Interest in Information and Communication Technologies for human monitoring, smart health and assisted living is growing due to the significant impact that these technologies are expected to have on improving the quality of life of ageing populations around the world. This book brings together chapters written by a range of researchers working in these topics, providing an overview of the areas and covering current research, developments and applications for a readership of researchers and research-led engineering practitioners. It discusses the promises and the possible advantages of these technologies, and also indicates the challenges for the future. Topics covered include: personal monitoring and health data acquisition in smart homes; contactless monitoring of respiratory activity; technology-based assistance of people with dementia; wearable sensors for gesture analysis; design and prototyping of home automation systems for the monitoring of elderly people; multi-sensor platform for circadian rhythm analysis; smart multi-sensor solutions for activity detection; human monitoring based on heterogeneous sensor networks; mobile health for vital signs and gait monitoring systems; and smartphone-based blood pressure monitoring for falls risk assessment.
Inspec keywords: assisted living; patient monitoring; gait analysis; data acquisition; medical signal processing; sensor fusion; pneumodynamics; blood pressure measurement; circadian rhythms
Other keywords: human monitoring; prototype realization; smart homes; heterogeneous sensor network; dementia; health data acquisition; contactless monitoring; feasibility study; respiratory activity; smart health living; assisted living; personal monitoring; ADL detection; elderly people; technology-based assistance; ambient intelligence; gait monitoring systems; smart healthcare applications; circadian rhythm analysis; electromagnetic waves; mHealth environments; Smartphone-based blood pressure monitoring; gesture analysis; wearable sensors; home automation system; fall risk assessment; vital signs
Subjects: Biomedical measurement and imaging; Physics of body movements; Signal processing and detection; Biomedical engineering; Biology and medical computing; Data acquisition systems; Sensor fusion; Digital signal processing; Haemodynamics, pneumodynamics
The use of ambient assisted living technology, namely technology to improve the quality of life of people at home, is becoming a common trait of modern society. This technology, however, is difficult to be completely defined and classified, since it addresses many different human needs ranging from the physiological sphere to the psychological and social ones. In this chapter we focus on personal monitoring and data acquisition in smart homes, and propose the results of our research activities in the form of the description of three functional prototypes, each one addressing a specific need: an environmental monitoring system to measure the respiratory rate, a domotic architecture for both comfort assessment and user indoor localization, and a device for supporting mobility indoors. Each prototype description is followed by an experimental analysis and, finally, by considerations suggesting possible future developments in the very near future.
Respiratory rate is a vital parameter of primary importance in medicine, sport/fitness and wellness in general, especially for most vulnerable categories of people like children and elderly people. Contactless determination of breathing activity provides a powerful and essential mean for evaluating this parameter in subjects who cannot accommodate physical sensors on their bodies. In hospital such subjects may be intensive care patients, prematurely born children and hosts of burn units. Moreover, also for long-term measurements of healthy people, for example, an elder living in home alone or in a care centre, invasive systems prove to be uncomfortable and annoying. Even for a night-time diagnosis of respiratory sleep disorders, like apnoea and hypopnoea, they demonstrate to interfere with the sleep regularity. Therefore, in the last decades many electronic devices have been conceived and realized to detect such an important parameter along with different branches of physics: strain gauges, ultrasounds, optics, thermometry, etc. This chapter presents the theoretical studies, the design and realization of a standalone Electromagnetic (EM) system for contactless determination of breathing frequency and subject's activity. Two major EM solutions are already known in the literature, continuous wave (CW) systems, and ultra-wideband (UWB) systems. The first evaluates the Doppler effect caused by the chest displacement during breathing at a single frequency, and the other one is a radar that detects the body motion by measuring the time shifts of sequential pulses.An intermediate solution thatjoins the advantages ofboth and overcomes their drawbacks is proposed. Through the use of a frequency sweep, in fact, it is possible to retrieve the equivalent information that UWB pulses are able to give, yet keeping the same contained hardware complexity of a CW system. At the same time, the proposed system proves to be robust and insensible to environmental changes. The theoretical studies have aimed at the demonstration that the solution under study helps in avoiding the blind frequencies that affect CW systems, because of sensitivity issues that depend on the variability of the reflection coefficient from the frequency and, as proved, from the harmonic content of the monitored motion. Supported by such theoretical studies, the preliminary tests are performed using laboratory instrumentation (a VNA and a commercial double ridge antenna) for a thorough campaign of measurements on assorted frequency bands, both in a controlled environment (anechoic angle) and in a concrete house, that inherently clutter the received signal. The second step involves the design and realization of a custom antenna, to be used in place of the double ridge and operating in a narrower band, which has demonstrated the same reliability of the commercial one. It has been verified in different conditions that the proposed system is able to detect both the position of the subject (i.e. distance from the antenna) and his breathing frequency, without any need for collaboration from the subject under measure. The final activity is the realization of a prototype of the device that implements the algorithms that have been studied. It is worth to highlight that the proposed system can be profitably adopted for Ambient Assisted Living framework, since it is not invasive and does not infringe the privacy of the end user, and yet it provides many valuable information about the subject's health status.
The impact of dementia and other cognitive diseases on the capability of making everyday life activities is well-known, as well as it has been demonstrated that dementia may strongly affect not only the life of the person affected, but also the surrounding relatives. In fact, in the majority of the cases, the caregiver of a person with dementia is a family member, who usually loses the possibility to run a normal life, because of the burden of assistance. As a consequence, a number of research projects have been carried out, under the umbrella of different funding schemes, to identify the right technologies able to support caregivers of people with dementia, both informal and formal ones. This chapter provides first a review of the research and market state of the art of assistive technologies for dementia. Then, from this analysis, the requirements, barriers and success factors of the different solutions are identified and discussed. Two projects are presented in detail, to cover the domain of technologies for informal caregivers, applied at home where the patient with dementia lives; and to show how similar technologies, in a different architectural arrangement and with the provision of different services and functionalities, may be used in nursing homes and care institutions. Conclusions regarding the current stage of development and open issues are finally presented in the last section.
Technological solutions represent new opportunities to help elderly people and their caregivers in daily life. Understanding human behavior becomes thus essential in Ambient Assisted Living field especially for prevention and monitoring applications. In particular, recognition of human gestures is important to deliver personalized service to keep elderly people independent, while being monitored by caregivers. This chapter aims to underline the importance of recognizing behavior and in particular gesture in order to monitor older persons. An overview of the gesture recognition applications in AAL is therefore presented with a focus on the existing technologies used to capture hand gestures. Algorithms for data processing and classification are also described. Finally, an example of daily gesture recognition in AAL is presented where different gestures are recognized by mean of SensHand.
The population of elderly people keeps increasing rapidly. It is becoming a predominant aspect of our societies. It is therefore necessary to find ways to ensure the care of the needs of older people. We propose the project AngelHome, an innovative home automation system for Ambient Assisted Living that aims to improve the lives of people in their homes. In this way they can live independently and safely within their home without changing their habits. Angel Home offers an integrated and cooperative system with sensors and SOs to enhance the comfort inside the home. It also allows to ensure the safety people through the use of systems able to detect emergency situations. Another objective of this project is the identification and classification of rare events, through machine learning approach, based on data coming from different embedded systems and from the information system. This amount of information (located in a cloud system) also enhances the intelligence of the house, which will learn from what happened in the past.
Irregular patterns in the circadian rhythm may cause health problems, such as psychological or neurological disorders. Consequently, early detection of anomalies in circadian rhythm could be useful for the prevention of such problems. This work describes a multi-sensor platform for anomalies detection in circadian rhythm. The inputs of the platform are sequences of human postures, extensively used for analysis of activities of daily living and, more in general, for human behaviour understanding. The postures are acquired by using both ambient and wearable sensors that are time-of-flight 3D vision sensor, ultra-wideband radar sensor and triaxial accelerometer. The suggested platform aims to provide an abstraction layer with respect to the underlying sensing technologies, exploiting the postural information in common to all involved sensors (i.e., standing, bending, sitting, lying down). Furthermore, in order to fill the lack of datasets containing long-term postural sequences, which are required in circadian rhythm analysis, a simulator of activities of daily living/postures has been proposed. The capability of the platform in providing a sensing invariant interface (i.e., abstracted from any specific sensing technology) was demonstrated by preliminary results, exhibiting high accuracy in circadian rhythm anomalies detection using the three aforementioned sensors.
This chapter provides a review of the State of the Art in the Field of advanced solutions for the monitoring of critical events against elderly persons and people with neurological pathologies (e.g., Alzheimer's, Parkinson's). Many systems have been proposed in the literature which use cameras or other forms of sensing. Privacy, reliability and false alarms are the main challenges to be considered for the development of efficient systems to detect and classify the Activities of Daily Living (ADL) and Falls. The design of such systems, especially if wearable, requires a user centered design approach as well as the use of reliable sensors and advanced signal processing techniques, which have to fulfill constraints given by the power and computational budgets. As a case study, a solution based on a multi-sensor data fusion approach is presented. The system is able to recognize critical events like falls or prolonged inactivity and to detect the user posture. In particular, algorithms developed for the Activities of Daily Living classification combine data from an accelerometer and a gyroscope embedded in the user device. Tests performed on the developed prototype confirm the suitability of the device performances, which have been estimated in terms of sensitivity and specificity in performing Falls and ADL classification tasks. Apart from alerts management, the information provided by this system is useful to track the evolution of the user pathology, also during rehabilitation tasks.
Healthcare paradigms, due to demographic changes, are definitely aiming at effective prevention and early diagnosis strategies. This inherently calls for continuous monitoring of (ageing) people in their own living environment and while attending at daily living activities. Such monitoring may rely on a wide range of sensing technologies, each featuring different trade-offs among main parameters such as accuracy, expressivity, cost, reliability and intrusively. This includes clinical sensors (suitable for self-managed, precise measurement of physiological parameters), wearable devices (continuously monitoring health or activity features) and environmental sensors distributed in the living environment (suitable for indirect assessment of relevant behaviours, besides serving basic safety purposes). In this chapter, the meaning of human monitoring from a home-care point of view will be defined, and the basic sensor categories will be reviewed. Then, the design and the main features of the CARDEA home monitoring system will be discussed. Finally, some application examples, coming from European project living-lab experiences, will be illustrated, and some results obtained by data fusion and analysis techniques, suitable for inferring health and wellness information by effectively correlating raw data coming from the sensor field, will be presented.
This chapter presents an overview about the application of Ambient Intelligence to Healthcare environments, and how the current use of mobile devices provides new opportunities and determines new research areas such as mHealth, focused on the use of mobile technologies to improve people quality life getting clinical benefits. In this sense, monitoring is an important branch today in which researchers are working. There are many systems to monitor several factors regarding health. In our case, three kinds of monitoring systems are detailed. The first approach describes a framework-based system to monitor several diseases like diabetes taking into account the most common factors. In the second case, we present the importance of long-term gait monitoring to detect frailty symptoms in early stages by developing software system and hardware infrastructures based on a variety of sensors. Finally, we detail an analysis tool to measure the level of performance of Instrumental Activities of Daily Living (IADL) of elderly people at home. Likewise, this tool provides some functionalities to assess level stress and quality of life of caregivers by conducting questionnaires. Besides the description of the systems, we detail the evaluations carried out in each. We show the results according to system characteristics, usability, functionality and deployment of features among others.
Smart patient monitoring systems have rapidly evolved during the past two decades and have the potential to improve current patient care and medical staffworkflow. With advanced sensors, sophisticated hardware and fast-growing wireless communication technologies, there are enormous opportunities for ubiquitous solutions in all areas of healthcare, especially patient monitoring. Current methods of non-invasive blood pressure measurement are based on inflation and deflation of a cuff with some effects on arteries where blood pressure is being measured. This approach is non-continuous, time delayed, and might cause patient discomfort. We aim to monitor and measure cuff-less and continuous blood pressure using a smartphone. Cuff-less approach enables continuous blood pressure monitoring capabilities and is particularly attractive as blood pressure is one of the most important factors to assess risk of falls in older adults. A smartphone application was developed to collect PhotoPlethysmoGram (PPG) waveform and electrocardiogram (ECG) in order to calculate pulse transit time (PTT). The user's systolic blood pressure is calculated using the PPT and precise optimisation model. The proposed application can be integrated with our developed falls risk assessment algorithm for inpatient older adults. This study proposes a novel approach of continuous blood pressure monitoring using cuff-less method that can be employed for prevention of inpatient falls using smartphone.