Information and Communication Technologies for Ageing Well and e-Health

The reduction of public expenditure and investments in health care provisioning calls for new, sustainable models to transform the increasing aging population and dementia-related diseases incidence from global challenges into new opportunities. In this context, Information and Communication Technologies play a vital role, to both promote aging in place and home management of Patients with Dementia, and to provide new tools and solutions to facilitate the working conditions of the care staff in nursing homes, which remain an essential facility when cognitive-impaired patients cannot live at home anymore. Night staff in nursing homes are a vulnerable group, receiving less supervision and support than day staff, but with high levels of responsibility. Additionally, nighttime attendance of patients affected by dementia may be difficult, because of their incremented neuropsychiatric symptoms. This paper describes an integrated system for the night monitoring of patients with dementia in nursing homes, based on a product originally conceived for domestic use, but re-designed to provide support to nurses, by means of a set of sensors located in each patient’s room, and suitable software applications to detect dangerous events and raise automatic alerts delivered to the nurses through mobile devices. The results obtained from the first experimental installation of the monitoring system proved the effectiveness of the proposed solution to support nurses during the night supervision of patients, and suggested suitable modifications and additional features to increase the nurses’ compliance.

A framework that utilizes audio information for recognition of activities of daily living (ADLs) in the context of a health monitoring environment is presented in this chapter. We propose integrating a Raspberry PI single-board PC that is used both as an audio acquisition and analysis unit. So Raspberry PI captures audio samples from the attached microphone device and executes a set of real-time feature extraction and classification procedures, in order to provide continuous and online audio event recognition to the end user. Furthermore, a practical workflow is presented, that helps the technicians that setup the device to perform a fast, user-friendly and robust tuning and calibration procedure. As a result, the technician is capable of “training” the device without any need for prior knowledge of machine learning techniques. The proposed system has been evaluated against a particular scenario that is rather important in the context of any healthcare monitoring system for the elder: In particular, we have focused on the “bathroom scenario” according to which, a Raspberry PI device equipped with a single microphone is used to monitor bathroom activity on a 24/7 basis in a privacy-aware manner, since no audio data is stored or transmitted. The presented experimental results prove that the proposed framework can be successfully used for audio event recognition tasks.

This research study is conducted to assess Ehealth readiness within the Libyan national health services by carrying out research case studies of hospitals and clinics in both urban and rural areas of Libya. The outcome results will help constructing framework for Ehealth implementation in the Libyan National Health Service (LNHS). The research study assessed how prescription were prescribed, information communication technology (ICT) was used in recording healthcare information, patients were referred, how healthcare staff carry out their consultations, and how they were trained to use IT. This research study was carried out in Zawia, Misrata, Sirt, Benghazi, Tripoli and Sabha healthcare institutions and was focused upon both five urban and rural area and explored the readiness levels of the technical, political, healthcare and social factors that need to be examined when healthcare information systems are planned. Qualitative interviews and quantitative questionnaires were formulated for this research and used the Chan framework (2010) for their formulations. Data were managed using NVIVO for interviews and an SPSS statistical package for Questionnaires. The findings from this research indicated that there was no evidence of Ehealth technology in the LNHS found by the researcher and insufficient IT support and staff ICT training. These results from the rural and urban healthcare institutions place them on the Ehealth Maturity Curve at the interaction and presence stages (level zero). Thus it is essential for specific Ehealth frameworks to be created that are based around these findings for moving the Ehealth technology usage in these healthcare institutions from 0 to 2 in the E-Health Maturity Curve levels.

In this article, we present a new methodology for human motion identification based on motion dependent binary classifiers that afterwards fuse their decisions to identify an Activity of Daily Living (ADL). Temporal and spectral features extracted from the sensor signals (accelerometer and gyroscope) and concatenated to a single feature vector are used to train motion dependent binary classification models. Each individual model is capable to recognize one motion versus all the others. Afterwards the decisions are combined by a fusion function using as weights the sensitivity values derived from the evaluation of each motion dependent classifier on the provided training set. The proposed methodology was evaluated using SVMs for the motion dependent classifiers and is compared against the common multiclass classification approach optimized using either feature selection or subject dependent classification. The classification accuracy of the proposed methodology reaches 99% offering competitive performance comparing to the other approaches.

Public Clouds offer a convenient way for storing and sharing the large amounts of medical data that are generated by, for example, wearable health monitoring devices. Nevertheless, using a public infrastructure raises significant security and privacy concerns. Even if the data are stored in an encrypted form, the data owner should share some information with the Cloud provider in order to enable the latter to perform access control; given the high sensitivity of medical data, even such limited information may jeopardize end-user privacy. In this paper we employ an access control delegation scheme to enable the users themselves to perform access control on their data, even though these are stored in a public Cloud. In our scheme access control policies are evaluated by a user-controlled gateway and Cloud providers are only entrusted with respecting the gateway’s decision. Furthermore, since medical data must often be shared with health providers of the user’s choice, we rely on a proxy re-encryption technique to allow such sharing to take place. Our scheme encrypts data before storing them in the Cloud and applies proxy re-encryption using Cloud resources to encrypt data separately for each (authorized) user. Our proxy re-encryption scheme ensures that misbehaving Cloud providers cannot use re-encryption keys to share content with unauthorized clients, while delegating the costly re-encryption operations to the Cloud.

Electronic Health Records (EHRs) systems enable the construction of longitudinal collection of health information about individual patients, by integrating health data produced by the healthcare facilities. The advantags associated with the use of such systems are in terms of improvement of quality of care and cost reduction. An important barrier to the availability of exhaustive longitudinal collections of health data is represented by the lack of interoperability among EHR systems. In Italy, each region has been developing its own EHR systems according to the national guidelines and technical specifications compliant to the indications provided by a Italian Law issued in 2012 and updated in 2013. This paper describes the national technological framework designed from a National Technical Board for making interoperable the regional EHR systems each other, preserving the privacy of the patients. The framework, based on a System-of-Systems approach, enables healthcare professionals both to (i) consult health documents associated with a patient, even if they are produced in other regions, and (ii) register new health documents for patients assisted by other regions.

As one of the fastest spreading technologies and due to their rich sensing features, smartphones have become popular elements of modern human activity recognition systems. Besides activity recognition, smartphones have also been employed with success in fall detection/recognition systems, although a combined approach has not been evaluated yet. This article presents the results of a comprehensive evaluation of using a smartphone’s acceleration sensor for human activity and fall recognition, including 12 different types of activities of daily living (ADLs) and 4 different types of falls, recorded from 66 subjects in the context of creating “MobiAct”, a publicly available dataset for benchmarking and developing human activity and fall recognition systems. An optimized feature selection and classification scheme is proposed for each, a basic, i.e. recognition of 6 common ADLs only (99.9% accuracy), and a more complex human activity recognition task that includes all 12 ADLs and 4 falls (96.8% accuracy).

The paper analyzes current weaknesses of behavioral change support systems such as the lack of adequately taking into account the heterogeneity of target users. Based on this analysis the paper presents an application framework that comprises various components to accommodate user preferences and to adapt system interventions to individual users: a goal hierarchy which users can tailor to their needs, dividing nudges into different types that correspond to speech acts, rules for context-specific triggering of nudges. User adaptation is realized with approaches from user modeling and collaborative filtering. The result is a self-learning application that changes in line with a user’s progress, which is expected to enhance user acceptance and increase and sustain people’s motivation for behavioral change. The application framework will be evaluated by comparing a mobile health app using the framework with a simplified version of the app that does not support user tailoring and adaptation.