Matthew D'Souza

Wireless localisation network for patient tracking

We present an inexpensive and robust wireless localisation network that can track the location of patients in an indoor environment and monitor their physical status i.e. walking, running, etc. Static nodes are placed at predetermined positions in a building. The static nodes are used to determine the presence of the user in an area of a building. The user carries a mobile node on them. The localisation network was implemented using the small sized Fleck Nano wireless sensor. This platform also measured a userpsilas inertial movement using a three-axis accelerometer sensor. We also compared our localisation network to a commercially available indoor wireless localisation and tracking system. Further work involves developing a multi-hypothesis testing model tracking users, prediction human motion events and investigating the wireless network requirements of supporting large numbers of active users.

Evaluation of realtime people tracking for indoor environments using ubiquitous motion sensors and limited wireless network infrastructure

Abstract We present the development and evaluation of a realtime indoor localisation system for tracking people. Our aim was to track a person’s indoor position using dead-reckoning, while limiting position error without depending on extensive wireless network infrastructure. The Indoor People Tracker used wearable motion sensors, a floor-plan map and a limited wireless sensor network for proximity ranging. We evaluated how the position accuracy of the Indoor People Tracker was affected by floor-plan map features, wireless proximity range and motion information. The advantage of the Indoor People Tracker was found; it was able to achieve accurate position resolution with minimal error, while not depending on wireless proximity.

Indoor localisation using a context-aware dynamic position tracking model

Indoor wireless localisation is a widely sought feature for use in logistics, health, and social networking applications. Low-powered localisation will become important for the next generation of pervasive media applications that operate on mobile platforms. We present an inexpensive and robust context-aware tracking system that can track the position of users in an indoor environment, using a wireless smart meter network. Our context-aware tracking system combines wireless trilateration with a dynamic position tracking model and a probability density map to estimate indoor positions. The localisation network consisted of power meter nodes placed at known positions in a building. The power meter nodes are tracked by mobile nodes which are carried by users to localise their position. We conducted an extensive trial of the context-aware tracking system and performed a comparison analysis with existing localisation techniques. The context-aware tracking system was able to localise a persons indoor position with an average error of 1.21u2009m.

Wireless interactive system for patient healthcare monitoring using mobile computing devices

Recently there has been a need to incorporate the use of mobile computing devices in hospital or clinical applications, to enhance patient care. The advancement of wireless technology has created unique mechanisms of interaction that can meet the needs of e-health system robustness, reliability and accuracy requirements. This paper presents an interactive wireless medical information system that allows patient instrumentation output data to be accessed and patient details to be entered by medical practitioners using mobile computing devices. We examine mobile and wireless information technology concepts that can be used to interact with a medical information system for controlling real-time data streaming medical instruments.

Wireless outdoor personal area network using adaptive inquiry scanning for location-based services

The Infopoint Explorer is an outdoor personal area network for location-based services that allows users to access locality information interactively using mobile computing devices such as PDAs and smartphones. The Infopoint explorer was used to study the limitations imposed by inexpensive and popular wireless technologies such as Bluetooth for outdoor location-based services. The Infopoint Explorer functioned as a multimedia guidebook that allowed access to location-specific information using wireless Infopoints. The Infopoints detect and attempt to transfer multimedia content to nearby mobile computing devices using Bluetooth connectivity. An adaptive Bluetooth Inquiry Access Code algorithm was developed to reduce the time taken for the Infopoint to detect nearby devices. The Infopoint was powered with solar panels and was deployed for a four-month trial. During the trial, over 8,000 Bluetooth connections were attempted, with 300 successful transfers.

Using context-aware sub sorting of received signal strength fingerprints for indoor localisation

Mobile indoor localisation has numerous uses for logistics, health, sport and social networking applications. Current wireless localisation systems experience reliability difficulties while operating within indoor environments due to interference caused by the presence of metallic infrastructure. Current position localisation use wireless channel propagation characteristics, such as RF receive signal strength to localise a users position, which is subject to interference. To overcome this, we developed a Fingerprint Context Aware Partitioning tracking model for tracking people within a building. The Fingerprint Context Aware Partitioning tracking model used received RF signal strength fingerprinting, combined with localised context aware information about the users immediate indoor environment surroundings. We also present an inexpensive and robust wireless localisation network that can track the location of users in an indoor environment, using the Zigbee/802.15.4 wireless communications protocol. The wireless localisation network used reference nodes placed at known positions in a building. The reference nodes are used by mobile nodes, carried by users to localise their position. We found that the Fingerprint Context Aware Partitioning model had improved performance than using only multilateration, in locations that were not in range of multiple reference nodes. Further work includes investigating how multiple mobile nodes can be used by Fingerprint Context Aware Partition model to improve position accuracy.

Wireless indoor localisation using received signal strength fingerprinting with Context Aware Partitioning

We presented a wireless indoor localisation system that tracked users in an indoor environment, using the FCAP tracking model. The FCAP model used RSSI fingerprinting combined with context-aware information, such as a building floorplan. The use of context aware information allowed the FCAP model to improve position accuracy. We evaluated and compared the FCAP model to conventional multilateration. The FCAP model performed better than multilateration, where the layout of the wireless indoor localisation network did not restrict the position of the mobile node to areas covered by multiple reference nodes. This was advantageous by not restricting the placement of the reference nodes. Further work involves investigating the use of multiple operating mobile nodes with the FCAP model and how 3-dimensional localisation can be achieved with context awareness of the surrounding environment.

Biometric Monitoring of Footstep and Heart Rate Using Wireless Inertial Sensors

Inertial sensors are widely used for a variety of biomedical applications, such as human activity monitoring. We present a wireless biomedical monitoring network used for measuring footstep parameters and the heart rate of a person. The wireless biomedical monitoring network uses inertial sensors to record and monitor heart rate and consists of multiple monitoring nodes placed on a person, that communicate with a base node. The monitoring nodes placed on a person’s ankle measure the acceleration generated during a footstep. By analysing this data, we are able to determine the average footstep length and walking velocity to be 80cm and the average walking speed to be 1m/s which corresponds to results found by existing studies. A monitoring node placed on the right Carotid artery in the neck region was able to measure the vibrations generated by the movement of blood. Analysis of the acceleration signals generated by the pressure pulse showed that it was possible to determine the heart rate of the person. We found that a sitting person had a heart rate of 75 BPM, which was confirmed by an electrocardiogram device. Further areas of investigation involve improving the sensitivity of the monitoring node’s accelerometer sensor by using a diaphragm and also to measure a person’s blood pressure using inertial sensing.Copyright