Sensor-based Home Research Articles

Sign To Speech Conversion And Home Automation Control Using Smart Gloves

In today's world, there are many new technologies changing the way we do things. One interesting technology is the home automation system. It helps people control things in their homes from far away, making life more comfortable, saving money, and being easy to use. But some people find it a bit tricky to use these systems well. To make it simpler for everyone, we can use something called a flex sensor-based home automation system. This system lets you control things by moving your hands, which is really helpful for older people or those who might find it a bit hard to move. It's also great for people who haven't had much learning. And if we add voice help, it becomes even easier. This way, people who are in bed or dealing with physical problems can use it too. This makes technology not just cool but helpful for everyone in different situations. In this paper, we explore the integration of flex sensors onto hand gloves to facilitate a dynamic interaction between hand movements and technological outputs. The flex sensors, akin to miniature potentiometers, are strategically affixed to the fingers, registering changes in value corresponding to the bending action. As the finger bends, the sensor's resistance alters, influencing the output in an inversely proportional manner. This innovative system allows for nuanced control, where specific angles of finger bending lead to calibrated adjustments in output, demonstrating a responsive and intuitive interface between human gestures and technology. The implementation of flex sensor- based gloves introduces a versatile means of capturing and translating hand movements into actionable data. The project leverages the concept of resistance modulation to precisely interpret the degree of finger bending, creating a reliable framework for diverse applications such as gesturecontrolled devices or assistive technologies for individuals with limited mobility. The abstracted communication between human hand gestures and technology opens avenues for accessible and intuitive interfaces, promising potential applications across various domains.

Explanation before Adoption: Supporting Informed Consent for Complex Machine Learning and IoT Health Platforms

Explaining health technology platforms to non-technical members of the public is an important part of the process of informed consent. Complex technology platforms that deal with safety-critical areas are particularly challenging, often operating within private domains (e.g. health services within the home) and used by individuals with various understandings of hardware, software, and algorithmic design. Through two studies, the first an interview and the second an observational study, we questioned how experts (e.g. those who designed, built, and installed a technology platform) supported provision of informed consent by participants. We identify a wide range of tools, techniques, and adaptations used by experts to explain the complex SPHERE sensor-based home health platform, provide implications for the design of tools to aid explanations, suggest opportunities for interactive explanations, present the range of information needed, and indicate future research possibilities in communicating technology platforms.

The effectiveness of a telemedical program for COVID-19 positive high-risk patients in domestic isolation

Abstract Background For almost two years, the Covid-19 pandemic has posed an enormous challenge to healthcare systems. Recurrent waves of disease brought the health systems to the limit of their resilience. Purpose The Tele-Covid telemedicine care program was installed in December 2020 to monitor high-risk patients in home isolation. Close monitoring allows early detection of disease deterioration and timely intensification of therapy, ideally avoiding intensive care. Conversely, if the course of the disease is stable, unnecessary hospitalisation can be avoided, thus reducing the burden on the healthcare system. Methods Patient acquisition was performed in collaboration with the local public health service and primary care physicians. Covid-19 positive high-risk patients (age >65 years and/or severe comorbidities) from the greater Innsbruck area were fitted with an ear sensor-based home monitoring system. The ear sensor measures SpO2, respiratory rate, body temperature and heart rate. The monitoring team (25 medical students supervised by 6 physicians) provided continuous monitoring of vital signs (24/7). After validation of the measurements, the collected parameters were evaluated using a specially developed risk score. If a defined risk score was exceeded, the patient was contacted by telephone. The combination of the clinical condition and the risk score determined the further course of action: (a) wait and see, (b) notify the primary care physician, or (c) refer for inpatient admission. The program was active from December 2020 to March 2022. In Summer 2021, the program was temporarily paused due to the epidemiological situation. Results A total of 132 patients (59.8% women) were monitored. The median age was 74 years (IQR: [67.3–80.8]). 91 patients (68.9%) had at least one relevant comorbidity. During the monitoring period, hospitalisation was required in 20 patients (15.2%), 3 of whom were transferred to the intensive care unit. Of the hospitalised patients, 3 (15%) patients died. During the same monitoring period, the Austrian Ministry of Health reported a mortality rate of 20.5% of all hospitalised patients in Austria aged 70–79 years. Subjectively, the patients felt safe due to close monitoring. Conclusion The Tele-Covid program is the successful implementation of a remote monitoring system in a pandemic situation. In the future, a broad application of the program is feasible. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Funded by the Region of the Tyrol

The effectiveness of a telemedical program for COVID-19 positive high-risk patients in domestic isolation

Abstract Background For almost two years, the Covid-19 pandemic has posed an enormous challenge to healthcare systems. Recurrent waves of disease brought the health systems to the limit of their resilience. Purpose The Tele-Covid telemedicine care program was installed in December 2020 to monitor high-risk patients in home isolation. Close monitoring allows early detection of disease deterioration and timely intensification of therapy, ideally avoiding intensive care. Conversely, if the course of the disease is stable, unnecessary hospitalisation can be avoided, thus reducing the burden on the healthcare system. Methods Patient acquisition was performed in collaboration with the local public health service and primary care physicians. Covid-19 positive high-risk patients (age >65 years and/or severe comorbidities) from the greater Innsbruck area were fitted with an ear sensor-based home monitoring system. The ear sensor measures SpO2, respiratory rate, body temperature and heart rate. The monitoring team (25 medical students supervised by 6 physicians) provided continuous monitoring of vital signs (24/7). After validation of the measurements, the collected parameters were evaluated using a specially developed risk score. If a defined risk score was exceeded, the patient was contacted by telephone. The combination of the clinical condition and the risk score determined the further course of action: (a) wait and see, (b) notify the primary care physician, or (c) refer for inpatient admission. The program was active from December 2020 to March 2022. In Summer 2021, the program was temporarily paused due to the epidemiological situation. Results A total of 132 patients (59.8% women) were monitored. The median age was 74 years (IQR: [67.3–80.8]). 91 patients (68.9%) had at least one relevant comorbidity. During the monitoring period, hospitalisation was required in 20 patients (15.2%), 3 of whom were transferred to the intensive care unit. Of the hospitalised patients, 3 (15%) patients died. During the same monitoring period, the Austrian Ministry of Health reported a mortality rate of 20.5% of all hospitalised patients in Austria aged 70–79 years. Subjectively, the patients felt safe due to close monitoring. Conclusion The Tele-Covid program is the successful implementation of a remote monitoring system in a pandemic situation. In the future, a broad application of the program is feasible. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Funded by the Region of the Tyrol

Effectiveness of Sensor-based Rehabilitation in Improving Outcomes in Patients Undergoing Total Knee Arthroplasty.

Physical rehabilitation after total knee arthroplasty (TKA) is important for long-term functional recovery. Recently, sensor-based home rehabilitation (SHR) has gained prominence as a promising method that allows monitoring and guidance that is both structured and accessible, compared to traditional methods of physical rehabilitation. Despite the advent of wearable sensor systems, there is a paucity of evidence regarding SHR in the current literature. Thus, this systematic review aimed to evaluate the effect of wearable SHR on post-TKA outcomes. We performed a systematic search of three electronic databases from the beginning of record to March 12, 2021. Primary outcomes were patient-reported outcome measures (PROMs) after rehabilitation, including the Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Knee Society Score (KSS). Secondary outcomes were physical activity levels and functional performance including range of motion (ROM) and Timed Up and Go Test (TUG). A total of 16 studies involving 1321 subjects were included. All wearable sensors in our included studies involved a combination of accelerometers, gyroscopes and magnetometers as functional units. These studies reported favourable outcomes for all three PROMs, although the extent of improvement in specific domains varied among studies. Moreover, physical activity in terms of daily steps and time spent on physical activity increased post-rehabilitation. Similarly, there were improvements in ROM and TUG that reflected a favourable post-operative trajectory during rehabilitation. SHR is effective for improving subjective and objective outcomes post-TKA. The role of SHR should be evaluated by a dedicated cost-benefit analysis to facilitate its wider adoption in healthcare systems.

Testing the Feasibility of Sensor-Based Home Health Monitoring (TEC4Home) to Support the Convalescence of Patients With Heart Failure: Pre-Post Study.

BackgroundPatients with heart failure (HF) can be affected by disabling symptoms and low quality of life. Furthermore, they may frequently need to visit the emergency department or be hospitalized due to their condition deteriorating. Home telemonitoring can play a role in tracking symptoms, reducing hospital visits, and improving quality of life.ObjectiveOur objective was to conduct a feasibility study of a home health monitoring (HHM) solution for patients with HF in British Columbia, Canada, to prepare for conducting a randomized controlled trial.MethodsPatients with HF were recruited from 3 urban hospitals and provided with HHM technology for 60 days of monitoring postdischarge. Participants were asked to monitor their weight, blood pressure, and heart rate and to answer symptomology questions via Bluetooth sensors and a tablet computer each day. A monitoring nurse received this data and monitored the patient’s condition. In our evaluation, the primary outcome was the combination of unscheduled emergency department revisits of discharged participants or death within 90 days. Secondary outcomes included 90-day hospital readmissions, patient quality of life (as measured by Veterans Rand 12-Item Health Survey and Kansas City Cardiomyopathy Scale), self-efficacy (as measured by European Heart Failure Self-Care Behaviour Scale 9), end-user experience, and health system cost-effectiveness including cost reduction and hospital bed capacity. In this feasibility study, we also tested the recruitment strategy, clinical protocols, evaluation framework, and data collection methods.ResultsSeventy participants were enrolled into this trial. Participant engagement to monitoring was measured at 94% (N=70; ie, data submitted 56/60 days on average). Our evaluation framework allowed us to collect sound data, which also showed encouraging trends: a 79% reduction of emergency department revisits post monitoring, an 87% reduction in hospital readmissions, and a 60% reduction in the median hospital length of stay (n=36). Cost of hospitalization for participants decreased by 71%, and emergency department visit costs decreased by 58% (n=30). Overall health system costs for our participants showed a 56% reduction post monitoring (n=30). HF-specific quality of life (Kansas City Cardiomyopathy Scale) scores showed a significant increase of 101% (n=35) post monitoring (P<.001). General quality of life (Veterans Rand 12-Item Health Survey) improved by 19% (n=35) on the mental component score (P<.001) and 19% (n=35) on the physical component score (P=.02). Self-efficacy improved by 6% (n=35). Interviews with participants revealed that they were satisfied overall with the monitoring program and its usability, and participants reported being more engaged, educated, and involved in their self-management.ConclusionsResults from this small-sample feasibility study suggested that our HHM intervention can be beneficial in supporting patients post discharge. Additionally, key insights from the trial allowed us to refine our methods and procedures, such as shifting our recruitment methods to in-patient wards and increasing our scope of data collection. Although these findings are promising, a more rigorous trial design is required to test the true efficacy of the intervention. The results from this feasibility trial will inform our next step as we proceed with a randomized controlled trial across British Columbia.Trial RegistrationClinicalTrials.gov NCT03439384; https://clinicaltrials.gov/ct2/show/NCT03439384

Human Identification for Activities of Daily Living: A Deep Transfer Learning Approach

ABSTRACT Sensor-based home Activities of Daily Living (ADLs) monitoring systems have emerged to monitor elderly people’s self-care ability remotely. However, the unobtrusive, privacy-friendly object motion sensor-based systems face challenges such as scarce labeled data and ADL performer confusion in a multi-resident setting. This study adopts the design science paradigm to develop an innovative deep transfer learning framework for human identification (DTL-HID) to address both challenges. A novel convolutional neural network (CNN) is proposed to automatically extract comprehensive temporal and cross-axial motion patterns for the DTL-HID framework. We rigorously evaluate the DTL-HID framework against state-of-the-art benchmarks (e.g., k Nearest Neighbors, Support Vector Machines, and alternative CNN designs). Results demonstrate our proposed DTL-HID framework can identify the ADL performer accurately even on a small amount of labeled data. We demonstrate a case study and discuss how stakeholders can further apply this approach to unobtrusive smart home monitoring for senior citizens. Beyond demonstrating the framework’s practical utility, we discuss two implications of our design principles to mobile analytics and design science research: (1) extracting temporal and axial local dependencies can capture richer information from multi-axial time-series data and (2) transferring knowledge learned on a relevant source domain with sufficient data can improve the performance of the desired task on the target domain with scarce data.

Sensor-Based Optimization Model for Air Quality Improvement in Home IoT.

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market.

Designing Home Security Monitoring System Based Internet of Things(IoTs) Model

The uninhabited housing situation is very vulnerable to the existence of theft activities. Anticipation using a variety of door locking system of the house also often can be overcome by various the perpetrators of theft. Information technology today is inseparable from the development of internet communication technology. The Internet today are an essential part of every society as a means of Informastion Source. Therefore, there needs to be integration of home security system which can be integrated with internet based information system as one of innovation in security system. The research objective is the design and manufacture of sensor-based home security system as a passive infrared motion detectors integrated with Internet-based information system with the concept of the Internet of things (IoTs). The research used Passive Infra Red Sensor as input data, Arduino Uno Microcontroller as Sensor Data Processor and Ethernet as Web Server. The research resulted in a series of home security information system prototype based internet information and has successfully accessed through an Internet browser in the form of home security information to detect the presence of an intruder without permission remotely via the internet.

PATHway I: design and rationale for the investigation of the feasibility, clinical effectiveness and cost-effectiveness of a technology-enabled cardiac rehabilitation platform

IntroductionExercise-based cardiac rehabilitation (CR) independently alters the clinical course of cardiovascular diseases resulting in a significant reduction in all-cause and cardiac mortality. However, only 15%–30% of all eligible patients participate...

Sensor-based home activity monitoring of geriatric fracture patients in rehabilitation – The GAL NATARS study

O 013 Sensor-based home activity monitoring of geriatric fracture patients in rehabilitation –The GAL NATARS study M. Marscholleka,b,c,d,e,f,g,h,i, M. Apela,b,c,d,e,f,g,h,i, P. Bentea,b,c,d,e,f,g,h,i, L. Dasenbrocka,b,c,d,e,f,g,h,i, K. Elbersa,b,c,d,e,f,g,h,i, R. Hauxa,b,c,d,e,f,g,h,i, A. Heina,b,c,d,e,f,g,h,i, G. Kolba,b,c,d,e,f,g,h,i, H. Kunemunda,b,c,d,e,f,g,h,i, C. Lammel-Polchaua,b,c,d,e,f,g,h,i, M. Meisa,b,c,d,e,f,g,h,i, H. Meyer zu Schwabedissena,b,c,d,e,f,g,h,i, H. Remmersa,b,c,d,e,f,g,h,i, E.E. Steena,b,c,d,e,f,g,h,i, J. Wanga,b,c,d,e,f,g,h,i, K.H. Wolf a,b,c,d,e,f,g,h,i, J. Bauera,b,c,d,e,f,g,h,i a Hannover Medical School, Hannover, Germany b Klinikum Oldenburg, Oldenburg. Germany c Std. Klinikum Braunschweig, Braunschweig, Germany d St. Bonifatius Hospital Lingen, Lingen, Germany e TU Braunschweig, Braunschweig, Germany f OFFIS, Oldenburg, Germany g Universitat Vechta, Vechta, Germany h Horzentrum Oldenburg, Oldenburg, Germany i Universitat Osnabruck, Osnabruck, Germany

Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System

This paper describes the development of a fall database for biomechanical simulation. First, data on children’s daily activities were collected at a “sensor home,” which is a imitation daily living space. The sensor-based home comprises a video-surveillance system embedded into a daily-living environment and a wearable acceleration-gyro sensor. Falls were then detected from sensor data using a fall detection algorithm that we developed, and videos of detected falls were extracted from long-time recorded video. Extracted videos were used for fall motion analysis. A new Computer Vision (CV) algorithm was developed to automate fall motion analysis. Using the CV algorithm, fall motion data were accumulated into a database. The database allows a user to perform conditional searches for fall data by inputting search conditions, such as a child’s attributes, and fall situations.

Unsynchronized Duty-cycle Control for Sensor Based Home Automation Networks

Home automation networks are good environments for merging sensor networks and consumer electronics technologies. It is very important to reduce the energy consumption of each sensor node because sensor nodes operate with limited power based on a battery that cannot be easily replaced. One of the primary mechanisms for achieving low energy operation in energy-constrained wireless sensor networks is the duty-cycle operation, but this operation has several problems. For example, unnecessary energy consumption occurs during synchronization between transmission schedules and sleep schedules. In addition, a low duty-cycle usually causes more performance degradation, if the network becomes congested. Therefore, an appropriate control scheme is required to solve these problems. In this paper, we propose UDC (Unsynchronized Duty-cycle Control), which prevents energy waste caused by unnecessary preamble transmission and avoids congestion using duty-cycle adjustment. In addition, the scheme adjusts the starting point of the duty-cycle in order to reduce sleep delay. Our simulation results show that UDC improves the reliability and energy efficiency while reducing the end-to-end delay of the unsynchronized duty-cycled MAC (Media Access Control) protocol in sensor-based home automation networks.

Current Sensor Based Home Appliance and State of Appliance Recognition

This paper presents a current sensor-based home appliance and its state recognition method for intelligent outlets. Our system has three main functions: remote control, monitoring, and power supply schedule management. This research focuses particular on the monitoring function. To recognize the appliance and the state of the appliance, we extract ten features based on a measured current signal. In the experiment, we gather a number of signals with various appliances, and find that three features Irms, Iavg, and Ipeak yield valid recognition results of 84.3%, 86.4%, and 90.3% for classifying the state of the appliance into three categories. Moreover, sufficient recognition rates of 97.4%, 97.7%, and 99.0% are obtained by consideration of three candidates.