AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking profile, we take advantage of the fact that a linear relationship exists between step frequency and stride length, which is unique to individuals and applies to everyone regardless of age. Autonomous calibration invisible to users allows the system to maintain a high level of accuracy under changing conditions. AutoGait can be integrated into any pedometer or indoor navigation software on handheld devices as long as they are equipped with GPS. The main contribution of this paper is two fold: (1) we propose an auto-calibration method that trains a person's walking profile by effectively processing noisy GPS readings, and (2) we build a prototype system and validate its performance by performing extensive experiments. Our experimental results confirm that the proposed auto-calibration method can accurately estimate a person's walking profile and thus significantly reduce the error rate.

The interests in health care have considerably increased these days as the aging population becomes larger. Health care has grown to the one of most active research areas especially in the area of wireless, mobile health monitoring systems. The wireless network technologies have advanced to the point where they can enable and help deploy a very broad gamut of systems suitable for medical applications. Several researches have proposed to replace the wired connections among medical devices with wireless connections. Wireless network technologies interwork with sensor equipped BodyLANs. Wireless personal area networks (WPANs) are well positioned to support health care applications in limited geographic areas. In particular, the characteristics of Bluetooth and its popularity make it the preferred network infrastructure for HealthNet environments. However, Bluetooth has the "bad reputation" of long connection delays, which may be disastrous in some health applications. In this paper we address the connection delay and propose a new data transfer protocol based on Bluetooth version 2.1. The 2.1 version was published very recently and offers new features that are interest to our application. Among the new features we leverage extended inquiry response (EIR) and secure simple pairing (SSP) to solve the delay problem. Extensive simulation results show that the proposed system significantly improves data delivery as well as power consumption. It solves a well known problem in Bluetooth based wireless networks. Using our proposed scheme, Bluetooth devices are now adequate to support sophisticated scenarios such as emergencies and urgent data dissemination requirements.

Remote medical monitoring with medical sensors and wireless communications has recently gained attention for the potential savings and improved quality in patient health-care. Currently, many mobile wireless medical infrastructures connect to the Internet in a point-to-point fashion, such as the cellular network. However, our research investigates new models for remote patient care that exploit P2P networking among patients and healthcare providers (nurses, doctors, emergency personnel). In this paper, we identify several medical applications based on P2P Health Networking focusing on two specific scenarios where nurses and patients are both equipped with Bluetooth devices. Nurses opportunistically collect, share and upload data in a P2P fashion. During an emergency, the nearest nurse is alerted via Bluetooth Enhanced Inquiry Response mechanism. The simulation and test-bed experimentation demonstrate that Bluetooth P2P networking is both feasible and cost-effective in remote medical monitoring.