemedicine, in which the patient measures their face at home and gets medical treatment consultation remotely.
Using smartphone images to quantify color presents a noninvasive way to assess jaundice and other color-related biomarkers of the human body. Here we focus on assessing jaundice through accurate bilirubin measurement in adult liver patients, the first time optical imaging has been applied to this cohort. These patients can suffer from very high levels of bilirubin, indicating their severity of liver disease. A smartphone assessment technique for jaundice based around the color of the sclera (white of the eye) extracted from images is being developed, as smartphone imaging enables cheap, non-invasive and quantitative readings. Variations in ambient light cause large changes to recorded pixel values so must be accounted for to ensure that any changes detected are due to changes in jaundice level. Here we suggest the use of an ambient subtraction approach to minimise the effects of ambient light. Pairs of flash/ no-flash images are captured and the extracted values subtracted to yield data as though under a pure flash illumination. We present data demonstrating the technique with a group of healthy adult volunteers. We also present data from a patient study involving adults with liver disease. Images were captured and the bilirubin (jaundice) level predicted from these images before and after subtraction was compared to the ground truth value obtained via blood test. The linear correlation coefficient increased from 0.47 to 0.85 (p < 0.001 in both cases) upon application of subtraction, demonstrating the effectiveness of the technique.
Rapidly evolving technologies like data analysis, smartphone and web-based applications, and the Internet of things have been increasingly used for healthy living, fitness and well-being. These technologies are being utilized by various research studies to reduce obesity. This paper demonstrates design and development of a dataflow protocol that integrates several applications. After registration of a user, activity, nutrition and other lifestyle data from participants are retrieved in a centralized cloud dedicated for health promotion. In addition, users are provided accounts in an e-Learning environment from which learning outcomes can be retrieved. Using the proposed system, health promotion campaigners have the ability to provide feedback to the participants using a dedicated messaging system. Participants authorize the system to use their activity data for the program participation. The implemented system and servicing protocol minimize personnel overhead of large-scale health promotion campaigns and are scalable to assist automated interventions, from automated data retrieval to automated messaging feedback. This paper describes end-to-end workflow of the proposed system. The case study tests are carried with Fitbit Flex2 activity trackers, Withings Scale, Verizon Android-based tablets, Moodle learning management system, and Articulate RISE for learning content development.
The use of mobile and web tools in health care has greatly improved interactions between doctors, patients and healthcare professionals in the past few years. According to the University of Texas Health Science Center at San Antonio (UTHSCSA) almost 75% of the 296,980 women in the United States that are diagnosed with breast cancer will have hormone receptor-positive breast cancers. Endocrine hormonal therapy (EHT) is very effective for nearly all women with hormone-receptive positive tumours and is the most widely prescribed one. The dedicated use and adherence to this therapy for 5 years has also shown larger reduction in recurrence [6]. However, even with such proven benefits, the adherence is limited to just 33% of all the women who are prescribed. In such cases, the use of interactive easy-to-use apps would promote and improve adherence [2]. Such apps should enable fast responses to patient queries, guide patients through treatment, help them understand symptoms, motivate them through educational content, and prompt interaction with their peers. In this paper, we describe an approach for accelerating app prototyping using the existing Google Android platform and converting it to a cross-platform web application thereafter. Google Firebase [1] is used as a database server to assist in monitoring and sending notifications to users without compromising the safety and security of patients' data. The proposed system and approach can also be further tailored for similar technology-assisted health promotion and intervention studies. The effectiveness of the approach is evaluated through a randomized controlled study with breast cancer patients conducted by the UTHSCSA research team.
We propose an objective measurement protocol to evaluate the autofocus performance of a digital still camera. As most pictures today are taken with smartphones, we have designed the first implementation of this protocol for devices with touchscreen trigger. The lab evaluation must match with the users' real-world experience. Users expect to have an autofocus that is both accurate and fast, so that every picture from their smartphone is sharp and captured precisely when they press the shutter button. There is a strong need for an objective measurement to help users choose the best device for their usage and to help camera manufacturers quantify their performance and benchmark different technologies.