A Survey on Ambient Intelligence in Health Care

By Giovanni Acampora, Diane Joyce Cook, Parisa Rashidi, Athanasios V. Vasilakos. 2013

616 citations recorded by the source corpus.

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive, and anticipatory communications. Such innovative interaction paradigms make AmI technology a suitable candidate for developing various real life solutions, including in the healthcare domain. This survey will discuss the emergence of AmI techniques in the healthcare domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of AmI, such as smart environments and wearable medical devices. We will summarize the state-of-the-art artificial intelligence (AI) methodologies used for developing AmI system in the healthcare domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions), and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Surgery

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, background): “s the potential to enhance the healthcare domain dramatically by learning from user interaction, reasoning reasoning about users’ goals and intensions, and planning activities and future interactions [350]. By using various kinds of sensing devices, such as smart phones, GPS and body sensors monitor”
A Survey on Information and Communication Technologies for Industry 4.0: State-of-the-Art, Taxonomies, Perspectives, and Challenges (2019, background): “, for observation and emergency assistance [312] or for proposing medical and/or life style engagements [313].”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, background): “These challenges are also aggravated by the increasing elderly population, which translates into a horde of chronic diseases and higher demand for healthcare services [52].”
The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges (2018, background): “gency assistance, in case of strong changes in the observed parameters or deviations from the normal range [3].”
Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0 (2020, background): “gies would allow to automatically alert a healthcare center for observation and emergency assistance, in case deviations from the normal activities and parameters are detected [123], or for less urgent cases would allow to propose medical or life-style engagements [124].”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “…places where people live to sense people’s movements, gestures, habits, and intentions, and respond to needs in a seamless and nonintrusive manner (Acampora et al. 2013). r s it L i r a r y Indeed, such ambient systems have the potential to provide visibility into the most intimate spheres of a…”
Mobile Crowd Sensing and Computing (2015, background): “For example, the fact that an object with an RFID tag can be uniquely identiﬁed and tracked back to its user might bring up many privacy issues [Acampora et al. 2013].”
High-Accuracy Localization for Assisted Living: 5G systems will turn multipath channels from foe to friend (2016, background): “A recent survey on ambient intelligence in health care [1] illustrates the wide range of applications that could be supported by a cm-accuracy indoor positioning system alone: activity recognition, behavioral pattern discovery, anomaly detection, and decision support methods can all be based on such”
Seven HCI Grand Challenges (2019, methodology): “An important potential pitfall refers to overreliance on the technology, which may tamper with individuals ’ self-confidence to manage their life and result in preliminary loss of abilities (Acampora et al., 2013).”