Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

Implementing and Evaluating a Wireless Body Sensor System for Automated Physiological Data Acquisition at Home

Advances in embedded devices and wireless sensor networks have resulted in new and inexpensive health care solutions. This paper describes the implementation and the evaluation of a wireless body sensor system that monitors human physiological data at home. Specifically, a waist-mounted triaxial accelerometer unit is used to record human movements. Sampled data are transmitted using an IEEE 802.15.4 wireless transceiver to a data logger unit. The wearable sensor unit is light, small, and consumes low energy, which allows for inexpensive and unobtrusive monitoring during normal daily activities at home. The acceleration measurement tests show that it is possible to classify different human motion through the acceleration reading. The 802.15.4 wireless signal quality is also tested in typical home scenarios. Measurement results show that even with interference from nearby IEEE 802.11 signals and microwave ovens, the data delivery performance is satisfactory and can be improved by selecting an appropriate channel. Moreover, we found that the wireless signal can be attenuated by housing materials, home appliances, and even plants. Therefore, the deployment of wireless body sensor systems at home needs to take all these factors into consideration.Comment: 15 page

Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing

In this paper, a first approach to the design of a portable device for non-contact monitoring of respiratory rate by capacitive sensing is presented. The sensing system is integrated into a smart vest for an untethered, low-cost and comfortable breathing monitoring of Chronic Obstructive Pulmonary Disease (COPD) patients during the rest period between respiratory rehabilitation exercises at home. To provide an extensible solution to the remote monitoring using this sensor and other devices, the design and preliminary development of an e-Health platform based on the Internet of Medical Things (IoMT) paradigm is also presented. In order to validate the proposed solution, two quasi-experimental studies have been developed, comparing the estimations with respect to the golden standard. In a first study with healthy subjects, the mean value of the respiratory rate error, the standard deviation of the error and the correlation coefficient were 0.01 breaths per minute (bpm), 0.97 bpm and 0.995 (p < 0.00001), respectively. In a second study with COPD patients, the values were -0.14 bpm, 0.28 bpm and 0.9988 (p < 0.0000001), respectively. The results for the rest period show the technical and functional feasibility of the prototype and serve as a preliminary validation of the device for respiratory rate monitoring of patients with COPD.Ministerio de Ciencia e Innovación PI15/00306Ministerio de Ciencia e Innovación DTS15/00195Junta de Andalucía PI-0010-2013Junta de Andalucía PI-0041-2014Junta de Andalucía PIN-0394-201

Mobile platform-independent solutions for body sensor network interface

Body Sensor Networks (BSN) appeared as an application of Wireless Sensor Network (WSN) to medicine and biofeedback. Such networks feature smart sensors (biosensors) that capture bio-physiological parameters from people and can offer an easy way for data collection. A new BSN platform called Sensing Health with Intelligence Modularity, Mobility and Experimental Reusability (SHIMMER) presents an excellent opportunity to put the concept into practice, with suitable size and weight, while also supporting wireless communication via Bluetooth and IEEE 802.15.4 standards. BSNs also need suitable interfaces for data processing, presentation, and storage for latter retrieval, as a result one can use Bluetooth technology to communicate with several more powerful and Graphical User Interface (GUI)-enabled devices such as mobile phones or regular computers. Taking into account that people currently use mobile and smart phones, it offers a good opportunity to propose a suitable mobile system for BSN SHIMMER-based networks. This dissertation proposes a mobile system solution with different versions created to the four major smart phone platforms: Symbian, Windows Mobile, iPhone, and Android. Taking into account that, currently, iPhone does not support Java, and Java cannot match a native solution in terms of performance in other platforms such as Android or Symbian, a native approach with similar functionality must be followed. Then, four mobile applications were created, evaluated and validated, and they are ready for use

An IoT based Virtual Coaching System (VSC) for Assisting Activities of Daily Life

Nowadays aging of the population is becoming one of the main concerns of theworld. It is estimated that the number of people aged over 65 will increase from 461million to 2 billion in 2050. This substantial increment in the elderly population willhave significant consequences in the social and health care system. Therefore, in thecontext of Ambient Intelligence (AmI), the Ambient Assisted Living (AAL) has beenemerging as a new research area to address problems related to the aging of the population. AAL technologies based on embedded devices have demonstrated to be effectivein alleviating the social- and health-care issues related to the continuous growing of theaverage age of the population. Many smart applications, devices and systems have beendeveloped to monitor the health status of elderly, substitute them in the accomplishment of activities of the daily life (especially in presence of some impairment or disability),alert their caregivers in case of necessity and help them in recognizing risky situations.Such assistive technologies basically rely on the communication and interaction be-tween body sensors, smart environments and smart devices. However, in such contextless effort has been spent in designing smart solutions for empowering and supportingthe self-efficacy of people with neurodegenerative diseases and elderly in general. Thisthesis fills in the gap by presenting a low-cost, non intrusive, and ubiquitous VirtualCoaching System (VCS) to support people in the acquisition of new behaviors (e.g.,taking pills, drinking water, finding the right key, avoiding motor blocks) necessary tocope with needs derived from a change in their health status and a degradation of theircognitive capabilities as they age. VCS is based on the concept of extended mind intro-duced by Clark and Chalmers in 1998. They proposed the idea that objects within theenvironment function as a part of the mind. In my revisiting of the concept of extendedmind, the VCS is composed of a set of smart objects that exploit the Internet of Things(IoT) technology and machine learning-based algorithms, in order to identify the needsof the users and react accordingly. In particular, the system exploits smart tags to trans-form objects commonly used by people (e.g., pillbox, bottle of water, keys) into smartobjects, it monitors their usage according to their needs, and it incrementally guidesthem in the acquisition of new behaviors related to their needs. To implement VCS, thisthesis explores different research directions and challenges. First of all, it addresses thedefinition of a ubiquitous, non-invasive and low-cost indoor monitoring architecture byexploiting the IoT paradigm. Secondly, it deals with the necessity of developing solu-tions for implementing coaching actions and consequently monitoring human activitiesby analyzing the interaction between people and smart objects. Finally, it focuses on the design of low-cost localization systems for indoor environment, since knowing theposition of a person provides VCS with essential information to acquire information onperformed activities and to prevent risky situations. In the end, the outcomes of theseresearch directions have been integrated into a healthcare application scenario to imple-ment a wearable system that prevents freezing of gait in people affected by Parkinson\u2019sDisease