Lifelog agent for human activity pattern analysis on health avatar platform.

OBJECTIVES: To provide accurate personalized medical care, it is necessary to gather individual-related data or contextual information regarding the target person. Nowadays a large number of people possess smartphones, which enables sensors in the smartphones to be used for lifelogging. The objective of the study is to analyze human activity pattern by using lifelog agent cooperating with the Health Avatar platform. METHODS: Using the lifelog measured by accelerometer and gyroscope in a smartphone at a 50 Hz rate, the agent reveals how long the user walks, runs, sits, stands, and lies down, and this information is summarized by hours. The summaries are sent to the Health Avatar platform and finally are written in the Continuity of Care Record (CCR) format. RESULTS: The lifelog agent is successfully operated with the Health Avatar platform. In addition, we implement an application that displays the user's activity patterns in a graph and calculates the metabolic equivalent of task based calorie burned by hour or by day using the lifelog of the CCR form to show that the lifelog can be used as medical records. CONCLUSIONS: The agent shows how lifelogs are analyzed and summarized to help activity recognition. We believe that our agent demonstrates a way of incorporating lifelogs into medical care and a way of exploiting lifelogs in a medical format.

U-healthcare System using Smart Headband.

The wearable healthcare system should be not only small and easy for users to wear them but accurate in measuring physiological signals in daily life without any inconvenience. To meet these requirements, we propose the U-healthcare System which consists of a Smart Headband and a Health-state Monitor program. It can monitor the health-state in walking and running. The Smart Headband measures a photo-plethysmography (PPG) signal at the forehead for monitoring the heart rate and acceleration signals. The Health-state Monitor program is developed to show the health-state. Health-state Monitor program is operated in the handheld computer, such like (Ultra Mobile Personal Computer (UMPC). It provides the user PPG, heart rate and steps for the precise measurement, Adaptive Noise Cancellation (ANC) is introduced. The Smart Headband and the Health-state Monitor program are communicated by the WPAN using Zigbee protocol. If the heart rate is higher or lower than usual, the Health-state Monitor program sends the SMS message to the designated person, such as family or the attending physician in the medical center, to inform the emergency situation.

Personalized healthcare through intelligent gadgets.

An intelligent gadget is a wearable platform which is reconfigurable, scalable, and component-based and which can be equipped, carried as a personal accessory, or in a certain case, implanted internally into a body. Various kinds of personal information can be gathered with intelligent gadgets, and that information is used to provide specially personalized services to people in the ubiquitous computing environment. In this paper, we show a personalized healthcare service through intelligent gadgets. A service based on intelligent gadgets can be built intuitively and easily with a context representation language, called the intelligent gadget markup language (IGML) based on the event-condition-action (ECA) rule. The inherent nature of extensibility, not only environmental information but also physiological information can be specified as a context in IGML and can be dealt with an intelligent gadget with ease. It enables intelligent gadgets to be adopted to many different kinds of personalized healthcare services.

Adaptive noise cancellation using accelerometers for the PPG signal from forehead.

For the upcoming ubiquitous computing environments in the u-health areas involve the measurement of physiological signals in the daily life. However, the measurement of those signals, such as the photoplethysmorgraphy (PPG), and the electrocardiogram (ECG), requires being still tight during the measurement in order to get the accurate result preventing noises caused by the casual movement. In this paper, we propose a method to obtain the accurate physiological signals in the situation where the little movement is allowed. By measuring PPG and motion signals at the forehead during in motion, we calibrate the distorted PPG signal with the motion signals. We show that the calibrated PPG signal is accurate enough by comparing with the result that is measured at the finger without any movement.