Utilization of automated location tracking for clinical workflow analytics and visualization

abstract: The analysis of clinical workflow offers many challenges to clinical stakeholders and researchers, especially in environments characterized by dynamic and concurrent processes. Workflow analysis in such environments is essential for monitoring performance and finding bottlenecks and sources of error. Clinical workflow analysis has been enhanced with the inclusion of modern technologies. One such intervention is automated location tracking which is a system that detects the movement of clinicians and equipment. Utilizing the data produced from automated location tracking technologies can lead to the development of novel workflow analytics that can be used to complement more traditional approaches such as ethnography and grounded-theory based qualitative methods. The goals of this research are to: (i) develop a series of analytic techniques to derive deeper workflow-related insight in an emergency department setting, (ii) overlay data from disparate sources (quantitative and qualitative) to develop strategies that facilitate workflow redesign, and (iii) incorporate visual analytics methods to improve the targeted visual feedback received by providers based on the findings. The overarching purpose is to create a framework to demonstrate the utility of automated location tracking data used in conjunction with clinical data like EHR logs and its vital role in the future of clinical workflow analysis/analytics. This document is categorized based on two primary aims of the research. The first aim deals with the use of automated location tracking data to develop a novel methodological/exploratory framework for clinical workflow. The second aim is to overlay the quantitative data generated from the previous aim on data from qualitative observation and shadowing studies (mixed methods) to develop a deeper view of clinical workflow that can be used to facilitate workflow redesign. The final sections of the document speculate on the direction of this work where the potential of this research in the creation of fully integrated clinical environments i.e. environments with state-of-the-art location tracking and other data collection mechanisms, is discussed. The main purpose of this research is to demonstrate ways by which clinical processes can be continuously monitored allowing for proactive adaptations in the face of technological and process changes to minimize any negative impact on the quality of patient care and provider satisfaction.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Ibeacon based proximity and indoor localization system

User location can be leveraged to provide a wide range of services in a variety of indoor locations including retails stores, hospitals, airports, museums and libraries etc. The widescale proliferation of user devices such as smart phones and the interconnectivity among different entities, powered by Internet of Things (IoT), makes user device-based localization a viable approach to provide Location Based Services (LBS). Location based services can be broadly classified into 1) Proximity based services that provides services based on a rough estimate of users distance to any entity, and 2) Indoor localization that locates a user\u27s exact location in the indoor environment rather than a rough estimate of the distance. The primary requirements of these services are higher energy efficiency, localization accuracy, wide reception range, low cost and availability. Technologies such as WiFi, Radio Frequency Identification (RFID) and Ultra Wideband (UWB) have been used to provide both indoor localization and proximity based services. Since these technologies are not primarily intended for LBS, they do not fulfill the aforementioned requirements. Bluetooth Low Energy (BLE) enabled beacons that use Apple\u27s proprietary iBeacon protocol are mainly intended to provide proximity based services. iBeacons satisfy the energy efficiency, wide reception range and availability requirements of LBS. However, iBeacons are prone to noise due to their reliance on Received Signal Strength Indicator (RSSI), which drastically fluctuates in indoor environments due to interference from different obstructions. This limits its proximity detection accuracy. In this thesis, we present an iBeacon based proximity and indoor localization system. We present our two server-based algorithms to improve the proximity detection accuracy by reducing the variation in the RSSI and using the RSSI-estimated distance, rather than the RSSI itself, for proximity classification. Our algorithms Server-side Running Average and Server-side Kalman Filter improves the proximity detection accuracy by 29% and 32% respectively in contrast to Apple\u27s current approach of using moving average of RSSI values for proximity classification. We utilize a server-based approach because of the greater computing power of servers. Furthermore, server-based approach helps reduce the energy consumption of user device. We describe our cloud based architecture for iBeacon based proximity detection. We also use iBeacons for indoor localization. iBeacons are not primarily intended for indoor localization as their reliance on RSSI makes them unsuitable for accurate indoor localization. To improve the localization accuracy, we use Bayesian filtering algorithms such as Particle Filter (PF), Kalman Filter (KF), and Extended Kalman Filter (EKF). We show that by cascading Kalman Filter and Extended Kalman Filter with Particle Filter, the indoor localization accuracy can be improved by 28% and 33.94% respectively when compared with only using PF. The PF, KFPF and PFEKF algorithm on the server side have average localization error of 1.441 meters, 1.0351 meters and 0.9519 meters respectively

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

A multi-modal perception based assistive robotic system for the elderly

Edited by Giovanni Maria Farinella, Takeo Kanade, Marco Leo, Gerard G. Medioni, Mohan TrivediInternational audienceIn this paper, we present a multi-modal perception based framework to realize a non-intrusive domestic assistive robotic system. It is non-intrusive in that it only starts interaction with a user when it detects the user's intention to do so. All the robot's actions are based on multi-modal perceptions which include user detection based on RGB-D data, user's intention-for-interaction detection with RGB-D and audio data, and communication via user distance mediated speech recognition. The utilization of multi-modal cues in different parts of the robotic activity paves the way to successful robotic runs (94% success rate). Each presented perceptual component is systematically evaluated using appropriate dataset and evaluation metrics. Finally the complete system is fully integrated on the PR2 robotic platform and validated through system sanity check runs and user studies with the help of 17 volunteer elderly participants