Shahina Ferdous

Web based medicine intake tracking application

One of the issues in healthcare systems or medical information systems is the reduction of medical errors to ensure patient safety. Inside an assistive environment, we apply RFID tags to monitor drug taking pattern and its consequences are reported to the care giver. This paper talks about an application which tracks the medicine intake pattern for the elderly using RFID readers and tags, motion sensors, and a wireless sensor mote. With the adoption of this ambient assistive technology in healthcare systems, the concept of heterogeneous sensor data management becomes an issue. In this paper, using a Web Based Caregiver Module makes the process of monitoring medicine intake for health-related matters of the elderly living alone simpler and easier. We also propose to use an energy efficient technique by using multiple sensor devices which employ a sequence of innetwork data fusion as needed.

Multi-modal Person Localization And Emergency Detection Using The Kinect

Person localization is of paramount importance in an ambient intelligence environment since it is the first step towards context-awareness. In this work, we present the development of a novel system for multi-modal person localization and emergency detection in an assistive ambient intelligence environment for the elderly. Our system is based on the depth sensor and microphone array of 2 Kinect devices. We use skeletal tracking conducted on the depth images and sound source localization conducted on the captured audio signal to estimate the location of a person. In conjunction with the location information, automatic speech recognition is used as a natural and intuitive means of communication in order to detect emergencies and accidents, such as falls. Our system attained high accuracy for both the localization and speech recognition tasks, verifying its effectiveness. Keywords-localization; multi-modal; Kinect; speech recognition; context-awareness; 3-D interaction

A survey of assistive devices for cerebral palsy patients

In this paper, we present a survey of recent advances in assistive technologies used to foster rehabilitation and improved quality of life for children with Cerebral palsy. The survey focuses specifically on robotics and interactive games used in rehabilitative therapy, as well as general electronic assistive devices for everyday use. The systems and clinical studies discussed show that Cerebral palsy treatment can benefit greatly from emerging technologies.

Applying data warehousing technique in pervasive assistive environment

Radio Frequency Identification (RFID) is an automatic identification (Auto-ID) Technology, which is most commonly used now days in healthcare for tracking and identifying objects. In the context of assistive environment, statistical query analysis over the history of Data generated from RFID Applications as well as real time monitoring of the patients or the elderly people (people who need assistance) are really important. But Data generated from these types of healthcare applications can be very large, if each individual object becomes RFID-Tagged. As a result, the RFID technology is also imposing a greater challenge to provide efficient query responses over these Data. In this paper, we show how to apply traditional Data Warehousing techniques to model these massive amounts of RFID Data. In short, we describe how to construct an RFID Warehouse so that important query analyses can be performed very efficiently. We also show how to process a continuous stream of RFID Data to answer real time queries using Sliding Window techniques. By the help of using synthetic Datasets, we conclude that querying over Data Warehouse is much faster than traditional Relational DBMS. We also find that the aforesaid performance improvement is expected to be much higher as the size of the Dataset increases.

A survey on multi person identification and localization

In this paper, we present a survey of technology and algorithms used to identify and localize multiple people simultaneously in an assistive environment. The survey summarizes localization approaches based on video cameras and RFID, as well as describes identification methods based on RFID tags or wearable devices. The paper also describes state-of-the-art multimodal data fusion methods that estimate the identification and location of every person more precisely.

Multi-person Identification and Localization for Ambient Assistive Living

In this paper, we present a novel, non-intrusive system that uses RFID technology and the Kinect sensor in order to identify and track multiple people in an assistive apartment. RFID is used for both identification and location estimation while information from the Kinect sensor is used for accurate localization. Data from the various modalities is fused using two techniques. During the experiments conducted, our system exhibited high accuracy, thus proving the effectiveness of the proposed design.

Multi-person identification and localization using RFID and passive sensor technology

Continuous location tracking in an assistive environment is considered to be the fundamental step for most of the monitoring applications, as it provides a very good indication about different ongoing activities. But, tracking in a multi-person environment, such as hospitals, requires simultaneous identification and localization mechanisms, which makes the system extremely complex. In this paper, we describe a system that uses RFID and other passive sensors deployed at various locations of the environment to continuously track and identify a person in a multi-person assistive environment. Given, sensor activations at different time points, the proposed system considers location uncertainties of each person and applies the nearest neighbor algorithm to update the location of a person.

An Adaptable Framework for Integrating and Querying Sensor Data

Sensor data generated by pervasive applications are very diverse and are rarely described in standard or established formats. Consequently, one of the greatest challenges in pervasive systems is to integrate heterogeneous repositories of sensor data into a single view. The traditional approach to data integration, where a global schema is designed to incorporate the local schemas, may not be suitable to sensor data due to their highly transient schemas and formats. Furthermore, researchers and professionals in healthcare need to combine relevant data from various data streams and other data sources, and to be able to perform searches over all of these collectively using a single interface or query. Often, users express their search in terms of a small set of predefined fields from a single schema that is the most familiar to them, but they want their search results to include data from other compatible schemas as well. We have designed and implemented a framework for a sensor data repository that gives access to heterogeneous sensor metadata schemas in a uniform way. In our framework, the user specifies a query in an arbitrary schema and specifies the mappings from this schema to all the collections he wants to access. To ease the task of mapping specification, our system remembers metadata mappings previously used and uses them to propose other relevant mapping choices for the unmapped metadata elements. That way, users may build their own metadata mappings based on earlier mappings, each time specifying (or improving) only those components that are different. We have created a repository using data collected from various pervasive applications in a healthcare environment, such as activity monitoring, fall detection, sleep-pattern identification, and medication reminder systems, which are currently undergoing at the Heracleia Lab. We have also developed a flexible query interface to retrieve relevant records from the repository that allows users to specify their own choices of mappings and to express conditions to effectively access fine-grained data.

Computer aided sensor placement and automatic state definition

The design of an Finite State Machine for human activity recognition heavily depends on the type, number and deployment of the sensors used. In this work we propose a tool that automatically creates the FSM, independently of the type and number of sensors or the environment where these are deployed in. This tool allows the users to define the areas of interest in an assistive living environment.