1,721,008 research outputs found
Health@Home: lesson learnt and future perspective in the monitoring of patients affected by chronic heart failure
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Assistive technologies developed at the Department of Information Engineering of Pisa University
No full textePhone - a Technical Aid System to Ease Accessibility to Android Smartphones for Motor Skill Impaired Users
No full textA mobility aid system for visually impaired people on the historical walls of Lucca city, Tuscany, Italy
No full textThe Research Project 'Walls for All' ('Le Mura per Tutti') aims at the realization on the historical walls of Lucca city, Tuscany, Italy, of a mobility aid system for visually impaired people able to provide guidance and context information on a predefined outdoor safe path, thanks to a traditional white cane equipped with custom electronics (Smart Cane) and a smartphone featuring an ad-hoc developed Android application. The chosen scenario is allowing living of an outdoor public place of a relevant tourist/cultural interest. The Smart Cane allows the user to follow the path thanks to a tactile vibration provided by the handle when the smart cane tip is over the predefined path, which is implemented by means of buried cables in which a modulated current flows. By means of a small joystick on the cane handle, the user may query the smartphone application and be provided with vocal guidance information on the route and information on the surrounding points of interest (e.g. historical buildings, benches, rest points, etc.), according to the user's current position derived from the smartphone internal GPS receiver and from the path detection data provided by the Smart Cane through a Bluetooth connection. The system path infrastructure is currently being installed on a route longer than 1 km on the walls of Lucca city for the final testing and setup phase
Self electrocardiogram acquisition device
No full textAn electrocardiographic device (40) comprises a central portion (31) and two side portions (33) that have respective conductive surfaces (1,3) configured as acquisition electrodes for receiving a raw electrocardiographic signal from a patient's (99) respective hands (98); an acquisition unit (25) electrically connected to the conductive surfaces (1,3), so as to receive the raw signal and configured for filtering it within a predetermined passband to obtain a filtered signal; a digitalization unit (18) connected to the acquisition unit (25), so as to receive the filtered signal, and configured for sampling it to obtain a digital signalized; a data interface unit (22) electrically connected to the digitalization unit (18), so as to receive the digital signalized, and configured for providing it so as it can be drawn; wherein the side portions (33) have a base surface (36) that is opposite to the conductive surfaces (1,3) configured to be arranged on a plane; a height (D) between a base surface (36) and the conductive surfaces (1,3) set between 1 cm and 8 cm, the side portions having a front edge (37) to be arranged distally with respect to the patient (99) and in such a way that the distance between the front edge (37) and the conductive surfaces (1,3) is shorter than a predetermined value (L), so that the patient (99), arranging the bases (36) on the plane (28), can cause the palm (97) of his own respective hands (98) to bear on the side portions (33), maintaining the hand palm (97) in contact with the conductive surfaces (1,3) and at the same time assisting the fingers (98") of the hands (98) to possibly rest on the plane (28). This way, by causing the hand palm to bear on the raised side portions allows to discharge a large part of the weight of the arm on the conductive surfaces substantially without any muscle tension and without any movement, which would affect the contact surface during the acquisition, and substantially without any noise associated thereto, so that a filtering passband can be chosen that has a lower limit value lower than in the prior art devices, which allows preserving a large part of the information content of the electrocardiographic signal
A flexible home monitoring platform for patients affected by chronic heart failure directly integrated with the remote Hospital Information System
No full textToday Chronic Heart Failure (CHF) represents one of leading cause of hospitalization among chronic disease, especially for elderly citizens, with a consequent considerable impact on patient quality of life, resources congestion and healthcare costs for the National Sanitary System. The current healthcare model is mostly in-hospital based and consists of periodic visits, but unfortunately it does not allow to promptly detect exacerbations resulting in a large number of re-hospitalization. Recently physicians and administrators identify telemonitoring systems as a strategy able to provide effective and cost efficient healthcare services for CHF patients, ensuring early diagnosis and treatments in case of necessity. This work presents a complete and integrated ICT solution to improve the management of chronic heart failure through the remote monitoring of vital signs at patient home, able to connect in-hospital care of acute syndrome with out-of-hospital follow-up. The proposed platform represents the patient's interface, acting as link between biomedical sensors and the data collection point at the Hospital Information System (HIS) in order to handle in transparent way the reception, analysis and forwarding of the main physiological parameters
An embedded sensing and communication platform, and a healthcare model for remote monitoring of chronic diseases
Full text linkThis paper presents a new remote healthcare model, which, exploiting wireless biomedical sensors, an embedded local unit (gateway) for sensor data acquisition-processing-communication, and a remote e-Health service center, can be scaled in different telemedicine scenarios.The aim is avoiding hospitalization cost and long waiting lists for patients affected by chronic illness who need continuous and long-term monitoring of some vital parameters.In the “1:1” scenario, the patient has a set of biomedical sensors and a gateway to exchange data and healthcare protocols with the remote service center.In the “1:N” scenario the use of gateway and sensors is managed by a professional caregiver, e.g., assigned by the Public Health System to a number N of different patients.In the “point of care” scenario the patient, instead of being hospitalized, can take the needed measurements at a specific health corner, which is then connected to the remote e-Health center.A mix of commercially available sensors and new custom-designed ones is presented.The new custom-designed sensors range from a single-lead electrocardiograph for easy measurements taken by the patients at their home, to a multi-channel biomedical integrated circuit for acquisition of multi-channel bio signals, to a new motion sensor for patient posture estimation and fall detection.Experimental trials in real-world telemedicine applications assess the proposed system in terms of easy usability from patients, specialist and family doctors, and caregivers, in terms of scalability in different scenarios, and in terms of suitability for implementation of needed care plans
ECG-Based Stress Detection and Productivity Factors Monitoring: The Real-Time Production Factory System
Full text linkProductivity and production quality have become primary goals for the success of companies in all industrial and manufacturing sectors. Performance in terms of productivity is influenced by several factors including machinery efficiency, work environment and safety conditions, production processes organization, and aspects related to workers’ behavior (human factors). In particular, work-related stress is among the human factors that are most impactful and difficult to capture. Thus, optimizing productivity and quality in an effective way requires considering all these factors simultaneously. The proposed system aims to detect workers’ stress and fatigue in real time using wearable sensors and machine learning techniques and also integrate all data regarding the monitoring of production processes and the work environment into a single platform. This allows comprehensive multidimensional data analysis and correlation research, enabling organizations to improve productivity through appropriate work environments and sustainable processes for workers. The on-field trial demonstrated the technical and operational feasibility of the system, its high degree of usability, and the ability to detect stress from ECG signals exploiting a 1D Convolutional Neural Network (accuracy 88.4%, F1-score 0.90)
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