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

Integrated ZigBee RFID sensor networks for resource tracking and monitoring in logistics management

The Radio Frequency Identification (RFID), which includes passive and active systems and is the hottest Auto-ID technology nowadays, and the wireless sensor network (WSN), which is one of the focusing topics on monitoring and control, are two fast-growing technologies that have shown great potential in future logistics management applications. However, an information system for logistics applications is always expected to answer four questions: Who, What, When and Where (4Ws), and neither of the two technologies is able to provide complete information for all of them. WSN aims to provide environment monitoring and control regarded as When and What , while RFID focuses on automatic identification of various objects and provides Who (ID). Most people usually think RFID can provide Where at all the time. But what normal passive RFID does is to tell us where an object was the last time it went through a reader, and normal active RFID only tells whether an object is presenting on site. This could sometimes be insufficient for certain applications that require more accurate location awareness, for which a system with real-time localization (RTLS), which is an extended concept of RFID, will be necessary to answer Where constantly. As WSN and various RFID technologies provide information for different but complementary parts of the 4Ws, a hybrid system that gives a complete answer by combining all of them could be promising in future logistics management applications. Unfortunately, in the last decade those technologies have been emerging and developing independently, with little research been done in how they could be integrated. This thesis aims to develop a framework for the network level architecture design of such hybrid system for on-site resource management applications in logistics centres. The various architectures proposed in this thesis are designed to address different levels of requirements in the hierarchy of needs, from single integration to hybrid system with real-time localization. The contribution of this thesis consists of six parts. Firstly, two new concepts, Reader as a sensor and Tag as a sensor , which lead to RAS and TAS architectures respectively, for single integrations of RFID and WSN in various scenarios with existing systems; Secondly, a integrated ZigBee RFID Sensor Network Architecture for hybrid integration; Thirdly, a connectionless inventory tracking architecture (CITA) and its battery consumption model adding location awareness for inventory tracking in Hybrid ZigBee RFID Sensor Networks; Fourthly, a connectionless stochastic reference beacon architecture (COSBA) adding location awareness for high mobility target tracking in Hybrid ZigBee RFID Sensor Networks; Fifthly, improving connectionless stochastic beacon transmission performance with two proposed beacon transmission models, the Fully Stochastic Reference Beacon (FSRB) model and the Time Slot Based Stochastic Reference Beacon (TSSRB) model; Sixthly, case study of the proposed frameworks in Humanitarian Logistics Centres (HLCs). The research in this thesis is based on ZigBee/IEEE802.15.4, which is currently the most widely used WSN technology. The proposed architectures are demonstrated through hardware implementation and lab tests, as well as mathematic derivation and Matlab simulations for their corresponding performance models. All the tests and simulations of my designs have verified feasibility and features of our designs compared with the traditional systems

Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs

A caregiver support platform within the scope of an ambient assisted living ecosystem

The Ambient Assisted Living (AAL) area is in constant evolution, providing new technologies to users and enhancing the level of security and comfort that is ensured by house platforms. The Ambient Assisted Living for All (AAL4ALL) project aims to develop a new AAL concept, supported on a unified ecosystem and certification process that enables a heterogeneous environment. The concepts of Intelligent Environments, Ambient Intelligence, and the foundations of the Ambient Assisted Living are all presented in the framework of this project. In this work, we consider a specific platform developed in the scope of AAL4ALL, called UserAccess. The architecture of the platform and its role within the overall AAL4ALL concept, the implementation of the platform, and the available interfaces are presented. In addition, its feasibility is validated through a series of tests.Project “AAL4ALL”, co-financed by the European Community Fund FEDER, through COMPETE—Programa Operacional Factores de Competitividade (POFC). Foundation for Science and Technology (FCT), Lisbon, Portugal, through Project PEst-C/CTM/LA0025/2013. Project CAMCoF—Context-Aware Multimodal Communication Framework funded by ERDF—European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT—Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980. This work is part-funded by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst-OE/EEI/UI0752/201

Human activity recognition using multisensor data fusion based on Reservoir Computing

Activity recognition plays a key role in providing activity assistance and care for users in smart homes. In this work, we present an activity recognition system that classifies in the near real-time a set of common daily activities exploiting both the data sampled by sensors embedded in a smartphone carried out by the user and the reciprocal Received Signal Strength (RSS) values coming from worn wireless sensor devices and from sensors deployed in the environment. In order to achieve an effective and responsive classification, a decision tree based on multisensor data-stream is applied fusing data coming from embedded sensors on the smartphone and environmental sensors before processing the RSS stream. To this end, we model the RSS stream, obtained from a Wireless Sensor Network (WSN), using Recurrent Neural Networks (RNNs) implemented as efficient Echo State Networks (ESNs), within the Reservoir Computing (RC) paradigm. We targeted the system for the EvAAL scenario, an international competition that aims at establishing benchmarks and evaluation metrics for comparing Ambient Assisted Living (AAL) solutions. In this paper, the performance of the proposed activity recognition system is assessed on a purposely collected real-world dataset, taking also into account a competitive neural network approach for performance comparison. Our results show that, with an appropriate configuration of the information fusion chain, the proposed system reaches a very good accuracy with a low deployment cost

An IoT-aware AAL System to Capture Behavioral Changes of Elderly People

The ageing of population is a phenomenon that is affecting the majority of developed countries around the world and will soon affect developing economies too. In recent years, both industry and academia are focused on the development of several solutions aimed to guarantee a healthy and safe lifestyle to the elderly. In this context, the behavioral analysis of elderly people can help to prevent the occurrence of Mild Cognitive Impairment (MCI) and frailty problems. The innovative technologies enabling the Internet of Things (IoT) can be used in order to capture personal data for automatically recognizing changes in elderly people behavior in an unobtrusive, low-cost and low-power modality. This work aims to describe the ongoing activities within the City4Age project, funded by the Horizon 2020 Programme of the European Commission, mainly focused on the use of IoT technologies to develop an innovative AAL system able to capture personal data of elderly people in their home and city environments. The proposed architecture has been validated through a proof-of-concept focused mainly on localization issues, collection of ambient parameters, and user-environment interaction aspects

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease

The Design and Implementation of OMA RESTful Location Services in Wireless Sensor Environments

Open Mobile Alliance (OMA) RESTful location services are standard RESTful Web services for terminal location. They are location technology-independent and enable applications’ portability and interoperability. Wireless sensors are electronic devices that can sense context: space, environment and physiology. Location is a key element of space context information. Wireless sensors can sense location with a level of accuracy that most other technologies cannot provide, which has made them the technology of choice for several applications. This thesis is about the design and implementation of OMA RESTful location services in wireless sensor environments for improved accuracy. A novel architecture is proposed. The architectural components and operational procedures are defined and implemented. The proof of concept prototype has been realized, along with the measurements for a preliminary performance evaluation. Several lessons were learned. For instance, it is possible to map location information for each of the OMA services to the sensor-based location information. However, using geographic coordinates (i.e. geographic latitude, longitude and altitude) to describe terminal location does not match with the fine-grained location accuracy provided by WSNs

Ambient assisted living deployment aims to empower people living with dementia (AnAbEL)

Ambient Assisted Living aims to support the wellbeing of people with special needs by offering assistive solutions. Those systems focused on dementia claim to increase the autonomy of people living with dementia by monitoring their activities. Thus, topics such as Activity Recognition related to dementia and specific solutions such as reminders and tracking users by Global Positioning System offer great advances that seek users' safety and to preserve their healthier lifestyle. However, these solutions address secondary parties by providing useful activities logs or alerts but excluding the main interested user: the person living with dementia. Although primary users are taken into consideration at some design stages by using user-centred design frameworks, final products tend not to fully address the user's needs. This paper presents an Ambient Intelligent system aimed to reduce this limitation by developing a final solution more strongly focused on enhancing a healthy lifestyle by empowering the user's autonomy. Through continued activities monitoring in real-time, the system can provide reminders to the users by coaching them to keep healthy routines. Continuous monitoring also provides a complete user's behaviour tracking and the context-awareness logic used involves the caregivers through alerts when necessary to ensure the user's safety. This article describes the process followed to develop the system aimed to cover the previous concerns and the practical feedback from health professionals over the system deployment working in a real environment

Activity-Aware Sensor Networks for Smart Environments

The efficient designs of Wireless Sensor Network protocols and intelligent Machine Learning algorithms, together have led to the advancements of various systems and applications for Smart Environments. By definition, Smart Environments are the typical physical worlds used in human daily life, those are seamlessly embedded with smart tiny devices equipped with sensors, actuators and computational elements. Since human user is a key component in Smart Environments, human motion activity patterns have key importance in building sensor network systems and applications for Smart Environments. Motivated by this, in this thesis my work is focused on human motion activity-aware sensor networks for Smart Environments. The main contributions of this thesis are in two important aspects: (i) Designing event activity context-aware sensor networks for efficient performance optimization as well as resource usage; and (ii) Using binary motion sensing sensor networks\u27 collective data for device-free real-time tracking of multiple users. Firstly, I describe the design of our proposed event activity context-aware sensor network protocols and system design for Smart Environments. The main motivation behind this work is as follows. A sensor network, unlike a traditional communication network, provides high degree of visibility into the environmental physical processes. Therefore its operation is driven by the activities in the environment. In long-term operations, these activities usually show certain patterns which can be learned and effectively utilized to optimize network design. In this thesis I have designed several novel protocols: (i) ActSee for activity-aware radio duty-cycling, (ii) EAR for activity-aware and energy balanced routing, and (iii) ActiSen complete working system with protocol suites for activity-aware sensing/ duty-cycling/ routing. Secondly, I have proposed and designed FindingHuMo (Finding Human Motion), a Machine Learning based real-time user tracking algorithm for Smart Environments using Sensor Networks. This work has been motivated by increasing adoption of sensor network enabled Ubiquitous Computing in key Smart Environment applications, like Smart Healthcare. Our proposed FindingHuMo protocol and system can perform device-free tracking of multiple (unknown and variable number of) users in the hallway environments, just from non-invasive and anonymous binary motion sensor data