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The system described in this paper is aimed at improving the clinical workflow of post-stroke patients under oral anticoagulant therapy (OAT). The system helps both physicians and patients during the periodic control visits necessary to assess the anticoagulation status and the next therapeutic plan. Controls represent a burden for both patients, which after blood drawing must wait for the result, and for physicians, that, after assessing the therapy plan, must communicate it to patients, face-to-face or by telephone. A system is proposed, which embeds an algorithm for the patient-tailored calculation of the drug dosage and scheduling, and an automatic telephone dialogue for the communication of the therapy plan, once it has been validated or adjusted by the physician.

Stroke is a serious neurological accident which accounts for a wide fraction of the healthcare costs in industrialized societies. Recovery from stroke and other neurological accidents usually include motor rehabilitation, maintained for several months, and logopedic training for the recovery of cognitive and speech abilities. The MyHeart consortium is addressing several aspects of cardiovascular diseases' management by combining clothes with embedded biomedical sensors and information technologies. One of the application areas is especially devoted to supporting Neurological Rehabilitation (NR). This article describes how MyHeart's Concept NR is structured and how technologies are leveraged to support both motor rehabilitation and speech/cognitive training. Information technology and garment-embedded sensors, combined, permit assisted training both at the clinic and at home, after discharge from the intensive care unit.

Conductive elastomer elements can be industrially embedded into garments to form unobtrusive strain sensing stripes. The present article outlines the structure of a strain-sensor based gesture detection algorithm. Current sensing prototypes include several dozens of sensors; their redundancy with respect to the limb's degrees of freedom, and other artifacts implied by this measurement technique, call for the development of novel robust multivariate pattern-matching techniques. The algorithm's construction is explained, and its performances are evaluated in the context of motor rehabilitation exercises for both two-class and multi-class tasks.

Stroke and other neurological accidents account for a wide fraction of the healthcare costs in industrialised societies. The last step in the chain of recovery from a neurological event often includes motor rehabilitation. While current motion-sensing technologies are inadequate for automated monitoring of rehabilitation exercises at home, conductive elastomers are a novel strain-sensing technology which can be embedded unobtrusively into a garment's fabric. A sensorized garment was realized to simultaneously measure the strains at multiple points of a shirt covering the thorax and upper limb. Supervised learning techniques were employed to analyse the strain measures in order to reconstruct upper-limb posture and provide real-time feedback on exercise progress.

The recent integration of telephony systems with information and communication technology (ICT) enables the development of innovative tools for telemedicine. The dissemination and widespread acceptance of telephone-based care monitoring systems challenge the researcher to deal with the cognitive factors involved in the patient-physician interaction, and the way they should be to shape up the technological solutions. This paper proposes a model that describes the impact of socio-cognitive factors in the complex process of health care management. The model has been used to design and develop a telephone system for the management of hypertensive patient within the EU funded Homey project. The knowledge existed in a widely accepted guideline for the care of hypertension has been represented and augmented through the proposed cognitive model. The final product is an intelligent system able to manage an adaptive dialogue. It monitors patients' adherence and increases their involvement by promoting self-care through frequent virtual visits, which is complementary to the traditional face-to-face encounters with their primary care physicians.

Recent advances in automatic speech recognition and related technologies allow computers to carry on conversations by telephone. We developed an intelligent dialog system that interacts with hypertensive patients to collect data about their health status. Patients thus avoid the inconvenience of going for frequent visits that monitor some clinical variables they can easily measure at home; the physician is facilitated in acquiring and reviewing patient information and related risk indicators, which are evaluated from the data according to noted guidelines. The system described here is a prototype of future configurable and component-based dialog systems, which may allow a new modality for users and physicians to access electronic health records.