Nursing Activity Sensing Using Mobile Sensors and Proximity Sensors

In recent years, big data are utilized in many industries.In this study, in order to analyze duties of thenurses, we performed experiments to collect the dutiesactivity data of the nurses for a long term. Weset 38 nurses as subjects and asked them to carry outduties while attaching a wearable small sensor device,and collected the acceleration data, meeting informationbetween nurses and the nurse duties information.In addition, we collected the location information of the nurses by using infrared information and communication equipment at the same time. From various data collected, we analyzed intensity and positional information of duties activity of the nurse, meeting information and the duties information between nurses and considered the influence that each factor affected to the nurse. As the result, we found that intensity of the activity increases in such nurses as who has many times of meeting with other nurses, visits the patient room many times, or who works on jobs concerning with the assistance of the patients such as rehabilitation assistance duties or the activity assistance dutiesThe 47th ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS\u2715), December 5-8, 2015, Waikiki Beach Marriott Resort & Spa, Hawaii, US

An Advanced Home ElderCare Service

With the increase of welfare cost all over the developed world, there is a need to resort to new technologies that could help reduce this enormous cost and provide some quality eldercare services. This paper presents a middleware-level solution that integrates monitoring and emergency detection solutions with networking solutions. The proposed system enables efficient integration between a variety of sensors and actuators deployed at home for emergency detection and provides a framework for creating and managing rescue teams willing to assist elders in case of emergency situations. A prototype of the proposed system was designed and implemented. Results were obtained from both computer simulations and a real-network testbed. These results show that the proposed system can help overcome some of the current problems and help reduce the enormous cost of eldercare service

Adaptive physical human-robot interaction (PHRI) with a robotic nursing assistant.

Recently, more and more robots are being investigated for future applications in health-care. For instance, in nursing assistance, seamless Human-Robot Interaction (HRI) is very important for sharing workspaces and workloads between medical staff, patients, and robots. In this thesis we introduce a novel robot - the Adaptive Robot Nursing Assistant (ARNA) and its underlying components. ARNA has been designed specifically to assist nurses with day-to-day tasks such as walking patients, pick-and-place item retrieval, and routine patient health monitoring. An adaptive HRI in nursing applications creates a positive user experience, increase nurse productivity and task completion rates, as reported by experimentation with human subjects. ARNA has been designed to include interface devices such as tablets, force sensors, pressure-sensitive robot skins, LIDAR and RGBD camera. These interfaces are combined with adaptive controllers and estimators within a proposed framework that contains multiple innovations. A research study was conducted on methods of deploying an ideal HumanMachine Interface (HMI), in this case a tablet-based interface. Initial study points to the fact that a traded control level of autonomy is ideal for tele-operating ARNA by a patient. The proposed method of using the HMI devices makes the performance of a robot similar for both skilled and un-skilled workers. A neuro-adaptive controller (NAC), which contains several neural-networks to estimate and compensate for system non-linearities, was implemented on the ARNA robot. By linearizing the system, a cross-over usability condition is met through which humans find it more intuitive to learn to use the robot in any location of its workspace, A novel Base-Sensor Assisted Physical Interaction (BAPI) controller is introduced in this thesis, which utilizes a force-torque sensor at the base of the ARNA robot manipulator to detect full body collisions, and make interaction safer. Finally, a human-intent estimator (HIE) is proposed to estimate human intent while the robot and user are physically collaborating during certain tasks such as adaptive walking. A NAC with HIE module was validated on a PR2 robot through user studies. Its implementation on the ARNA robot platform can be easily accomplished as the controller is model-free and can learn robot dynamics online. A new framework, Directive Observer and Lead Assistant (DOLA), is proposed for ARNA which enables the user to interact with the robot in two modes: physically, by direct push-guiding, and remotely, through a tablet interface. In both cases, the human is being “observed” by the robot, then guided and/or advised during interaction. If the user has trouble completing the given tasks, the robot adapts their repertoire to lead users toward completing goals. The proposed framework incorporates interface devices as well as adaptive control systems in order to facilitate a higher performance interaction between the user and the robot than was previously possible. The ARNA robot was deployed and tested in a hospital environment at the School of Nursing of the University of Louisville. The user-experience tests were conducted with the help of healthcare professionals where several metrics including completion time, rate and level of user satisfaction were collected to shed light on the performance of various components of the proposed framework. The results indicate an overall positive response towards the use of such assistive robot in the healthcare environment. The analysis of these gathered data is included in this document. To summarize, this research study makes the following contributions: Conducting user experience studies with the ARNA robot in patient sitter and walker scenarios to evaluate both physical and non-physical human-machine interfaces. Evaluation and Validation of Human Intent Estimator (HIE) and Neuro-Adaptive Controller (NAC). Proposing the novel Base-Sensor Assisted Physical Interaction (BAPI) controller. Building simulation models for packaged tactile sensors and validating the models with experimental data. Description of Directive Observer and Lead Assistance (DOLA) framework for ARNA using adaptive interfaces

MakeSense: An IoT Testbed for Social Research of Indoor Activities

There has been increasing interest in deploying IoT devices to study human behaviour in locations such as homes and offices. Such devices can be deployed in a laboratory or `in the wild' in natural environments. The latter allows one to collect behavioural data that is not contaminated by the artificiality of a laboratory experiment. Using IoT devices in ordinary environments also brings the benefits of reduced cost, as compared with lab experiments, and less disturbance to the participants' daily routines which in turn helps with recruiting them into the research. However, in this case, it is essential to have an IoT infrastructure that can be easily and swiftly installed and from which real-time data can be securely and straightforwardly collected. In this paper, we present MakeSense, an IoT testbed that enables real-world experimentation for large scale social research on indoor activities through real-time monitoring and/or situation-aware applications. The testbed features quick setup, flexibility in deployment, the integration of a range of IoT devices, resilience, and scalability. We also present two case studies to demonstrate the use of the testbed, one in homes and one in offices.Comment: 20 pages, 11 figure

Mobile Robots and Autonomic Ambient Assisted Living

The use of Smart Environments in the delivery of pervasive care is a research topic that has witnessed increasing interest in recent years. These environments aim to deliver pervasive care through ubiquitous sensing by monitoring the occupants Activities of Daily Living. In order for these environments to succeed in achieving their goal, it is crucial that sensors deployed in the environment perform faultlessly. In this research we investigate addressing anomalous sensor behavior through the utilization of a mobile robot. The robot’s role is twofold; it must provide substitution in the presence of suspected sensor faults and act as an observer of anomalous sensor behavior in order to understand the changes that occur in the behavior of sensors deployed within the environment over time. The aim of this work is to explore a paradigm shift to the use of Autonomic Ambient Assisted Living.We have discovered that the use of a mobile robot is a viable means of introducing this paradigm to a Smart Environment

Real-Time Indoor Movement Animation System In 3D Environment

In the era of smart homes, activities of daily living (ADLs) gains more and more attentions, ADLs can be used to present an individual’s living status, gauge an individual’s level of functioning and estimate his/her health condition. With smart devices, our daily motions can be captured and mapped into digital data, these raw data can be analyzed so that activities can be recognized. Although activities can be determined, they’re still represented as digital data, it would be boring, hard and time-consuming to read and understand such data. A system that could provide real-time monitoring and visualizing of an individual’s activities without privacy issue will be an urgent requirement for the non-technical, especially for nurses, doctors and family members, in order to observe people in need and in turn provide better nursing services. For this project, I present a real-time animation system for human movements in an 3D indoor environment with simultaneous tracking of an individual. This system collects and recognizes activities only through a smart phone and animates these movement behaviors in a 3D environment. The indoor activities are recognized by processing the data combination of magnetic field, acceleration and Wi-Fi signals. With this system, people can easily see the daily movement information of an individual and where that individual is located at a specific time. The purpose of this system is to provide a cost-effective and convenient healthcare service for people in need

Collaborative human-machine interfaces for mobile manipulators.

The use of mobile manipulators in service industries as both agents in physical Human Robot Interaction (pHRI) and for social interactions has been on the increase in recent times due to necessities like compensating for workforce shortages and enabling safer and more efficient operations amongst other reasons. Collaborative robots, or co-bots, are robots that are developed for use with human interaction through direct contact or close proximity in a shared space with the human users. The work presented in this dissertation focuses on the design, implementation and analysis of components for the next-generation collaborative human machine interfaces (CHMI) needed for mobile manipulator co-bots that can be used in various service industries. The particular components of these CHMI\u27s that are considered in this dissertation include: Robot Control: A Neuroadaptive Controller (NAC)-based admittance control strategy for pHRI applications with a co-bot. Robot state estimation: A novel methodology and placement strategy for using arrays of IMUs that can be embedded in robot skin for pose estimation in complex robot mechanisms. User perception of co-bot CHMI\u27s: Evaluation of human perceptions of usefulness and ease of use of a mobile manipulator co-bot in a nursing assistant application scenario. To facilitate advanced control for the Adaptive Robotic Nursing Assistant (ARNA) mobile manipulator co-bot that was designed and developed in our lab, we describe and evaluate an admittance control strategy that features a Neuroadaptive Controller (NAC). The NAC has been specifically formulated for pHRI applications such as patient walking. The controller continuously tunes weights of a neural network to cancel robot non-linearities, including drive train backlash, kinematic or dynamic coupling, variable patient pushing effort, or slope surfaces with unknown inclines. The advantage of our control strategy consists of Lyapunov stability guarantees during interaction, less need for parameter tuning and better performance across a variety of users and operating conditions. We conduct simulations and experiments with 10 users to confirm that the NAC outperforms a classic Proportional-Derivative (PD) joint controller in terms of resulting interaction jerk, user effort, and trajectory tracking error during patient walking. To tackle complex mechanisms of these next-gen robots wherein the use of encoder or other classic pose measuring device is not feasible, we present a study effects of design parameters on methods that use data from Inertial Measurement Units (IMU) in robot skins to provide robot state estimates. These parameters include number of sensors, their placement on the robot, as well as noise properties on the quality of robot pose estimation and its signal-to-noise Ratio (SNR). The results from that study facilitate the creation of robot skin, and in order to enable their use in complex robots, we propose a novel pose estimation method, the Generalized Common Mode Rejection (GCMR) algorithm, for estimation of joint angles in robot chains containing composite joints. The placement study and GCMR are demonstrated using both Gazebo simulation and experiments with a 3-DoF robotic arm containing 2 non-zero link lengths, 1 revolute joint and a 2-DoF composite joint. In addition to yielding insights on the predicted usage of co-bots, the design of control and sensing mechanisms in their CHMI benefits from evaluating the perception of the eventual users of these robots. With co-bots being only increasingly developed and used, there is a need for studies into these user perceptions using existing models that have been used in predicting usage of comparable technology. To this end, we use the Technology Acceptance Model (TAM) to evaluate the CHMI of the ARNA robot in a scenario via analysis of quantitative and questionnaire data collected during experiments with eventual uses. The results from the works conducted in this dissertation demonstrate insightful contributions to the realization of control and sensing systems that are part of CHMI\u27s for next generation co-bots

Challenges of measuring body temperatures of free-ranging birds and mammals

The thermal physiology of most birds and mammals is characterised by considerable spatial and temporal variation in body temperature. Body temperature is, therefore, a key parameter in physiological, behavioural and ecological research. Temperature measurements on freely moving or free-ranging animals in the wild are challenging but can be undertaken using a range of techniques. Internal temperature may be sampled using thermometry, surgically implanted loggers or transmitters, gastrointestinal or non-surgically placed devices. Less invasive approaches measure peripheral temperature with subcutaneous passive integrated transponder tags or skin surface-mounted radio transmitters and data loggers, or use infrared thermography to record surface temperature. Choice of technique is determined by focal research question and region of interest that reflects appropriate physiological or behavioural causal mechanisms of temperature change, as well as welfare and logistical considerations. Particularly required are further studies that provide opportunities of continuously sampling from multiple sites from within the body. This will increase our understanding of thermoregulation and temperature variation in different parts of the body and how these temperatures may change in response to physiological, behavioural and environmental parameters. Technological advances that continue to reduce the size and remote sensing capability of temperature recorders will greatly benefit field research