https://www.mdu.se/

Accurate bladder monitoring is critical in the management of conditions such as urinary incontinence, voiding dysfunction, and spinal cord injuries. Electrical bioimpedance (EBI) has emerged as a cost-effective and non-invasive approach to monitoring bladder activity in daily life, with particular relevance to patient groups who require measurement of bladder urine volume (BUV) to prevent urinary leakage. However, the impact of activities in daily living (ADLs) on EBI measurements remains incompletely characterized. In this study, we investigated the impact of normal ADLs such as sitting, standing, and walking on EBI measurements using the MAX30009evkit system with four electrodes placed on the lower abdominal area. We developed an algorithm to identify artifacts caused by the different activities from the EBI signals. Our findings demonstrate that various physical activities clearly affected the EBI measurements, indicating the necessity of considering them during bladder monitoring with EBI technology performed during physical activity (or normal ADLs). We also observed that several specific activities could be distinguished based on their impedance values and waveform shapes. Thus, our results provide a better understanding of the impact of physical activity on EBI measurements and highlight the importance of considering such physical activities during EBI measurements in order to enhance the reliability and effectiveness of EBI technology for bladder monitoring.

Bladder monitoring, including urinary incontinence management and bladder urinary volume monitoring, is a vital part of urological care. Urinary incontinence is a common medical condition affecting the quality of life of more than 420 million people worldwide, and bladder urinary volume is an important indicator to evaluate the function and health of the bladder. Previous studies on non-invasive techniques for urinary incontinence management technology, bladder activity and bladder urine volume monitoring have been conducted. This scoping review outlines the prevalence of bladder monitoring with a focus on recent developments in smart incontinence care wearable devices and the latest technologies for non-invasive bladder urine volume monitoring using ultrasound, optical and electrical bioimpedance techniques. The results found are promising and their application will improve the well-being of the population suffering from neurogenic dysfunction of the bladder and the management of urinary incontinence. The latest research advances in bladder urinary volume monitoring and urinary incontinence management have significantly improved existing market products and solutions and will enable the development of more effective future solutions.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and hypertension is a major risk factor for acquiring CVDs. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. In this study, a linear SVM machine learning model was used to classify subjects as normal or at different stages of hypertension. The features combined statistical parameters derived from the acceleration plethysmography waveforms and clinical parameters extracted from a publicly available dataset. The model achieved an overall accuracy of 87.50% on the validation dataset and 95.35% on the test dataset. The model's true positive rate and positive predictivity was high in all classes, indicating a high accuracy, and precision. This study represents the first attempt to classify cardiovascular conditions using a combination of acceleration photoplethysmogram (APG) features and clinical parameters The study demonstrates the potential of APG analysis as a valuable tool for early detection of hypertension.

The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

Photoplethysmography is a non-invasive technique used for measuring several vital signs and for the identification of individuals with an increased disease risk. Its principle of work is based on detecting changes in blood volume in the microvasculature of the skin through the absorption of light. The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task, where various feature extraction methods have been proposed in the literature. In this work, we present PPGFeat, a novel MATLAB toolbox supporting the analysis of raw photoplethysmography waveform data. PPGFeat allows for the application of various preprocessing techniques, such as filtering, smoothing, and removal of baseline drift; the calculation of photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting photoplethysmography fiducial points. PPGFeat includes a graphical user interface allowing users to perform various operations on photoplethysmography signals and to identify, and if required also adjust, the fiducial points. Evaluating the PPGFeat’s performance in identifying the fiducial points present in the publicly available PPG-BP dataset, resulted in an overall accuracy of 99% and 3038/3066 fiducial points were correctly identified. PPGFeat significantly reduces the risk of errors in identifying inaccurate fiducial points. Thereby, it is providing a valuable new resource for researchers for the analysis of photoplethysmography signals.

Photoplethysmography (PPG) signals offer a non-invasive and cost-effective mean sfor monitoring cardiovascular health. However, extracting clinically relevant information from these signals in real-time poses significant challenges. This paper presents a novel biosignal processingunit that utilizes the PPGFeat MATLAB toolbox to perform real-time signal processing and feature extraction from PPG signals, enabling continuous cardiovascular disease (CVD) monitoring andanalysis. We propose a system that interfaces with PPG sensors to acquire raw signals in real-time.The PPGFeat toolbox provides an interactive user interface, it identifies high-quality signals basedon their signal quality indices (SQIs) and performs segmentation The segmented PPG signals are then preprocessed by PPGFeat to remove noise and artifacts, smooth the waveforms, and correc tbaseline drift using a Chebyshev type II 4th order, 20 dB filter with a frequency range of 0.4–8 Hz.After preprocessing, a novel algorithm within PPGFeat is employed to accurately extract key fiducial points from the filtered PPG signals and their first and second derivatives. These includes ystolic peaks, diastolic peaks, onsets, and dicrotic notches, as well as inflection points, maxima, and minima on the derivative waveforms. Utilizing these extracted points, PPGFeat computes a comprehensive set of features, including pulse transit time, augmentation index, stiffness index,various magnitudes, and time intervals. These features characterize the PPG signal's morphology,timing intervals, and other relevant characteristics. These features are continuously streamed as output, providing a real-time stream of biomarkers and indicators for CVD analysis and monitoring.The resulting biomarkers and features can be fed into machine learning models or rule-based systems for real-time CVD identification, risk stratification, and monitoring applications. By utilizing PPGFeat's robust algorithms and proven accuracy, the proposed biosignal processing unit enables efficient real-time extraction of clinically relevant information from PPG signals, paving the way for improved cardiovascular health monitoring and personalized healthcare solutions.

Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.

Skin cancer is a rapidly growing and potentially deadly form of cancer, making early detection crucial for improved patient outcomes. This study introduces a machine learning-based educational system designed to aid early detection of skin cancer. The system leverages machine learning techniques to analyse skin lesion images from the ISIC-ISBI 2016 and 2017 datasets providing a non-invasive and cost-effective alternative to traditional biopsies. The primary objective of this dataset collection was to generate an automated prediction of lesion segmentation boundaries using dermoscopy images, where each image has a manual tracing of lesion boundaries done by an expert. To accommodate the diverse images acquired using many different devices, pre-processing and segmentation using OTSU thresholding isolate the region of interest, followed by extraction of detailed features such as texture, shape, and color. Principal component analysis (PCA) refines these features. An SVM ensemble classifier, trained on labeled images and evaluated on the ISIC-ISBI datasets, distinguishes cancerous from non-cancerous lesions. The system achieves an impressive95.73% accuracy, a 95.51% average similarity rate in segmentation, and a low mean squared error(MSE), demonstrating its effectiveness. This system operates in real-time as a user-friendly application executable on any desktop computer, tablet, or laptop. The application takes an image asinput, pre-processes it, and extracts relevant features. Using this feature matrix, the classifier determines whether the input image indicates a malignant or benign melanoma. The output provide sa clear label of 'cancerous melanoma' or 'benign melanoma' for each analyzed image. This system offers significant educational value for dermatology students and doctors. It can be used for hands-on learning and classroom training, enabling accurate diagnosis without the need for invasive biopsies. The system's potential portability makes it a valuable tool for resource-limited settings and large-scale educational initiatives focused on skin cancer detection.

Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human–robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human–robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human–robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

This paper examines whether social cues, such as facial expressions, can be used to adapt and tailor a robot-assisted training in order to maximize performance and comfort. Specifically, this paper serves as a basis in determining whether key facial signals, including emotions and facial actions, are common among participants during a physical and cognitive training scenario. In the experiment, participants performed basic arm exercises with a social robot as a guide. We extracted facial features from video recordings of participants and applied a recursive feature elimination algorithm to select a subset of discriminating facial features. These features are correlated with the performance of the user and the level of difficulty of the exercises. The long-term aim of this work, building upon the work presented here, is to develop an algorithm that can eventually be used in robot-assisted training to allow a robot to tailor a training program based on the physical capabilities as well as the social cues of the users.

Safety in human-robot interaction can be divided into physical safety and perceived safety, where the latter is still under-addressed in the literature. Investigating perceived safety in human-robot interaction requires a multidisciplinary perspective. Indeed, perceived safety is often considered as being associated with several common factors studied in other disciplines, i.e., comfort, predictability, sense of control, and trust. In this paper, we investigated the relationship between these factors and perceived safety in human-robot interaction using subjective and objective measures. We conducted a two-by-five mixed-subjects design experiment. There were two between-subjects conditions: the faulty robot was experienced at the beginning or the end of the interaction. The five within-subjects conditions correspond to (1) baseline, and the manipulations of robot behaviors to stimulate: (2) discomfort, (3) decreased perceived safety, (4) decreased sense of control and (5) distrust. The idea of triggering a deprivation of these factors was motivated by the definition of safety in the literature where safety is often defined by the absence of it. Twenty-seven young adult participants took part in the experiments. Participants were asked to answer questionnaires that measure the manipulated factors after within-subjects conditions. Besides questionnaire data, we collected objective measures such as videos and physiological data. The questionnaire results show a correlation between comfort, sense of control, trust, and perceived safety. Since these factors are the main factors that influence perceived safety, they should be considered in human-robot interaction design decisions. We also discuss the effect of individual human characteristics (such as personality and gender) that they could be predictors of perceived safety. We used the physiological signal data and facial affect from videos for estimating perceived safety where participants’ subjective ratings were utilized as labels. The data from objective measures revealed that the prediction rate was higher from physiological signal data. This paper can play an important role in the goal of better understanding perceived safety in human-robot interaction.

For many applications where interaction between robots and older people takes place, safety and security are key dimensions to consider. ‘Safety’ refers to a perceived threat of physical harm, whereas ‘security’ is a broad term which refers to many aspects related to health, well-being, and aging. This chapter presents a quantitative evaluation tool of the sense of safety and security for robots in elder care. By investigating the literature on measurement of safety and security in human–robot interaction, we propose new evaluation tools specially tailored to assess interaction between robots and older people.

Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

This paper describes a methodology for performing longitudinal evaluations when a social robotic telepresence system is deployed in realistic environments. This work is the core of an Ambient Assisted Living Project called ExCITE, Enabling Social Interaction Through Telepresence. The ExCITE project is geared towards an elderly audience and has as aim to increase social interaction among elderly, their family and healthcare services by using robotic telepresence. The robotic system used in the project is called the Giraff robot and over a three year period, prototypes of this platform are deployed at a number of test-sites in different European countries where user feedback is collected and fedback into the refinement of the prototype. In this paper, we discuss the methodology of ExCITE in particular relation to other methodologies for longitudinal evaluation. The paper also provides a discussion of the possible pitfalls and risks in performing longitudinal studies of this nature particularly as they relate to social robotic telepresence technologies.

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Introduction: Exercise physiology investigates the complex and multifaceted human body responses to physical activity (PA). The integration of electrical bioimpedance (EBI) has emerged as a valuable tool for deepening our understanding of muscle activity during exercise.

Method: In this study, we investigate the potential of using the EBI technique for human motion recognition. We analyze EBI signals from the quadriceps muscle and extensor digitorum longus muscle acquired when healthy participants in the range 20–30 years of age performed four lower body PAs, namely squats, lunges, balance walk, and short jumps.

Results: The characteristics of EBI signals are promising for analyzing PAs. Each evaluated PA exhibited unique EBI signal characteristics.

Discussion: The variability in how PAs are executed leads to variations in the EBI signal characteristics, which, in turn, can provide insights into individual differences in how a person executes a specific PA.

This panel will bring together experts on robotic telepresence from HCI and related fields. Panelists will engage the audience in a discussion of visions, opportunities and challenges for the future of telepresence robots.

This paper presents an image-based cooperative driving system for telepresence robot, which allows safe operation in indoor environments and is meant to minimize the burden on novice users operating the robot. The paper focuses on one emerging telepresence robot, namely, mobile remote presence systems for social interaction. Such systems brings new opportunities for applications in healthcare and elderly care by allowing caregivers to communicate with patients and elderly from remote locations. However, using such systems can be a difficult task particularly for caregivers without proper training. The paper presents a first implementation of a vision-based cooperative driving enhancement to a telepresence robot. A preliminary evaluation in the laboratory environment is presented.

One goal of mobile robotic telepresence for social interaction is to design robotic units that are easy to operate for novice users and promote good interaction between people. This paper presents an exploratory study on the effect of camera orientation and field of view on the interaction between a remote and local user. Our findings suggest that limiting the width of the field of view can lead to better interaction quality as it encourages remote users to orient the robot towards local users.

Mobile robotic telepresence systems used for social interaction scenarios require that users steer robots in a remote environment. As a consequence, a heavy workload can be put on users if they are unfamiliar with using robotic telepresence units. One way to lessen this workload is to automate certain operations performed during a telepresence session in order to assist remote drivers in navigating the robot in new environments. Such operations include autonomous robot localization and navigation to certain points in the home and automatic docking of the robot to the charging station. In this paper we describe the implementation of such autonomous features along with user evaluation study. The evaluation scenario is focused on the first experience on using the system by novice users. Importantly, that the scenario taken in this study assumed that participants have as little as possible prior information about the system. Four different use-cases were identified from the user behaviour analysis.

Mobile robotic telepresence (MRP) has been introduced to allow communication from remote locations. Modern MRP systems offer rich capabilities for human-human interactions. However, simply driving a telepresence robot can become a burden especially for novice users, leaving no room for interaction at all. In this video we introduce a project which aims to incorporate advanced robotic algorithms into manned telepresence robots in a natural way to allow human-robot cooperation for safe driving. It also shows a very first implementation of cooperative driving based on extracting a safe drivable area in real time using the image stream received from the robot.

Auditory immersion plays a significant role in generating a good feeling of presence for users driving a telepresence robot. In this paper, one of the key characteristics of auditory immersion - sound source localization (SSL) - is studied from the perspective of those who operate telepresence robots from remote locations. A prototype which is capable of delivering soundscape to the user through Interaural Time Difference (ITD) and Interaural Level Difference (ILD) using the ORTF stereo recording technique was developed. The prototype was evaluated in an experiment and the results suggest that the developed method is sufficient for sound source localization tasks.

Today, simple analogue assistive technologies are transformed into complex and sophisticated sensor networks. This raises many new privacy issues that need to be considered. In this paper, we investigate how this new generation of assistive technology incorporates Privacy by Design (PbD) principles. The research is conducted as a case study where we use PbD principles as an analytical lens to investigate the design of the new generation of digitalized assistive technology as well as the users’ privacy preferences that arise in use of this technology in real homes. Based on the findings from the case study, we present guidelines for building in privacy in new generations of assistive technologies and in this way protect the privacy of the people using these technologies.

A mobile robotic telepresence (MRP) system is characterized by a video conferencing system which is mounted on a mobile robotic base. The system allows remote pilot users to move the robot around while communicating with local users situated in the robot’s environment. One of the most promising application areas for MRP systems is to deploy them in homes of elderly who are in frequent contact with health care professionals and/or alarm operators. Using MRP systems, elderly can get in immediate contact with these services even without leaving their homes. However, this poses some challenges for the health care professionals and alarm operators. The alarm operators traditionally communicate with their clients using the telephone while the health care professionals see their patients face-to-face with little interference of computer applications. The encounters between health care professionals and patients typically take place at clinics. Neither the health care professionals nor the alarm operators visit the elderly in person. Accordingly, they have no knowledge about the layout of the homes or where the elderly may reside. Thus, the social communication between them and the elderly via MRP systems is more complex than their traditional interaction with elderly.

This compilation thesis makes a contribution towards understanding how interaction is affected by MRP system embodiment. The work focuses on measuring quality of interaction in MRP systems deployed in domestic settings in elder care. The thesis proposes a set of useful tools for measuring interaction quality. These tools are presence, spatial formations and sociometry. They were selected based on their ability to capture important characteristics for communication via MRP systems, e.g. social communication and mobility, and have been evaluated in experiments with real end-users, that is with alarm operators, health care professionals and elderly. The tools used to conduct the experimental evaluations of MRP systems include video-based evaluations, driving sessions and retrospective interviews. These methods were carefully chosen and take into consideration the organizational background of the participants involved and the challenge of conducting experiments with the aforementioned groups of users.

The use of video mediated communication technologies for interacting is increasing. An extension of these is mobile robotic telepresence (MRP) systems, video conferencing systems mounted on teleoperated mobile robots. The nature of the interaction via an MRP system is more complex than face-to-face interaction and involves not only social communication but also mobility. This research focuses on the use of MRP systems in domestic settings in elder care and contributes to the understanding of how interaction is affected by MRP system embodiment.

Mobile Robotic Telepresence (MRP) systems incorporate video conferencing equipment onto mobile robot devices which can be steered from remote locations. These systems, which are primarily used in the context of promoting social interaction between people, are becoming increasingly popular within certain application domains such as health care environments, independent living for the elderly and office environments. In this review, an overview of the various systems, application areas and challenges found in literature concerning mobile robotic telepresence is provided. The survey also proposes a set terminology for the field as there is currently a lack of standard terms for the different concepts related to MRP systems. Further, this review provides an outlook on the various research directions for developing and enhancing mobile robotic telepresence systems per se, as well as evaluating the interaction in laboratory and field settings. Finally, the survey outlines a number of design implications for the future of mobile robotic telepresence systems for social interaction.

In this paper we present data collected at a training session for health care personnel and alarm operators in steering a mobile social robotic telepresence robot for the first time. The purpose of the system is to be used as a communicative tool particularly when interacting with an elderly audience. The results are based on questionnaires which includes questions about experienced social and spatial presence from the Temple Presence Inventory as well as the Networked Minds Social Presence Inventory. Also investigated in this study is how intuitive the system was to use as well as how attentive the users were to what was going on in the environment. Over thirty healthcare personnel and alarm operators participated in the study and the overall results presented in the paper suggest that the two questionnaires are indeed suitable for use also in the social robotic telepresence domain fo rproviding indications on both social and spatial presence.

This article presents the results from a video-based evaluation study of a social robotic telepresence solution for elderly. The evaluated system is a mobile teleoperated robot called Giraff that allows caregivers to virtually enter a home and conduct a natural visit just as if they were physically there. The evaluation focuses on the perspectives from primary healthcare organizations and collects the feedback from different categories of health professionals. The evaluation included 150 participants and yielded unexpected results with respect to the acceptance of the Giraff system. In particular, greater exposure to technology did not necessarily increase acceptance and large variances occurred between the categories of health professionals. In addition to outlining the results, this study provides a number of indications with respect to increasing acceptance for technology for elderly.

In this paper, we focus on spatial formations when interacting via mobile robotic telepresence (MRP) systems. Previous research has found that those who used a MRP system to make a remote visit (pilot users) tended to use different spatial formations from what is typical in human-human interaction. In this paper, we present the results of a study where a pilot user interacted with ten elderly via a MRP system. Intentional deviations from known accepted spatial formations were made in order to study their effect on interaction quality from the local user perspective. Using a retrospective interviews technique, the elderly commented on the interaction and confirmed the importance of adhering to acceptable spatial configurations. The results show that there is a mismatch between pilot user behaviour and local user preference and that it is important to evaluate a MRP system from two perspectives, the pilot user’s and the local user’s.

The field of mobile robotic telepresence for social communication is in rapid expansion and it is of interest to understand what promotes good interaction. In this paper, we present the results of an experiment where novice users working in health care were given a guided tour while maneuvering a mobile robotic telepresence system for the first time. In a previous study, it was found that subjective presence questionnaires and observations of spatial configurations based on Kendon’s F-formations were useful to evaluate quality of interaction in mobile robotic telepresence. In an effort to find more automated methods to assess the quality of interaction, the study in this paper used the same measures, with the addition of objective sociometric measures. Experimental results show that the quantitative analysis of the sociometric data correlates with a number of parameters gathered via qualitative analysis, e.g. different dimensions of presence and observed problems in maneuvering the robot.

Robotic telepresence offers a means to connect to a remote location via traditional telepresence with the added value of moving and actuating in that location. Recently, there has been a growing focus on the use of robotic telepresence to enhance social interaction among elderly. However for such technology to be accepted it is likely that the experienced presence when using such a system will be important. In this paper, we present results obtained from a training session with a robotic telepresence system when used for the first time by healthcare personnel. The study was quantitative and based on two standard questionnaires used for presence namely, the Temple Presence Inventory (TPI) and the Networked Minds Social Presence Intentory. The study showed that overall the sense of social richness as perceived by the users was high. The users also had a realistic feeling regarding their spatial presence.

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.

Many new technologies claiming to support independent living and prolonged possibilities of aging in place have been developed. To support independent living and increase the sense of safety and security both for the elderly themselves and for their relatives, the technologies have to be easily adaptable to match the divergent user’s personal expectations and needs. The study reported in this paper was conducted as seven case studies where a sensor network was deployed in homes of people with a self-perceived memory decline. We describe problems related to adaptive personalization of such technology in real settings and discuss what consequences these problems may have for the elderly people's and their relatives willingness to use the technology. Our results indicate that a lack of sufficient possibilities to adaptive personalization of the system makes it difficult to address individual user's expectations and needs. This, in turn, leads to a decreased trustworthiness of the technology and a risk of unwillingness to use the technology.

Stiftelsen Länsförsäkringsbolagens Forskningsfond finansierar forskningsprojekt som har för avsikt att stärka människors trygghet i sin nuvarande bostad. Detta dokument rapporterar forskning som skett inom ramen för utlysningen ”Tryggt boende”. Projektet som har titeln ”En metod för mätning av ett sensornätverks inverkan på trygghet” innefattar såväl kvalitativ som kvantitativ datainsamling från fem månaders testning av ett sensornätverk i hemmiljöer.

Vi vill tacka forskningsfondens styrelse för förtroendet men också rikta ett varmt tack till vår referensgrupp i vilken Marie Villman (Örebro kommun), Susanne Lidmo (Länsförsäkringar) samt Ingvor Pettersson (Institutionen för Hälsovetenskap och medicin, Örebro universitet). Vi vill tacka Abilia som bistått vid installationer av sensornätverket, Örebro kommuns Minnesmottagning som hjälpt oss hitta testpersoner samt Örebroregionen Science Park som hjälpt oss att sprida information om detta projekt. Slutligen vill vi också tacka våra sju testpersoner med anhöriga som möjliggjort detta projekt.

Slutrapporten är skriven för att kunna läsas av alla intresserade av projektet. För mer ingående information kring forskningsresultaten hänvisas till de forskningspublikationer som är, eller kommer att bli, resultat av studien.

Örebro 2015-03-01

Annica Kristoffersson, tekn dr, Örebro universitet

Ella Kolkowska, fil dr, Örebro universitet

Amy Loutfi, tekn dr, docent, lektor, projektledare (Örebro universitet (amy.loutfi@oru.se)

Background Current research shows that perceived improvement of quality of life (QoL) is among the most important factors influencing acceptance of technology by older adults. Research Aim/question(s) The purpose of this study was to assess the utility of a sensor-based cognitive assistive technology (CAT) deployed in real homes and its impact on older adults’ and their informal caregivers’ perceived QoL. Methods During a period of five months, we conducted a summative evaluation by deploying a CAT in the home of couples in which one of the partners had a self-perceived memory decline. We applied a mixed method approach. Quantitative and qualitative data was collected via subjective forms, observations and 28 interviews conducted prior to the deployment, when the CAT was deployed and one month after the CAT removal. Results The study showed that the CAT had a psychosocial impact on older adults with a self-perceived memory decline and their informal caregivers but no impact on subjective forms measuring physiological and mental health, anxiety, depression or QoL. Additionally, we found that the CAT lacked a number of functionalities and that the test persons experienced several problems of different nature when using it in their homes. This affected the users’ experienced impact of the CAT. Conclusions This study contributes to the literature on sensor networks’ impact on QoL. While previous studies often use general forms intended to assess QoL, this summative evaluation indicates that using such instruments without considering contextual factors is not meaningful. The study also outlines a number of factors that sensor network providers might consider in order to increase their products impact on QoL and their perceived utility.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA)for different purposes, including assessment of gait and balance, prevention and/or detection of falls,recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoringof neurological disease progression. The article provides in-depth information on the retrievedarticles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthyparticipants vs patients, and study conditions. It is well known that motion patterns change with ageand the onset of illnesses, and that the risk of falling increases with age. Yet, studies including olderpersons are rare. Gender distribution was not even provided in several studies, and others includedonly, or a majority of, men. Another shortcoming is that none of the studies were conducted inreal-life conditions. Hence, there is still important work to be done in order to increase the usefulnessof wearable sensors in these areas. The article highlights flaws in how studies based on previouslycollected datasets report on study samples and the data collected, which makes the validity andgeneralizability of those studies low. Exceptions exist, such as the promising recently reported opendataset FallAllD, wherein a longitudinal study with older adults is ongoing. 

The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.