Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

Cicirelli, Grazia; Marani, Roberto; Petitti, Antonio; Milella, Annalisa; D’Orazio, Tiziana

doi:10.3390/s21103549

Cited by 123 publications

(62 citation statements)

References 120 publications

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“…Cicirelli et al [3] identified the four main steps in developing an AAL system: sensor selection, data acquisition, recognition of elementary actions, and evaluations of complex behaviors. We also observe that most AAL solutions include sensing, transmitting, storing, and using data.…”

Section: Discussionmentioning

confidence: 99%

Reference Architectures, Platforms, and Pilots for European Smart and Healthy Living—Analysis and Comparison

et al. 2021

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Motivated by the aging trend, much effort is being invested into implementing ICT (Information and Communications Technology)-enabled systems to provide a better quality of life and support the independent living of older people. As a result, many systems, often labeled as eHealth or AAL (Ambient/Active Assisted Living), were developed over the years. In creating such systems, which very often serve various needs, different architectures have emerged. This work focuses on analyzing and comparing the work and architectures from seven (six of which are in progress) EU-funded healthcare projects, with a total budget of 126MEUR in which we participate. After establishing the theoretical foundation by defining core concepts, we give a brief background on architectures in eHealth and AAL. We elaborate on the chosen analysis method based on three established healthcare and AAL taxonomies we identified by performing a literature survey and the selected Reference Architecture Model (RAM). Since there is no standard way of describing architectures in the eHealth and AAL domain, we conducted the online survey during August and September 2020 and identified CREATE-IoT 3D RAM as the most appropriate option. We present a classification of selected projects based on established taxonomies and map projects’ architectures to CREATE-IoT 3D RAM, which we also propose as standard RAM for future digital healthcare and AAL projects. During our analysis, we identify the most common types of assistance: communication support, reminders, monitoring, and guidance to address health and communication issues. We conclude that proper ecosystems are critical for lowering entry barriers and facilitating sustainable solutions for smart and healthy living.

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Section: Discussionmentioning

confidence: 99%

Reference Architectures, Platforms, and Pilots for European Smart and Healthy Living—Analysis and Comparison

et al. 2021

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“…Elderly people face various diseases that need permanent healthcare systems and an accurate localization system to locate elderly people at the accident time. In [104], a comprehensive study is provided of the research and skills related to Active and Assisted Living (AAL) systems. The authors in [105] evaluate the widespread LoRaWAN communication infrastructure to alert a remotely connected healthcare server about elderly people accidents able to monitor the position and motion type of the elderly.…”

Section: Localization Methods In Smart Healthcarementioning

confidence: 99%

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

et al. 2021

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Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

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“… 2) studies that partially overlap with our studies, such as the literature review on PD diagnosis by Mei et al (2021) , that in addition to PD diagnosis by gait, also discuss other modalities such as voice, handwriting, magnetic resonance imaging (MRI), etc. 3) studies that are in the scope of this review but only cover a specific topic such as human motion trajectory prediction ( Rudenko et al, 2020 ), wearable sensing technologies for sports biomechanics ( Taborri et al, 2020 ), self-powered sensors and systems ( Wu et al, 2020 ), person re-Identification ( Wang et al, 2016 ), ( Nambiar et al, 2019 ), ( Karanam et al, 2019 ), machine learning in soft robotics ( Kim et al, 2021 ), ambient assisted living technologies (mostly AI-enabled and gait-related) ( Cicirelli et al, 2021 ), human action recognition ( Gurbuz and Amin, 2019 ), biomechanics ( Halilaj et al, 2018 ), gait recognition ( Kusakunniran, 2020 ), ( Singh et al, 2018 ), ( Wan C. et al, 2018 ), gait event detection and gait phase recognition ( Prasanth et al, 2021 ), clinical gait diagnostics of knee osteoarthritis ( Parisi et al, 2020 ), knee pathology assessment ( Abid et al, 2019 ), data preprocessing in gait classification ( Burdack et al, 2019 ), age estimation ( Aderinola et al, 2021 ), and banchamrk datasets ( Nunes et al, 2019 ). A survey paper by Alzubaidi et al (2021) provides an overview of deep learning, with helpful definitions and a discussion of strengths, limitations, and future trends of various deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Human Gait-Based Artificial Intelligence Applications

2022

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We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

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Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population

Cited by 123 publications

References 120 publications

Reference Architectures, Platforms, and Pilots for European Smart and Healthy Living—Analysis and Comparison

Reference Architectures, Platforms, and Pilots for European Smart and Healthy Living—Analysis and Comparison

GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

A Survey of Human Gait-Based Artificial Intelligence Applications

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