Kun-chan Lan

Many studies utilize the signal strength of short-range radio systems (such as WiFi, ultrasound and infrared) to build a radio map for indoor localization, by deploying a large number of beacon nodes within a building. The drawback of such an infrastructure-based approach is that the deployment and calibration of the system are costly and labor-intensive. Some prior studies proposed the use of Pedestrian Dead Reckoning (PDR) for indoor localization, which does not require the deployment of beacon nodes. In a PDR system, a small number of sensors are put on the pedestrian. These sensors (such as a G-sensor and gyroscope) are used to estimate the distance and direction that a user travels. The effectiveness of a PDR system lies in its success in accurately estimating the user's moving distance and direction. In this work, we propose a novel waist-mounted based PDR that can measure the user's step lengths with a high accuracy. We utilize vertical acceleration of the body to calculate the user's change in height during walking. Based on the Pythagorean Theorem, we can then estimate each step length using this data. Furthermore, we design a map matching algorithm to calibrate the direction errors from the gyro using building floor plans. The results of our experiment show that we can achieve about 98.26% accuracy in estimating the user's walking distance, with an overall location error of about 0.48 m.

Tongue features are important objective basis for clinical diagnosis and treatment in both western medicine and Chinese medicine. The need for continuous monitoring of health conditions inspires us to develop an automatic tongue diagnosis system based on built-in sensors of smartphones. However, tongue images taken by smartphone are quite different in color due to various lighting conditions, and it consequently affects the diagnosis especially when we use the appearance of tongue fur to infer health conditions. In this paper, we captured paired tongue images with and without flash, and the color difference between the paired images is used to estimate the lighting condition based on the Support Vector Machine (SVM). The color correction matrices for three kinds of common lights (i.e., fluorescent, halogen and incandescent) are pre-trained by using a ColorChecker-based method, and the corresponding pre-trained matrix for the estimated lighting is then applied to eliminate the effect of color distortion. We further use tongue fur detection as an example to discuss the effect of different model parameters and ColorCheckers for training the tongue color correction matrix under different lighting conditions. Finally, in order to demonstrate the potential use of our proposed system, we recruited 246 patients over a period of 2.5 years from a local hospital in Taiwan and examined the correlations between the captured tongue features and alanine aminotransferase (ALT)/aspartate aminotransferase (AST), which are important bio-markers for liver diseases. We found that some tongue features have strong correlation with AST or ALT, which suggests the possible use of these tongue features captured on a smartphone to provide an early warning of liver diseases.

BACKGROUND: Diagnosing brain disorders, such as Parkinson's disease (PD) or Alzheimer's disease, is often difficult, especially in the early stages. Moreover, it has been estimated that nearly 40% of people with PD may not be diagnosed. Traditionally, the diagnosis of neurological disorders, such as PD, often required a doctor to observe the patient over time to recognize signs of rigidity in movement. MATERIALS AND METHODS: The pedestrian dead reckoning (PDR) system is a self-contained technique that has been widely used for indoor localization. In this work we propose a PDR-based method to continuously monitor and record the patient's gait characteristics using a smartphone. Seventeen patients were studied over a period of 1 year. During the year it became apparent that 1 of the patients was actually developing PD. To the best of our knowledge, our work is the first attempt to use sensors in a smartphone to help identify patients in their early stages of neurological disease. RESULTS: On average, the accuracy of our step length estimation was about 98%. Using a binary classification method-namely, support vector machine-we carried out a case study and showed that it was feasible to identify changes in the walking patterns of a PD patient with an accuracy of 94%. CONCLUSIONS: Using 1 year of gait trace data obtained from the users' phones, our work provides a first step to experimentally show the possibility of applying smartphone sensor data to provide early warnings to potential PD patients to encourage them to seek medical assistance and thus help doctors diagnose this disease earlier.