No abstract

This paper presents the hardware and software design and implementation of a low-cost, wearable, and unobstructive intelligent accelerometer sensor for the monitoring of human physical activities. In order to promote healthy lifestyles to elders for an active, independent, and healthy ageing, as well as for the early detection of psychomotor abnormalities, the activity monitoring is performed in a holistic manner in the same device through different approaches: 1) a classification of the level of activity that allows to establish patterns of behavior; 2) a daily activity living classifier that is able to distinguish activities such as climbing or descending stairs using a simple method to decouple the gravitational acceleration components of the motion components; and 3) an estimation of metabolic expenditure independent of the activity performed and the anthropometric characteristics of the user. Experimental results have demonstrated the feasibility of the prototype and the proposed algorithms.

No abstract

This work presents the design, development and implementation of a smart sensor to monitor the respiratory rate. This sensor is aimed at overcoming the drawbacks of other systems currently available in market, namely, devices that are costly, uncomfortable, difficult-to-install, provide low detection sensitivity, and little-to-null patient-to-patient calibration. The device is based on capacitive sensing by means of an LC oscillator. Experimental results show that the sensor meets the necessary requirements, making feasible the proposed monitoring system with the technology used.

In this paper, the main results related to a fall detection system are shown by means of a personal server for the control and processing of the data acquired from multiple intelligent biomedical sensors. This server is designed in the context of a telehealthcare system for the elderly, to whom falls represent a high-risk cause of serious injuries, and its architecture can be extended to patients suffering from chronic diseases. The main design issues and developments in terms of the server hardware and software are presented with the aim of providing a real-time analysis of the processed biosignals. As a result, the evaluation study of the implemented algorithm for fall detection through a set of laboratory experiments is presented, together with some important issues in terms of the device's consumption. The proposed algorithm exhibits excellent outcomes in fall detection.

Although the roles of body sensor networks (BSNs) are similar to those carried out by the generic wireless sensor networks (WSNs), new solutions must be established to optimize communications for true pervasive biomedical monitoring transparent to the user. In this paper, a proposal of a hardware and software platform for biomedical sensors is performed, which is specially designed to minimize energy consumption in BSNs through a modular processing scheme based on the detection of events and information abstraction. The data flow is implemented through a novel communications protocol that enhances the performances of consumption and time delay of the platform. A novel aspect of the protocol is the explicit incorporation of an additional level of communications to support the distributed processing architecture that allows the execution of multiple applications in parallel within the smart sensors. The results obtained with an implementation of a smart sensor for fall detection demonstrate its feasibility as well as the viability of the communication protocol for the development of energy-efficient BSNs.

The main objective of this paper is to present a distributed processing architecture that explicitly integrates capabilities for its continuous adaptation to the medium, the context, and the user. This architecture is applied to a falling detection system through: 1) an optimization module that finds the optimal operation parameters for the detection algorithms of the system devices; 2) a distributed processing architecture that provides capabilities for remote firmware update of the smart sensors. The smart sensor also provides an estimation of activities of daily living (ADL), which results very useful in monitoring of the elderly and patients with chronic diseases. The developed experiments have demonstrated the feasibility of the system and specifically, the accuracy of the proposed algorithms and procedures (100% success for impact detection, 100% sensitivity and 95.68% specificity rates for fall detection, and 100% success for ADL level classification). Although the experiments have been developed with a cohort of young volunteers, the personalization and adaption mechanisms of the proposed architecture related to the concepts of "design for all" and "design space" will significantly ease the adaptation of the system for its application to the elderly.

In this paper, the most important challenges and trends related to the application of Ambient Assisted Living (AAL) methods and techniques to the social/healthcare context are discussed. In order to find out technical solutions to these challenges, the main methodological issues concerning the design of open and distributed architectures are analyzed. The objective is to improve the efficiency/cost ratio in the provision of social and healthcare services to citizens with special needs, through the application of new paradigms in the context of AAL environments. Finally, some results and conclusions regarding the proposed open architecture are illustrated for the case of a distributed biomedical sensor network designed by the authors following this methodology.