AAL open source system for the monitoring and intelligent control of nursing homes

[EN] SAFE-ECH is an innovative intelligent AAL open source system for monitoring nursing homes, that creates an Ambient Intelligent environment in a residence by collecting and storing sensor monitoring data, performing intelligent data analysis and specific actions to enhance the safety, comfort and efficient care of aged people. Our system implements open standards of the Open Geospatial Consortium complying with Observations & Measurements Schema (O&M), SensorML and Sensor Web Enablement (SWE) specifications. Our system adapts to the specific needs of each nursing home, integrating the required sensors, actuators, rules and services. It is scalable and allows the management of several residences simultaneously.This research was partially funded by the European Union's Horizon 2020 research and innovation programme as part of the INTERIoT project under Grant Agreement 687283, and by SAFE-ECH funded by the Spanish Ministerio de Industria, Economía y Competitividad (MINECO) under Grant Agreement RTC-2015-4502-1González-Usach, R.; Collado, V.; Esteve Domingo, M.; Palau Salvador, CE. (2017). AAL open source system for the monitoring and intelligent control of nursing homes. IEEE Systems, Man, and Cybernetics Society. 1-6. https://doi.org/10.1109/ICNSC.2017.8000072S1

A new database of healthy and pathological voices☆ Ugo Cesari a, Giuseppe De Pietro b, Elio Marciano c, Ciro Niri d, Giovanna Sannino,b, Laura Verde e a Department of Otorhinolaryngology, University Hospital (Policlinico) Federico II of Naples, Via S.Pansini, 5 Naples, Italy b Institute of High Performance Computing and Networking (ICAR-CNR), Via Pietro Castellino, 111, Naples, Italy c Area of Audiology, Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Via S.Pansini, 5, Naples, Italy d Independent Doctor Surgeon Specialized in Audiology and Phoniatrics, Naples, Italy e Department of Engineering, University of Naples Parthenope, Centro Direzionale di Napoli, Isola C4, Naples, Italy

In the era of Edge-of-Things computing for the accomplishment of smart healthcare systems, the availability of accurate and reliable databases is important to provide the right tools for researchers and business companies to design, develop and test new techniques, methodologies and/or algorithms to monitor or detect the patient’s healthcare status. In this paper, the study and building of the VOice ICar fEDerico II (VOICED) database are presented, useful for anybody who needs voice signals in her/his research activities. It consists of 208 healthy and pathological voices collected during a clinical study performed following the guidelines of the medical SIFEL (Società Italiana di Foniatria e Logopedia) protocol and the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013 Statement. For each subject, the database contains a recording of the vowel /a/ of five seconds in length, lifestyle information, the medical diagnosis, and the results of two specific medical questionnaires

Patients Using an Online Forum for Reporting Progress When Engaging With a Six-Week Exercise Program for Knee Conditioning: Feasibility Study

Background: The use of electronic health (eHealth) and Web-based resources for patients with knee pain is expanding. Padlet is an online noticeboard that can facilitate patient interaction by posting virtual “sticky notes.” Objective: The primary aim of this study was to determine feasibility of patients in a 6-week knee exercise program using Padlet as an online forum for self-reporting on outcome progression. Methods: Undergraduate manual therapy students were recruited as part of a 6-week study into knee conditioning. Participants were encouraged to post maximum effort readings from quadriceps and gluteal home exercises captured from standard bathroom scales on a bespoke Padlet. Experience and progression reporting were encouraged. Posted data were analyzed for association between engagement, entry frequency, and participant characteristics. Individual data facilitated single-subject, multiple-baseline analysis using statistical process control. Experiential narrative was analyzed thematically. Results: Nineteen participants were recruited (47%, 9/19 female); ages ranged from 19 to 53 years. Twelve individuals (63%) opted to engage with the forum (range 4-40 entries), with five (42%) reporting across all 6 weeks. Gender did not influence reporting (odds ratio [OR] 0.76, 95% CI 0.06-6.93). No significant difference manifested between body mass index and engagement P=.46); age and entry frequency did not correlate (R2=.054, 95% CI –0.42 to 0.51, P=.83). Statistically significant conditioning profiles arose in single participants. Themes of pain, mitigation, and response were inducted from the experiences posted. Conclusions: Patients will engage with an online forum for reporting progress when undertaking exercise programs. In contrast to related literature, no significant association was found with reporting and gender, age, or body mass index. Individual posted data allowed multiple-baseline analysis and experiential induction from participants. Conditioning responses were evident on visual inspection. The importance of individualized visual data to patients and the role of forums in monitoring patients’ progress in symptomatic knee pain populations need further consideration

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Comparative study of healthcare messaging standards for interoperability in ehealth systems

Advances in the information and communication technology have created the field of "health informatics," which amalgamates healthcare, information technology and business. The use of information systems in healthcare organisations dates back to 1960s, however the use of technology for healthcare records, referred to as Electronic Medical Records (EMR), management has surged since 1990’s (Net-Health, 2017) due to advancements the internet and web technologies. Electronic Medical Records (EMR) and sometimes referred to as Personal Health Record (PHR) contains the patient’s medical history, allergy information, immunisation status, medication, radiology images and other medically related billing information that is relevant. There are a number of benefits for healthcare industry when sharing these data recorded in EMR and PHR systems between medical institutions (AbuKhousa et al., 2012). These benefits include convenience for patients and clinicians, cost-effective healthcare solutions, high quality of care, resolving the resource shortage and collecting a large volume of data for research and educational needs. My Health Record (MyHR) is a major project funded by the Australian government, which aims to have all data relating to health of the Australian population stored in digital format, allowing clinicians to have access to patient data at the point of care. Prior to 2015, MyHR was known as Personally Controlled Electronic Health Record (PCEHR). Though the Australian government took consistent initiatives there is a significant delay (Pearce and Haikerwal, 2010) in implementing eHealth projects and related services. While this delay is caused by many factors, interoperability is identified as the main problem (Benson and Grieve, 2016c) which is resisting this project delivery. To discover the current interoperability challenges in the Australian healthcare industry, this comparative study is conducted on Health Level 7 (HL7) messaging models such as HL7 V2, V3 and FHIR (Fast Healthcare Interoperability Resources). In this study, interoperability, security and privacy are main elements compared. In addition, a case study conducted in the NSW Hospitals to understand the popularity in usage of health messaging standards was utilised to understand the extent of use of messaging standards in healthcare sector. Predominantly, the project used the comparative study method on different HL7 (Health Level Seven) messages and derived the right messaging standard which is suitable to cover the interoperability, security and privacy requirements of electronic health record. The issues related to practical implementations, change over and training requirements for healthcare professionals are also discussed

Multi-dimensional Well-being Recognition System Using Daily Activity Data for Sustainable Living

芝浦工業大学2019年

Exploring Innovative Rehabilitation for the Knee using Ehealth, Biofeedback and Online Communities

Knee pain is regarded as an inevitable outcome in an ageing population and subsequent management, treatment and rehabilitation may exacerbate demand on stretched health services globally. Knee pain can be influenced by a number of factors; gender, body mass, activity profile, arthrokinematics, patient biopsychosociology and predisposing injury or trauma. Treatment options are typically viewed as pharmacological and non-pharmacological. Exercise and physical therapy are key elements within the latter option, alongside surgical procedures. Knee pain sufferers may vindicate their condition through clinical diagnosis and shift of locus of control; compliance to exercise interventions can depend on the scope of this shift. Such values should be acknowledged when monitoring individualised progression in the management of knee pain. Technology may have a role to play in capturing and influencing compliance within the scope of knee rehabilitation. The main aim of this thesis was to explore the use of innovative rehabilitation interventions for the knee that integrated eHealth, biofeedback and online communities. As this constitutes a complex scenario, this thesis has been reported using elements of the Medical Research Council (MRC) framework for the development and evaluation of complex interventions to improve health (Blackwood et al., 2010; Craig et al., 2008); notably the Preclinical (theory) stage, the Phase I (modelling) stage, and Phase II (exploratory trial). The findings further inform the options for rehabilitation around knee pain, encompassing latest generation techniques for addressing progressive joint disease and eHealth initiatives. These also included options for self-management and reporting that could be generalised to knee pain sufferers; an approach informed by the exploration of the reported experiences of individuals engaging with an online health community for knee pain. The eHealth component of the thesis looked to explore the use of simple Web 2.0 solutions and readily available domiciliary equipment for efficacy and accessibility