Wearable and Autonomous Biomedical Devices and Systems for Smart Environment

This chapter discusses the design and integration of fall and mobility sensor platforms for mobile and remote health signs monitoring. With a steadily increasing elderly population in Europe [1] and indeed throughout significant parts of the rest of the world, health services for elderly people are placing a growing strain on national health budgets [2] and the availability of nursing and care taker staff. Additionally, perhaps due to the advent of technology in general and changing social relations in society, there is an ever-increasing wish amongst our elderly citizens to live independently and be mobile for as long as possible. Recent advances in telecommunications, medical devices and technology in the home environment have enabled elderly people to live independently for longer than ever before. However, integrated systems targeting the monitoring of these elders’ health in the home environment are at best scarce.

The rapid diffusion recently experienced by minimally invasive therapies (MIT) is currently receiving a further significant boost towards modern medicine by the introduction of new nanotechnology-based techniques in the fields of medical imaging and localized therapeutic delivery. The innovative idea of “nanomedicine” is emerging, with its potential to revolutionize the entire disease management process, from diagnosis, through therapy, to serial follow-up, influencing the entire apparatus of medical devices. Nanoparticle contrast agents, in fact, can be targeted to specific cells and tissues of human body, allowing imaging of pathologic processes at a cellular scale. Moreover, nanoparticles are being increasingly involved in the development of new therapeutic approaches (e.g., site-targeted drug delivery, localized hyperthermia, optimized employment of laser and ultrasound power). This chapter reviewes recent nanotechnological applications in the field of non-ionizing cellular imaging and “personalized” therapies, with special focus on innovative strategies for selective cancer detection and treatment. Some very recent experimental results regarding automatic detection of innovative nanoparticle contrast agents on echographic images are also presented.

Many modern analytical instruments, such as mass spectrometry, have been developed to provide insight into the biochemical content of many different biological sample types. Typically these instruments are large bench-top machines that have very high sensitivity and specificity for the compounds they detect. However, these instruments are not mobile or autonomous, and they require highly trained personnel to operate. There have been many developments in the area of miniature chemical sensors that can maintain performance levels observed in large traditional bio-analytical instruments, but are low-power and potentially mobile and autonomous in function. Miniature differential mobility spectrometry (DMS) is a small instrument that can potentially be used in point-of-care diagnostic applications. This chapter will review the significant advances in this emerging research area, and provide insight as to how these systems could be further improved and adapted for use in autonomous health monitoring and sensing systems.

The chapter presents the development of a telemedical system. Works have been focused on algorithmic, applied hardware, software and transmission solutions. Modification of classical interrupter method, dedicated to the respiratory mechanics evaluation, has been postulated as a starting point for the whole project. It assumes exploitation of post-occlusional transients states in indirect measurement of physiological properties by solution of the inverse problem, where pressure and flow signals are pre-processed and then fitted to the outputs of reduced model in the time and/or frequency domain. The system consists of a base unit managing users and other devices as well as data transmission, processing, storage and presentation. It co-operates with home-based patient units capable of performing occlusional manoeuvres, tests. All the elements communicate via the Internet, wire or mobile telephony.

Several kinds of energy are available in the environment such as sunlight power, thermal gradients, wind, rain, tides, acoustic, and mechanical vibrations. This energy can be exploited to power electronic devices by means of suitable conversion mechanisms. Specifically, in the case of wearable device the need for onsite energy production emerges for the sake of both battery recharge and powering of sensors and electronics.

In this chapter a review of power harvesting methodology is presented along with two examples of devices implementing advanced energy harvesting.

In low risk pregnancies, the continuous monitoring of the foetal health is based on traditional protocols for counting the foetal movements felt by the mother. Although the maternal perception is a relevant characteristic for the evaluation of the foetal health, this kind of monitoring is hard to accomplish and being subjective can induce into errors due to mother’s anxiety and lack of concentration. Furthermore, the majority of foetal fatalities occur during the last weeks of low risk pregnancies. Therefore, it is important to obtain a universal electronic obstetric tracing, allowing for the identification of sudden changes in the foetus health, by continuously monitoring the foetus movements. The Smart-Clothing project aim has been the development of easy-to-wear belts with a telemedicine system for this purpose. One of the tried solutions is the Flex sensor belt system, which guarantees real-time and continuous foetal monitoring while creating effective interfaces for querying sensor data and store all the medical record (which can later be accessed by health professionals). Another developed belt has piezoelectric sensors incorporated onto it. The piezoelectric sensor belt has shown a high capacity to detect foetal movements, isolating them from external interferences.

A new research methodology named Brain Computer Interface (BCI) studies novel human-computer interactions; by means of BCI electronics devices, paralyzed patients are able to interact with the environment using no muscular contractions. This technique provides an external electronics support to all persons with severe motor disabilities, by acquiring in continuous mode the electroencephalogram (EEG) signals and operating some processing to control a computer or other domotics devices. Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention either on codified movements or on a letter or icon on a digital keyboard. The use of a customized and optimized spatial filtering technique embedded in the BCI system, based on the detection of the Electroencephalographic activity, improves the accuracy of BCI system itself, thanks to the explicit separation of the signal activity of interest from artefact signals. In this chapter, after an overview of the state-of-the-art research on BCI systems, the spatial filtering problem in EEG signals acquisition will be illustrated. In particular, a spatial filtering algorithm, known as ICA (Independent Component Analysis) and its application will be discussed. Finally, the design and implementation of an embedded system for EEG signals acquisition and real-time processing for BCI applications will be presented. The system is based onto a very performing and reconfigurable hardware platform. Moreover ICA algorithm has been implemented for noise reduction and artifacts removal.

Whole Body Vibration (WBV) is caused by vibration transmitted to human body through the seat or the feet. Typical causes are motor vehicles and machines. So high levels of whole-body vibration affect people who drive vehicles over rough surfaces as part of their job, for example off-road vehicles such as tractors, excavators, pallet-trucks and dumper trucks. Exposure to high levels of vibration can risk the health and safety of the worker. Typically vibration transmitted to body may be cause of back injuries and may aggravate pre-existing pathologies affecting the lumbar spine. The risks are greatest when the vibration magnitudes are high, or if the exposure time is long. Moreover frequent and regular exposure to severe shocks or jolts can increase the resultant effects. So syndromes and diseases affect backbone and cardio-vascular system. The European Directive 2002/44/EC deals with the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents. The “Vibration Directive” sets minimum standards for reducing the risks from whole-body vibration, so that daily exposure limits are fixed.

The present paper proposes a wearable measurement system for the whole-body vibration risk assessment. The system is a portable device based on a Pocket PC, a DAQ card and a set of accelerometers. It is able to estimate and to process the vibration exposure levels according to the guidelines of ISO Standards. The purpose is to check the exposure of workers to physical agents and to prevent risks during the use of driving vehicles.

The present Chapter intends to provide a practical guide for designers in planning smart measurement systems to be used in critical applications like medical ones. The authors propose an original approach to the design of measurement instrumentation with high performances. The procedure allows the designer to characterize the best measurement uncertainty that the system must have. The main purpose is to project a system with appropriate performances, so that suitable accuracy and reliability levels are guaranteed for the resulting measurements. The used approach starts from the consideration that typically the measured data are used during the processing stage in order to make decisions. In example, medical diagnoses are based on measurements which are put in comparison with reference limits. Therefore the measurement uncertainty can affect the reliability of the final results so to be source of mistaken decisions. Consequently high values of measurement uncertainty may be cause of unreliable data and inaccurate diagnoses. In the Chapter, a statistical model is used in order to characterize the functional relationship between the measurement uncertainty and the probability to make mistaken decisions because of the same uncertainty. Consequently the designer can characterize the best uncertainty value for the measurement system to be projected. So suitable reliability can be guaranteed during the decision-making stage by assuring a tolerable probability of mistaken decision. Furthermore the Chapter describes the architecture used in order to design smart and patient-adaptive biomedical systems. In detail the use of specific memory devices is shown. Information concerning the metrological characteristics of system and the patient data are so made available. In detail, information on measurement uncertainty and calibration curve is stored in a first memory device in order to estimate the reliability of measurement results. Whereas a further writable and readable storage device stores private and medical data of the specific examined subject. Such memory is a personal data-logger replaced for each patient and updated with the passing of time according to the current clinical conditions of the subject. In this way the computing algorithm fits the patient by means of the available information so to qualify the final diagnosis. In fact the available data allow the system to adapt and configure itself according to the patient features and to his health state so to get fault-tolerant diagnoses. In this way it is possible to project a biomedical system which is updateable and configurable according to the specific subject. Experimental results concerning the project of an ECG measurement system are added.

In order to motivate the activity of patients in rehabilitation training, bimanual limb training has been applied widely in rehabilitation systems. Based on this point, we propose a self-controlled master-slave robot for upper limb rehabilitation. The system contains two identical motors with a wired-connection. Under the action of the external forces from the two limbs that attached to the master and slave sides, the motor exerted with a larger force works in generating state and acts as the master, while the other motor works in electro-motion state and acts as the slave. A certain amount of compensated energy, together with the recycled energy from the master, enables the slave to reproduce the master’s movements accurately. The system realizes bilateral force sensing and supports different operating modes. Subjects coordinate the force of the two limbs based on visual feedback, further, controlling the handles in the two terminals to accomplish the predefined motion. Preliminary tests on different operating modes were conducted. The results confirm that the motion of the slave is precisely consistent with that of the master, and verify that the subjects can learn how to accomplish movements by practice.

The last decade has witnessed a rapid increase of interest in new sensing and monitoring devices including wearable wireless devices and sensor networks for several personal applications such as healthcare, well being & lifestyle, protection and safety. Smart Wearable Systems (SWS) are sensor-based integrated systems on body-worn platforms offering pervasive personalized solutions for continuous, non-invasive monitoring of body and external parameters, including feedback to the user. Several wearable solutions based on perimetric fixing using the body segments and the circular body part (e.g. head, arm, wrist and leg) are available today either in R&D prototype (the majority) or commercial products. Furthermore, new developments emerging from the miniaturization of electronics and materials processing have being leading to the integration of multiple smart functions into textiles without being a burden. The paper presents and discusses the main issues involved in the development of the area i.e. user requirements, technologies, research and development of integrated systems as well as future challenges to be met in order to reach a market with reliable and high value-added products.

Triaxial accelerometers are used as a low cost solution in wide areas of patient care. This paper describes the use of triaxial accelerometer together with ZigBee transceiver to detect fall of patients. The system, including calibration of accelerometers and measurement is explained in detail.

Demographic developments, social changes, and the rising costs of health and social care due to people with chronic disease, people with mobility limitations and elderly population make necessary to rethink care delivery. A practical way to improve care and cut healthcare costs is to develop integrated electronic health (e-health) solutions that permit monitoring of physiological parameters and motor activities of the users in their homes.

The unobtrusiveness and non-invasiveness of biomedical measuring devices are key factors on acceptance and satisfaction from the subjects in e-health context. This is justified taking into account that through unobtrusive and non-invasive measurements the data on user’s health status may be obtained with or without interactions between subject and biomedical monitoring system.

Unobtrusive cardiac and respiratory activity monitoring remains a challenging task. This chapter is dedicated to a review of unobtrusive biomedical sensing solutions with higher capability in integration on ubiquitous healthcare systems. Elements of signal processing associated with health status measuring channels are included in this chapter.

The paper presents a method of reliability estimation of scores obtained in subjective quality evaluation, which was inspired by procedures used in between-laboratory tests for determination of repeatability and reproducibility of measurement methods. The proposed use of Madel’s k and h statistics enables for significant decrease of MOS standard deviation, which is exemplified by SSCQE of compressed video results.

Assistive devices are a key aspect in wearable systems for biomedical applications, as they represent potential aids for people with physical and sensory disabilities that might lead to improvements in the quality of life. This chapter focuses on wearable assistive devices for the blind. It intends to review the most significant work done in this area, to present the latest approaches for assisting this population and to understand universal design concepts for the development of wearable assistive devices and systems for the blind.

Biotelemetry is remote monitoring, measuring and recording of a living organism’s function, activity or condition. Network of sensor nodes placed on or implanted inside the body of a subject is called Body Area Network (BAN). In this work we will describe the principles of a wireless body area network which uses the human body as a transmission medium, namely intrabody communication (IBC). We will describe the limitations set on the IBC systems, describe dielectric properties of the human body as a transmission medium, specify different ways of transmitting signals through the human body and compare characteristics of the IBC systems found in the literature.

The Heart Rate Variability (HRV) is based on the analysis of the R-peak to R-peak intervals (RR-intervals) of the ECG signal in the time and/or frequency domains. Doctors and psychologists are increasingly recognizing the importance of HRV; in fact, a number of studies have demonstrated that patients with anxiety, phobias and post-traumatic stress disorder consistently show lower HRV, even when not exposed to a trauma related prompt. Importantly, this relationship existed independently of age, gender, trait anxiety, cardio-respiratory fitness, heart rate, blood pressure and respiration rate. In this paper, we present a toolkit based on body sensor networks (BSN) for the time-domain HRV analysis, namely SPINE-HRV (Signal Processing In Node Environment-HRV). The SPINE-HRV is composed of a wearable heart activity monitoring system to continuously acquire the RR-intervals, and a processing application developed using the SPINE framework. The developed system consists of a wireless chest band, a wireless wearable sensor node and a base station. The RR-intervals are processed using the SPINE framework at the base station side through a time-domain analysis of HRV. The analysis provides seven common parameters known in medical literature to help cardiologists in the diagnosis related to several heart diseases. In particular, SPINE-HRV is applied for stress detection of people during activities in their everyday life. Experimentations carried out by monitoring subjects in specific activities have shown the effectiveness of SPINE-HRV in detecting stress.

This paper outlines the initial ideas and results surrounding the development of an accurate hand movement measurement tool. This tool will assist medical clinicians, specifically rheumatologists and orthopeadic hand surgeons, with measuring the loss of movement in the human hand. This has many direct applications within medical practice including diagnosis, prognosis and recovery assessment of patients with conditions specific to the hand e.g. to measure how far a patient can close their fingers (with a flare up in arthritis patients may not be able to make a fist).

Current measurement techniques available to clinicians are either invasive (xrays) or rely heavily on manual evaluation such as vision and touch which are dependent on training and experience and results often vary between observers. Measuring tape is commonly used to measure distances e.g. between palm and fingertip which also leads to issues with accuracy, as well as patient self questionnaires which allow for interpretation.