Real Homes Research Articles

I am frail and my robot is stuck on the corridor: cohabitation of people with frailty and robots

Abstract UK and European policies promote research and innovation for people living with frailty (PLWF), as realistic strategies for independent living. Yet homes are not designed for PLWF or robots, even though robots could support PLWF with activities, i.e., exercise and companionship. Adoption of robots in homes is limited to devices such as automated vacuum cleaners and voice assistants. Robotics aimed at assisting PLWF have been thus far been tested in laboratories and the study of real homes for the co-habitation of robots and older people is often neglected. We draw on expertise from human-robot interaction, built environment, public health and clinical practice to facilitate optimisation of human and robot cohabitation for PLWF. For this, we have reviewed different robots-for-frailty, in terms of, ability to fit and move within homes. We utilised a range of methods and sources of information including meetings with allied health professionals, visited the Robot House and explored the available robots from a fit for purpose perspective, audited five flats for PLWF in Gloucester, England in accordance with the needs of those with frailty, simulated robot’s function within the home environment, using persona’s for different use case scenarios, trial testing of selected robots in the home environment for comparison with simulation, using knowledge gained to extrapolate this data to estimate suitability for frail individuals in their homes. Most robots currently available would not adequately fit in an independent living accommodation or the smaller ones could be a tripping hazard. The communal areas of independent living would be more realistic for such human robot interactions, rather than the actual households. The research informs the process facilitating the adoption of robots to benefit PLWF both feasible and desired. The research produced a framework that can lead to future research exploring robot and PLWF co-habitation in real home settings. Key messages • The cohabitation of humans and robots currently faces barriers relevant to independent living built-environment spatial restrictions. • Technologies that support independent living and frailty must develop in spatially-unobtrusive ways.

SCALING: plug-n-play device-free indoor tracking

24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly researchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily set up by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document}6 sq m and 4.5×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document}8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking, respectively. Notably, only 1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort. In addition, we conduct Monte Carlo experiments to numerically analyze the impact of sensor placements and develop practical guidelines for deployment in real life scenarios.

A Novel Smart Shoe Instrumented with Sensors for Quantifying Foot Placement and Clearance during Stair Negotiation.

Trips and slips are significant causal perturbations leading to falls on stairs, especially in older people. The risk of a trip caused by a toe or heel catch on the step edge increases when clearance is small and variable between steps. The risk of a slip increases if the proportion of the foot area in contact with the step is reduced and variable between steps. To assess fall risk, these measurements are typically taken in a gait lab using motion-capture optoelectronic systems. The aim of this work was to develop a novel smart shoe equipped with sensors to measure foot placement and foot clearance on stairs in real homes. To validate the smart shoe as a tool for estimating stair fall risk, twenty-five older adults' sensor-based measurements were compared against foot placement and clearance measurements taken in an experimental staircase in the lab using correlations and Bland-Altman agreement techniques. The results showed that there was a good agreement and a strong positive linear correlation for foot placement (r = 0.878, p < 0.000) and foot clearance (r = 0.967, p < 0.000) between sensor and motion analysis, offering promise for advancing the current prototype into a measurement tool for fall risk in real-life staircases.

Truth that Matters

Truth that Matters

Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes

This work presents a novel method for motion sensor placement within smart homes. Using recordings from 3D depth cameras within six real homes, clusters are created with the resident’s tracked location. The resulting clusters identify the possible position of a sensor and its field of view. By using a sequence of clusters as input to a Recurrent Neural Network, we evaluate our method on the task of activity recognition and prediction. These results are compared to using sensor events as input sequence, from motion sensors that were installed empirically in the same homes. Different clustering methods are investigated and all outperform the installed motion sensors, achieving a significant increase of prediction accuracy and F1-score.

Demand response with heat pumps: Practical implementation of three different control options.

Three case studies of different heat pump demand response control strategies in real homes are presented. All three households reduced their electricity consumption during a peak period but delivered unintended consequences where the heat pump's logic did not correspond to the demand response requirements. This study highlights that the implementation of heat pump demand response to support electricity system operation requires a clear definition of electricity system need as well as practical demand response mechanisms to be integrated into heating system design.

SoHAM: A Sound-Based Human Activity Monitoring Framework for Home Service Robots

Monitoring daily activities is essential for home service robots to take care of the older adults who live alone in their homes. In this article, we proposed a sound-based human activity monitoring (SoHAM) framework by recognizing sound events in a home environment. First, the method of context-aware sound event recognition (CoSER) is developed, which uses contextual information to disambiguate sound events. The locational context of sound events is estimated by fusing the data from the distributed passive infrared (PIR) sensors deployed in the home. A two-level dynamic Bayesian network (DBN) is used to model the intratemporal and intertemporal constraints between the context and the sound events. Second, dynamic sliding time window-based human action recognition (DTW-HaR) is developed to estimate active sound event segments with their labels and durations, then infer actions and their durations. Finally, a conditional random field (CRF) model is proposed to predict human activities based on the recognized action, location, and time. We conducted experiments in our robot-integrated smart home (RiSH) testbed to evaluate the proposed framework. The obtained results show the effectiveness and accuracy of CoSER, action recognition, and human activity monitoring. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the goal to develop companion robots that can assist older adults living alone. Among many capabilities, monitoring human daily activities is an essential one for such robots. Though computer vision or wearable sensors-based methods have been developed by other researchers, they are not practical due to the privacy concern and intrusiveness. Sound-based daily activity recognition can address these concerns and offer a viable solution. In this regard, our proposed method adopts microphones on the robot and a small set of motion sensors distributed in the home. The proposed theoretical framework was tested in a small-scale mock-up apartment with promising results. Before such companion robots can be deployed to real homes for elderly care, there is a need to improve the robustness of the algorithms. More thorough tests in various realistic home environments should be conducted to fully evaluate the performance of the robots. In addition, privacy concern related to audio capture should be further mitigated.

Smart Homes or Real Homes: Building a Smarter Grid With “Dumb” Houses

Smart homes present numerous potential advantages for home occupants and utilities, including benefits such as energy savings, user convenience, and detailed data collection. Realizing any of these benefits requires a level of home instrumentation, such as the deployment of sensors or upgraded devices within the home. However, due to the expense and overhead of installing such equipment, instrumentation happens slowly and gradually, leading to many homes that are not particularly “smart,” yet should still function as part of the emerging smart grid. As a result, real-world smart home applications, particularly those that operate across many homes, will need to be realized in homes with modest and nonstandardized smart home capabilities. As a case study, we consider the problem of monitoring the energy use of specific devices across many homes without smart home capabilities and describe a prototype system to tackle this problem. Considering such systems may broaden our notions of what constitutes a “smart home” at all.

ApplianceNet: a neural network based framework to recognize daily life activities and behavior in smart home using smart plugs

A smart plug can transform the typical electrical appliance into a smart multi-functional device, which can communicate over the Internet. It has the ability to report the energy consumption pattern of the attached appliance which offer the further analysis. Inside the home, smart plugs can be utilized to recognize daily life activities and behavior. These are the key elements to provide human-centered applications including healthcare services, power consumption footprints, and household appliance identification. In this research, we propose a novel framework ApplianceNet that is based on energy consumption patterns of home appliances attached to smart plugs. Our framework can process the collected univariate time-series data intelligently and classifies them using a multi-layer, feed-forward neural network. The performance of this approach is evaluated on publicly available real homes collected dataset. The experimental results have shown the ApplianceNet as an effective and practical solution for recognizing daily life activities and behavior. We measure the performance in terms of precision, recall, and F1-score, and the obtained score is 87%, 88%, 88%, respectively, which is 11% higher than the existing method in terms of F1-score. Furthermore, our scheme is simple and easy to adopt in the existing home infrastructure.

SIMULATING A REAL TIME INTERACTIVE TELEMEDICINE IN A TYPICAL NIGERIAN HOSPITAL

Telemedicine involves the provision of health care and sharing of medical knowledge using telecommunications technologies. Diagnostic, patient education health can be provided via telemedicine. Due to the shortage of medical experts, hospitals coupled with geographic isolation and lack of opportunities, delay in the system, and cost of transportation, it important to develop a robust system that will improve and change the present state of health care and delivery services. In this research work, interactive service telemedicine is designed to provide tools for overcoming the unequal distribution of health care. The proposed system also provides opportunities for patients to gain easy access to medical personnel. The methodology used for this research work is the Dynamic System Development Method (DSDM). As a result of this research medical personnel, have real homes. Telemedicine is the future of health care.

Toward enhancing the autonomy of a telepresence mobile robot for remote home care assistance

AbstractIn health care, a telepresence robot could be used to have a clinician or a caregiver assist seniors in their homes, without having to travel to these locations. However, the usability of these platforms for such applications requires that they can navigate and interact with a certain level of autonomy. For instance, robots should be able to go to their charging station in case of low energy level or telecommunication failure. The remote operator could be assisted by the robot’s capabilities to navigate safely at home and to follow and track people with whom to interact. This requires the integration of autonomous decision-making capabilities on a platform equipped with appropriate sensing and action modalities, which are validated out in the laboratory and in real homes. To document and study these translational issues, this article presents such integration on a Beam telepresence platform using three open-source libraries for integrated robot control architecture, autonomous navigation and sound processing, developed with real-time, limited processing and robustness requirements, so that they can work in real-life settings. Validation of the resulting platform, named SAM, is presented based on the trials carried out in 10 homes. Observations made provide guidance on what to improve and will help identify interaction scenarios for the upcoming usability studies with seniors, clinicians and caregivers.

Emissions Measurements from Household Solid Fuel Use in Haryana, India: Implications for Climate and Health Co-benefits.

A large concern with estimates of climate and health co-benefits of "clean" cookstoves from controlled emissions testing is whether results represent what actually happens in real homes during normal use. A growing body of evidence indicates that in-field emissions during daily cooking activities differ substantially from values obtained in laboratories, with correspondingly different estimates of co-benefits. We report PM2.5 emission factors from uncontrolled cooking (n = 7) and minimally controlled cooking tests (n = 51) using traditional chulha and angithi stoves in village kitchens in Haryana, India. Minimally controlled cooking tests (n = 13) in a village kitchen with mixed dung and brushwood fuels were representative of uncontrolled field tests for fine particulate matter (PM2.5), organic and elemental carbon (p > 0.5), but were substantially higher than previously published water boiling tests using dung or wood. When the fraction of nonrenewable biomass harvesting, elemental, and organic particulate emissions and modeled estimates of secondary organic aerosol (SOA) are included in 100 year global warming commitments (GWC100), the chulha had a net cooling impact using mixed fuels typical of the region. Correlation between PM2.5 emission factors and GWC (R2 = 0.99) implies these stoves are climate neutral for primary PM2.5 emissions of 8.8 ± 0.7 and 9.8 ± 0.9 g PM2.5/kg dry fuel for GWC20 and GWC100, respectively, which is close to the mean for biomass stoves in global emission inventories.

Feeling of Safety and Comfort towards a Socially Assistive Unmanned Aerial Vehicle That Monitors People in a Virtual Home.

Unmanned aerial vehicles (UAVs) represent a new model of social robots for home care of dependent persons. In this regard, this article introduces a study on people’s feeling of safety and comfort while watching the monitoring trajectory of a quadrotor dedicated to determining their condition. Three main parameters are evaluated: the relative monitoring altitude, the monitoring velocity and the shape of the monitoring path around the person (ellipsoidal or circular). For this purpose, a new trajectory generator based on a state machine, which is successfully implemented and simulated in MATLAB/Simulink®, is described. The study is carried out with 37 participants using a virtual reality (VR) platform based on two modules, UAV simulator and VR Visualiser, both communicating through the MQTT protocol. The participants’ preferences have been a high relative monitoring altitude, a high monitoring velocity and a circular path. These choices are a starting point for the design of trustworthy socially assistive UAVs flying in real homes.

Platform Utilizing Similar Users’ Data to Detect Anomalous Operation of Home IoT Without Sharing Private Information

To mitigate the risk of cyberattacks on home IoT devices, we have proposed a method for detecting anomalous operations by learning the behaviors of users based on the operation sequences of their home IoT devices and home conditions. While this method requires a sufficient amount of training data, achieving accurate detection is still possible by utilizing the data of users with similar lifestyles. However, users are unwilling to share their private information with others. In this study, we propose a platform to utilize data of similar users without sharing private information. We introduce an agent that learns behaviors of users to detect anomalous operations in each home and cooperates with other agents. In this framework, an agent requiring cooperation with other agents sends a question to the other agents, attaching identifiers of past questions that are similar to the behaviors learned. The receivers decide whether the question is from a similar agent by using the attached information. If the question is from a similar agent, the agent answers the question. We evaluate our platform by using behavior datasets collected from real homes. We simulate two cases: (1) sequences of operations are monitored, and (2) home IoT devices are used alone but sequences cannot be used for detection. The results show that our framework has a 50.5% higher detection ratio for case (1) when using the behavioral data of each user. For case (2), our framework has a 13.4% higher detection ratio when using all the behavioral data of users.

Effect of boiler oversizing on efficiency: a dynamic simulation study.

Gas boilers dominate domestic heating in the UK, and significant efficiency improvements have been associated with condensing boilers. However, the potential remains for further efficiency improvement by refining the control, system specification and installation in real dwellings. Dynamic building simulation modelling, including detailed heating system componentry, enables a deeper analysis of boiler underperformance. This paper explores the link between the space heat oversizing of boilers and on/off cycling using dynamic simulation, and their subsequent effect on boiler efficiency and internal temperatures. At plant size ratio (PSR) 8.5 daily cycles numbered over 50, similar to median levels seen in real homes. Simulations show that typical oversizing (PSR >3) significantly increases cycling behaviour and brings an efficiency penalty of 6–9%. There is a clear link between raising PSR, increased cycling and an associated decreased efficiency; however, in the UK, boilers are regularly oversized with respect to space heating, especially combination boilers to cover peak hot water demand. Current legislation and labelling (ErP and SAP) overlook PSR as a determinant of system efficiency, failing to incentivise appropriate sizing. Reducing boiler oversizing through addressing installation practices and certification has the potential to significantly improve efficiency at low cost, decreasing associated carbon emissions.Practical application: This research provides the basis for a practical and cost effective means of assessing the potential for underperformance of boiler heating systems at the point of installation or refurbishment. By assessing the oversizing of the boiler with respect to space heating, unnecessary cycling and the associated efficiency penalty can be avoided. Plant size ratio, as an indicator of cycling potential, can be implemented in energy performance certificates (EPCs), through the standard assessment procedure (SAP), using existing data. The potential for real carbon savings in the existing boiler stock is considerable, and the findings have wider implications for next generation heating systems.

Results of Field Trials with a Mobile Service Robot for Older Adults in 16 Private Households

In this article, we present results obtained from field trials with the Hobbit robotic platform, an assistive, social service robot aiming at enabling prolonged independent living of older adults in their own homes. Our main contribution lies within the detailed results on perceived safety, usability, and acceptance from field trials with autonomous robots in real homes of older users. In these field trials, we studied how 16 older adults (75 plus) lived with autonomously interacting service robots over multiple weeks. Robots have been employed for periods of months previously in home environments for older people, and some have been tested with manipulation abilities, but this is the first time a study has tested a robot in private homes that provided the combination of manipulation abilities, autonomous navigation, and non-scheduled interaction for an extended period of time. This article aims to explore how older adults interact with such a robot in their private homes. Our results show that all users interacted with Hobbit daily, rated most functions as well working, and reported that they believe that Hobbit will be part of future elderly care. We show that Hobbit’s adaptive behavior approach towards the user increasingly eased the interaction between the users and the robot. Our trials reveal the necessity to move into actual users’ homes, as only there, we encounter real-world challenges and demonstrate issues such as misinterpretation of actions during non-scripted human-robot interaction.

My Own Private Nightlife

Private nightlife environments of young people are likely characterized by their physical attributes, particular ambiance, and activities, but relatively little is known about it from social media studies. For instance, recent work has documented ambiance and physical characteristics of homes using pictures from Airbnb, but questions remain on whether this kind of curated data reliably represents everyday life situations. To describe the physical and ambiance features of homes of youth using manual annotations and machine-extracted features, we used a unique dataset of 301 crowdsourced videos of home environments recorded in-situ by young people on weekend nights. Agreement among five independent annotators was high for most studied variables. Results of the annotation task revealed various patterns of youth home spaces, such as the type of room attended (e.g., living room and bedroom), the number and gender of friends present, and the type of ongoing activities (e.g., watching TV alone; or drinking, chatting and eating in the presence of others.) Then, object and scene visual features of places, extracted via deep learning, were found to correlate with ambiances, while sound features did not. Finally, the results of a regression task for inferring ambiances from those features showed that six of the ambiance categories can be inferred with R 2 in the [0.21, 0.69] range. Our work is novel with regard to the type of data (crowdsourced videos of real homes of young people) and the analytical design (combined use of manual annotation and deep learning to identify relevant cues), and contributes to the understanding of home environments represented through digital media.

Agency in the Smart Home of the Future

Agency in the Smart Home of the Future is a short documentary film about a unique design fiction social experiment. Comprised mostly of trace evidence video footage recorded by field researchers on their smart phones, the film takes audiences directly into the reflexive lived experience of four Australian households, each of whom have had their real homes transformed into fully functional smart homes. Smart speakers, smart TVs, smart fridges, robo vacs, sensor lighting, etcetera! is installed into their homes as a gestalt to generate a paradigmatic shift in their everyday living and interactions. In the process, we discover how human agency and structure reproduce in this potential living environment of the future.

Monitoring meaningful activities using small low-cost devices in a smart home

A challenge associated with an ageing population is increased demand on health and social care, creating a greater need to enable persons to live independently in their own homes. Ambient assistant living technology aims to address this by monitoring occupants’ ‘activities of daily living’ using smart home sensors to alert caregivers to abnormalities in routine tasks and deteriorations in a person’s ability to care for themselves. However, there has been less focus on using sensing technology to monitor a broader scope of so-called ‘meaningful activities’, which promote a person’s emotional, creative, intellectual, and spiritual needs. In this paper, we describe the development of a toolkit comprised of off-the-shelf, affordable sensors to allow persons with dementia and Parkinson’s disease to monitor meaningful activities as well as activities of daily living in order to self-manage their life and well-being. We describe two evaluations of the toolkit, firstly a lab-based study to test the installation of the system including the acuity and placement of sensors and secondly, an in-the-wild study where subjects who were not target users of the toolkit, but who identified as technology enthusiasts evaluated the feasibility of the toolkit to monitor activities in and around real homes. Subjects from the in-the-wild study reported minimal obstructions to installation and were able to carry out and enjoy activities without obstruction from the sensors, revealing that meaningful activities may be monitored remotely using affordable, passive sensors. We propose that our toolkit may enhance assistive living systems by monitoring a wider range of activities than activities of daily living.

A Sensor Fusion Approach to Indoor Human Localization Based on Environmental and Wearable Sensors

The goal of this paper is to localize a resident in indoor environments by using motion information from distributed environmental sensors and body activity information from wearable sensors. The passive infrared sensor nodes distributed in a home provide binary information about human motion in their field of views, while the wearable inertial measurement unit sensor node collects motion data that can be used in body activity recognition, walking velocity, and heading estimation. Basic human activities such as sitting, sleeping, standing, and walking are recognized. We proposed a particle filter-based sensor fusion algorithm that takes advantage of the human location/activity correlation in indoor environments to increase the localization accuracy. Experiments were conducted in a mock apartment testbed. We used the ground truth data obtained from a motion capture system to evaluate the results. Note to Practitioners —This paper is motivated by a goal to realize various home automation applications through human localization. For example, with the knowledge of human location, lighting, and air conditioning systems can be more efficiently managed, or a home service robot can provide better service to the resident. There currently lacks an accurate human localization method in indoor environments. The theoretical framework proposed in this paper aims to combine simple environmental sensors (passive infrared sensors) and a single wearable motion sensor to accurately track human locations. This method reduces the intrusiveness to the human, while keeping the cost of the infrastructure low. The theoretical framework has been tested and evaluated in a mock apartment environment as a proof of concept. For deployment into real homes, we need to conduct more realistic tests and thorough evaluation of the system. It is also necessary to reduce the size and weight of the wearable motion sensor and simplify the installation of the environmental sensors.