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.

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.

Data gathering in wireless sensor networks (WSNs) is one of the major sources for power consumption. Compression is often employed to reduce the number of packet transmissions required for data gathering. However, conventional data compression techniques can introduce heavy in-node computation, and thus, the use of compressive sensing (CS) for WSN data gathering has recently attracted growing attention. Among existing CS-based data gathering approaches, hierarchical compressive data gathering (HCDG) methods currently offer the most transmission-efficient architectures. When employing HCDG, clustering algorithms can affect the number of data transmissions. Most existing HCDG works use the random clustering (RC) method as a clustering algorithm, which can produce significant number of transmissions in some cases. In this paper, we present a compressibility-based clustering algorithm (CBCA) for HCDG. In CBCA, the network topology is first converted into a logical chain, similar to the idea proposed in PEGASIS [1], and then the spatial correlation of the cluster nodes' readings are employed for CS. We show that CBCA requires significantly less data transmission than the RC method with a little recovery accuracy loss. We also identify optimal parameters of CBCA via mathematical analysis and validate them by simulation. Finally, we used water level data collected from a real-world flood inundation monitoring system to drive our simulation experiments and showed the effectiveness of CBCA.

Background: For several years now, research teams worldwide have been conducting high-tech research on the development of acupuncture robots. In this article, the design of such an acupuncture robot is presented. Methods: Robot-controlled acupuncture (RCA) equipment consists of three components: (a) Acupuncture point localization, (b) acupuncture point stimulation through a robotic arm, and (c) automated detection of a deqi event for the efficacy of acupuncture point stimulation. Results: This system is under development at the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan. Acupuncture point localization and acupuncture point stimulation through a robotic arm works well; however, automated detection of a deqi sensation is still under development. Conclusions: RCA has become a reality and is no longer a distant vision.

In traditional Chinese medicine (TCM), pulse diagnosis is one of the most important methods for diagnosis. A pulse can be felt by applying firm fingertip pressure to the skin where the arteries travel. The pulse diagnosis has become an important tool not only for TCM practitioners but also for several areas of Western medicine. Many pulse measuring devices have been proposed to obtain objective pulse conditions. In the past, pulse diagnosis instruments were single-point sensing methods, which missed a lot of information. Later, multi-point sensing instruments were developed that resolved this issue but were much higher in cost and lacked mobility. In this article, based on the concept of sensor fusion, we describe a portable low-cost system for TCM pulse-type estimation using a smartphone connected to two sensors, including one photoplethysmography (PPG) sensor and one galvanic skin response (GSR) sensor. As a proof of concept, we collected five-minute PPG pulse information and skin impedance on 24 acupoints from 80 subjects. Based on these collected data, we implemented a fully connected neural network (FCN), which was able to provide high prediction accuracy (>90%) for patients with a TCM wiry pulse.