Radar human breathing dataset for applications of ambient assisted living and search and rescue operations
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SUBMITTER: Eren C
PROVIDER: S-EPMC10694063 | biostudies-literature | 2023 Nov
REPOSITORIES: biostudies-literature
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Project description:IntroductionFor the first time in human history, the number of older people will be higher than the number of children. The prevalence of chronic diseases, such as hypertension, cardiovascular disease, diabetes and mental disorders in older adults is high. Given that, it is essential to make usage of related technology to provide improved health conditions and reduce the costs for promoting ageing in place, and that is precisely the aim of Ambient Assisted Living technology. Considering that these systems provide significant benefit to a vast number of stakeholders, can be applied to the functional diversity of application domains and have high economic and social impacts, it is essential to create reusable and interoperable platforms and standards that are able to deal with the heterogeneity of applications and domains. In this sense, reference architectures have been proposed and evaluated. A comprehensive scoping review concerning the reference architectures must clarify specific aspects, such as what the main domains are and how the solutions effectively deal with them.MethodsThis scoping review will follow the methodology framework defined in 'Scoping studies: advancing the methodology'. In this methodological framework, six stages are proposed for scoping review studies: identifying the research question; identifying relevant studies; study selection; charting the data; collating, summarising and reporting the results; and consultation. The research questions aim to investigate what are the motivations, stakeholders, benefits, domains, approaches, architectural components and governance aspects of the proposed reference architectures and models. The team will focus on the Scopus Document Search, PubMed (MEDLINE), IEEE Xplore Digital Library, ACM Digital Library and Science Direct electronic research databases. The search query is a combination of terms related to Ambient Assisted Living AND Reference Architecture.Ethics and disseminationThis is a scoping review study and there is no requirement for ethical approval, as primary data will not be collected. The results from this scoping review will be published in a peer-reviewed journal and reported at scientific meetings. We intend to share the results with the International Standards and Conformity Assessment - SyC AAL from Canada to use the review as a basis for establishing an assessment model of reference architectures.
| S-EPMC7783615 | biostudies-literature
Project description:Continuous monitoring of frail individuals for detecting dangerous situations during their daily living at home can be a powerful tool toward their inclusion in the society by allowing living independently while safely. To this goal we developed a pose recognition system tailored to disabled students living in college dorms and based on skeleton tracking through four Kinect One devices independently recording the inhabitant with different viewpoints, while preserving the individual's privacy. The system is intended to classify each data frame and provide the classification result to a further decision-making algorithm, which may trigger an alarm based on the classified pose and the location of the subject with respect to the furniture in the room. An extensive dataset was recorded on 12 individuals moving in a mockup room and undertaking four poses to be recognized: standing, sitting, lying down, and "dangerous sitting." The latter consists of the subject slumped in a chair with his/her head lying forward or backward as if unconscious. Each skeleton frame was labeled and represented using 10 discriminative features: three skeletal joint vertical coordinates and seven relative and absolute angles describing articular joint positions and body segment orientation. In order to classify the pose of the subject in each skeleton frame we built a two hidden layers multi-layer perceptron neural network with a "SoftMax" output layer, which we trained on the data from 10 of the 12 subjects (495,728 frames), with the data from the two remaining subjects representing the test set (106,802 frames). The system achieved very promising results, with an average accuracy of 83.9% (ranging 82.7 and 94.3% in each of the four classes). Our work proves the usefulness of human pose recognition based on machine learning in the field of safety monitoring in assisted living conditions.
| S-EPMC7240018 | biostudies-literature
Project description:Silicon anode materials have been developed to achieve high capacity lithium ion batteries for operating smart phones and driving electric vehicles for longer time. Serious volume expansion induced by lithiation, which is the main drawback of silicon, has been challenged by multi-faceted approaches. Mechanically rigid and stiff polymers (e.g. alginate and carboxymethyl cellulose) were considered as the good choices of binders for silicon because they grab silicon particles in a tight and rigid way so that pulverization and then break-away of the active mass from electric pathways are suppressed. Contrary to the public wisdom, in this work, we demonstrate that electrochemical performances are secured better by letting silicon electrodes breathe in and out lithium ions with volume change rather than by fixing their dimensions. The breathing electrodes were achieved by using a polysaccharide (pullulan), the conformation of which is modulated from chair to boat during elongation. The conformational transition of pullulan was originated from its α glycosidic linkages while the conventional rigid polysaccharide binders have β linkages.
| S-EPMC4585771 | biostudies-literature
Project description:BackgroundAmbient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work.ObjectiveThis study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems.MethodsThis study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI.ResultsWe included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern.ConclusionsThis study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines.Trial registrationPROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.
| S-EPMC9675018 | biostudies-literature
Project description:The automotive industry is interested in the estimation of vehicle trailer rotation (trailer angle or hitch angle) due to its use in trailer control algorithms. We present an experimental dataset collected in a study of the estimation problem [1] and a MATLAB code implementation of the study. The data collection apparatus is a truck mock-up that is attached to a flatbed trailer at the hitch ball. Two radars are installed in the taillight fixtures of the truck and a camera is installed in the truck's tailgate like a typical backup camera installation. A rotary motion sensor is also installed at the hitch ball to provide ground truth measurement of the trailer angle. To aid analysis of the dataset, both radar detections are transformed onto a vehicle coordinate system (VCS) having its origin at the hitch ball i.e. the different radar viewpoints are combined into one with respect to the hitch ball. The MATLAB code presented with this article has two major functionalities. The first functionality is the visualization of both radar detections, the combined radar detections in the VCS, the camera images, and the ground truth angles, as the trailer rotates. The second functionality of the code is the replication of the estimation results in [1], which used only the radar detections from the dataset.
| S-EPMC8405922 | biostudies-literature
Project description:BackgroundTo cope with the upcoming demographic change, economic efforts in the European Union are undertaken to promote activities in research and development of Ambient Assisted Living (AAL) solutions. As a result, a large variety of AAL products will be available in the next years. Only very few of these products are comprehensively evaluated regarding different aspects of quality of life in the target population. The aim of this study is to examine the effects of AAL on quality of life, health and technology acceptance of people at advanced age living in assisted living homes providing them the ModuLAAr Ambient Assisted Living system.Methods/designA treatment group of adults aged 60 years and older will be recruited within the participating assisted living homes. At baseline, the participating volunteers will report on quality of life, subjective health and sociodemographic conditions. After system installation, prospective follow-up (1, 4, 12 and 18 months) with additional reporting on technology acceptance and usability of the system will be conducted. Mixed methods data collection, linking quantitative data to interview-gathered qualitative data will be applied. Primary outcome measure will be the change in quality of life and subjective health across study duration.DiscussionAs there is currently very little evidence that AAL solutions can contribute to improved health and the particular dimensions of quality of life in elderly persons, there is a need to assess these technologies and services more carefully. This field trial seeks to investigate the most relevant aspects connected to advanced information and communication technologies and their impact on daily life of residents in assisted living conditions.Trial registrationClinicalTrials.gov: NCT02130102.
| S-EPMC4223693 | biostudies-literature
Project description:The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E. Kozan, G. Kent, Liu, Shi Qiang, 2016b) [1]). Producing an efficient daily schedule to optimise the rail operations requires integration of the main elements of harvesting, transporting and milling in the value chain of the Australian agriculture industry. The data utilised in this research involve four main tables: sidings, harvesters, sectional rail network and trains. The utilised data were collected from Australian sugar mills as a real application. Operations Research techniques such as metaheuristic and constraint programming are used to produce the optimised solutions in an analytical way.
| S-EPMC5053071 | biostudies-literature
Project description:The first weather radar campaign over Lima, the capital of Peru, a desertic area on the western side of the Peruvian Andes, was carried out to study the occurrence of rain events in summer 2018. The weather radar was installed strategically and was able to overlook three river basins: Rimac, Chillón, and Lurin. An X-band radar (PX-1000) was used, which operates at 9.55 GHz. PX-1000 was built by the Advanced Radar Research Center (ARRC) at the University of Oklahoma (U.S.A.). The radar operated from January 26th to April 1st, 2018, at Cerro Suche located 2910 m ASL and 55 km from the city of Lima. The PX-1000 performed plan-position-indicators (PPI) for elevations starting at 0° up to 20°. The data presented here were obtained using a three-dimensional constant-altitude plan-position-indicator (3D CAPPI), which was generated by high resolution (250 m) nearest point algorithm.
| S-EPMC8010614 | biostudies-literature
Project description:People increasingly use various technologies that enable them to ease their everyday lives in different areas. Not only wearable devices are gaining ground, but also sensor-based ambient devices and systems are increasingly perceived as beneficial in supporting users. Especially older and/or frail persons can benefit from the so-called lifelogging technologies assisting the users in different activities and supporting their mobility and autonomy. This paper empirically investigates users' technology acceptance and privacy perceptions related to sensor-based applications implemented in private environments (i.e., passive infrared sensors for presence detection, humidity and temperature sensors for ambient monitoring, magnetic sensors for user-furniture interaction). For this purpose, we designed an online survey entitled "Acceptance and privacy perceptions of sensor-based lifelogging technologies" and collected data from N = 312 German adults. In terms of user acceptance, statistical analyses revealed that participants strongly agree on the benefits of such sensor-based ambient technologies, also perceiving these as useful and easy to use. Nevertheless, their intention to use the sensor-based applications was still rather limited. The evaluation of privacy perceptions showed that participants highly value their privacy and hence require a high degree of protection for their personal data. The potential users assessed the collection of data especially in the most intimate spaces of domestic environments, such as bathrooms and bedrooms, as critical. On the other hand, participants were also willing to provide complete data transparency in case of an acute risk to their health. Our results suggest that users' perceptions of personal privacy largely affect the acceptance and successful adoption of sensor-based lifelogging in home environments.
| S-EPMC9255774 | biostudies-literature
Project description:BackgroundUsing Ambient Assisted Living sensors to detect acute stress could help people mitigate the harmful effects of everyday stressful situations. This would help both the healthy and those affected more by sudden stressors, e.g., people with diabetes or heart conditions. The study aimed to develop a method for providing reliable stress detection based on heart rate variability features extracted from portable devices.MethodsFeatures extracted from portable electrocardiogram sensor recordings were used for training various classification algorithms for stress detection purposes. Data were recorded in a clinical trial with 7 participants and two stressors, the Trier Social Stress Test and the Stroop colour word test, both validated by standardised questionnaires. Different heart rate variability feature sets (all, time-domain and non-linear only, frequency-domain only) were tested to investigate how classification performance is affected, in addition to various time window length setups and participant-wise training sessions. The accuracy and F1 score of the trained models were compared and analysed.ResultsThe best results were achieved with models using time-domain and non-linear heart rate variability features with 5-min-long overlapping time windows, yielding 96.31% accuracy and 96.26% F1 score. Shorter overlapping windows had slightly lower performance, with 91.62-94.55% accuracy and 91.77-94.55% F1 score ranges. Non-overlapping window configurations were less effective, with both accuracy and F1 score below 88%. For participant-wise learning, average F1 scores of 99.47%, 98.93% and 96.1% were achieved for feature sets using all, time-domain and non-linear, and frequency-domain features, respectively.ConclusionThe tested stress detector models based on heart rate variability data recorded by a single electrocardiogram sensor performed just as well as those published in the literature working with multiple sensors, or even better. This suggests that once portable devices such as smartwatches provide reliable hear rate variability recordings, efficient stress detection can be achieved without the need for additional physiological measurements.
| S-EPMC8323289 | biostudies-literature