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Sensor and Assistive Technologies for Smart Life

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 38653

Special Issue Editors


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Guest Editor
Department of Computer Technology, University of Alicante, 03690 Alicante, Spain
Interests: ambient assisted living; computer vision; evolutionary computation; human action recognition; ambient intelligence

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Guest Editor
Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: electronic measurements; wearable sensors; ambient assisted living; depth sensors
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Guest Editor
Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
Interests: Technology Acceptance; User Diversity; Usability; Human-Comupter Interaction; eHealth

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Guest Editor
Universidad de Alicante, Spain
Interests: computer vision; machine learning; human activity recognition

Special Issue Information

Dear Colleagues,

Innovation in technologies and services for active and assisted living (AAL) stands out as one promising solution to address the healthcare and social challenges produced by the demographic change and current economic context. AAL systems make use of a variety of sensors to monitor the environment and its dwellers to improve the quality of life and support independent and healthy living of older or impaired people at home, at the workplace, and in public spaces.

Recent advances in wearable computing, with a myriad of products in the market (e.g., wearable cameras and smart watches, wristbands, and glasses), increased functionality of mobile devices and apps for health and wellbeing, and easier installation of and cheaper home automation systems are supporting the adoption of healthcare and assisted living services by a larger population. For instance, lifelogging technologies may enable and motivate individuals to pervasively capture data about them, their environments, and the people with whom they interact. Acquisition, measurement, and processing of physiological signals (e.g., heart rate, respiratory rate, body temperature, and skin conductance), motion, location, performed activities, images seen, and sounds heard are the basis for the provision of a variety of cutting-edge services to age in place by increasing peoples’ health, wellbeing, and independence. In smart life, examples of these technological services include providing personalized healthcare, monitoring wellness and quality of life (e.g., physical activity, dietary habits), supporting people with memory cognitive impairments, assisting with social participation, helping with mobility, supporting formal and family caregivers, using predictive systems to address among others, decline in cognition, aggressive behaviors, and fall prevention.

Despite the numerous advantages, users can perceive this continuous monitoring as intrusive and in violation of fundamental rights to privacy and data protection, because of the concern that data could be accessed by unauthorized users or stored for inappropriate use. Depending on the individual technology and the context of use, users’ acceptance of such technologies can also be low because they create a sense of Orwellian “Big Brother” surveillance.

Therefore, this Special Issue, supported by project PAAL—Privacy-Aware and Acceptable Lifelogging services for older and frail people, addresses not only sensors in the latest technological advances in lifelogging and monitoring technologies, but also current research related to user acceptance, ethics, data protection, and privacy-by-design.

Dr. Francisco Florez-Revuelta
Dr. Susanna Spinsante
Dr. Wiktoria Wilkowska
Dr. Pau Climent-Perez
Guest Editors

Manuscript Submission Information

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Published Papers (6 papers)

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Research

21 pages, 6274 KiB  
Article
Framework for Simultaneous Indoor Localization, Mapping, and Human Activity Recognition in Ambient Assisted Living Scenarios
by Jesus D. Ceron, Diego M. López, Felix Kluge and Bjoern M. Eskofier
Sensors 2022, 22(9), 3364; https://doi.org/10.3390/s22093364 - 28 Apr 2022
Cited by 2 | Viewed by 1920
Abstract
Indoor localization and human activity recognition are two important sources of information to provide context-based assistance. This information is relevant in ambient assisted living (AAL) scenarios, where older adults usually need supervision and assistance in their daily activities. However, indoor localization and human [...] Read more.
Indoor localization and human activity recognition are two important sources of information to provide context-based assistance. This information is relevant in ambient assisted living (AAL) scenarios, where older adults usually need supervision and assistance in their daily activities. However, indoor localization and human activity recognition have been mostly considered isolated problems. This work presents and evaluates a framework that takes advantage of the relationship between location and activity to simultaneously perform indoor localization, mapping, and human activity recognition. The proposed framework provides a non-intrusive configuration, which fuses data from an inertial measurement unit (IMU) placed in the person’s shoe, with proximity and human activity-related data from Bluetooth low energy beacons (BLE) deployed in the indoor environment. A variant of the simultaneous location and mapping (SLAM) framework was used to fuse the location and human activity recognition (HAR) data. HAR was performed using data streaming algorithms. The framework was evaluated in a pilot study, using data from 22 people, 11 young people, and 11 older adults (people aged 65 years or older). As a result, seven activities of daily living were recognized with an F1 score of 88%, and the in-door location error was 0.98 ± 0.36 m for the young and 1.02 ± 0.24 m for the older adults. Furthermore, there were no significant differences between the groups, indicating that our proposed method works adequately in broad age ranges. Full article
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
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17 pages, 3980 KiB  
Article
Acceptance and Preferences of Using Ambient Sensor-Based Lifelogging Technologies in Home Environments
by Julia Offermann, Wiktoria Wilkowska, Angelica Poli, Susanna Spinsante and Martina Ziefle
Sensors 2021, 21(24), 8297; https://doi.org/10.3390/s21248297 - 11 Dec 2021
Cited by 5 | Viewed by 2598
Abstract
Diverse sensor-based technologies can be used to track (older and frail) people’s movements and behaviors in order to detect anomalies and emergencies. Using several ambient sensors and integrating them into an assisting ambient system allows for the early identification of emergency situations and [...] Read more.
Diverse sensor-based technologies can be used to track (older and frail) people’s movements and behaviors in order to detect anomalies and emergencies. Using several ambient sensors and integrating them into an assisting ambient system allows for the early identification of emergency situations and health-related changes. Typical examples are passive infrared sensors (PIR), humidity and temperature sensors (H&T) as well as magnetic sensors (MAG). So far, it is not known whether and to what extent these three specific sensor types are perceived and accepted differently by future users. Therefore, the present study analyzed the perception of benefits and barriers as well as acceptance of these specific sensor-based technologies using an online survey (reaching N=312 German participants). The results show technology-related differences, especially regarding the perception of benefits. Furthermore, the participants estimated the costs of these sensors to be higher than they are, but at the same time showed a relatively high willingness to pay for the implementation of sensor-based technologies in their home environment. The results enable the derivation of guidelines for both the technical development and the communication and information of assisting sensor-based technologies and systems. Full article
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
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40 pages, 4449 KiB  
Article
The SHAPES Smart Mirror Approach for Independent Living, Healthy and Active Ageing
by Javier Dorado Chaparro, Jesus Fernandez-Bermejo Ruiz, Maria J. Santofimia Romero, Cristina Bolaños Peño, Luis Unzueta Irurtia, Meritxell Garcia Perea, Xavier del Toro Garcia, Felix J. Villanueva Molina, Sonja Grigoleit and Juan C. Lopez
Sensors 2021, 21(23), 7938; https://doi.org/10.3390/s21237938 - 28 Nov 2021
Cited by 12 | Viewed by 8459
Abstract
The benefits that technology can provide in terms of health and support for independent living are in many cases not enough to break the barriers that prevent older adults from accepting and embracing technology. This work proposes a hardware and software platform based [...] Read more.
The benefits that technology can provide in terms of health and support for independent living are in many cases not enough to break the barriers that prevent older adults from accepting and embracing technology. This work proposes a hardware and software platform based on a smart mirror, which is equipped with a set of digital solutions whose main focus is to overcome older adults’ reluctance to use technology at home and wearable devices on the move. The system has been developed in the context of two use cases: the support of independent living for older individuals with neurodegenerative diseases and the promotion of physical rehabilitation activities at home. Aspects such as reliability, usability, consumption of computational resources, performance and accuracy of the proposed platform and digital solutions have been evaluated in the initial stages of the pilots within the SHAPES project, an EU-funded innovation action. It can be concluded that the SHAPES smart mirror has the potential to contribute as a technological breakthrough to overcome the barriers that prevent older adults from engaging in the use of assistive technologies. Full article
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
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38 pages, 20654 KiB  
Article
Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities
by Francisco M. Calatrava-Nicolás, Eduardo Gutiérrez-Maestro, Daniel Bautista-Salinas, Francisco J. Ortiz, Joaquín Roca González, José Alfonso Vera-Repullo, Manuel Jiménez-Buendía, Inmaculada Méndez, Cecilia Ruiz-Esteban and Oscar Martínez Mozos
Sensors 2021, 21(20), 6865; https://doi.org/10.3390/s21206865 - 16 Oct 2021
Cited by 13 | Viewed by 8031
Abstract
The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and [...] Read more.
The increasingly ageing population and the tendency to live alone have led science and engineering researchers to search for health care solutions. In the COVID 19 pandemic, the elderly have been seriously affected in addition to suffering from isolation and its associated and psychological consequences. This paper provides an overview of the RobWell (Robotic-based Well-Being Monitoring and Coaching System for the Elderly in their Daily Activities) system. It is a system focused on the field of artificial intelligence for mood prediction and coaching. This paper presents a general overview of the initially proposed system as well as the preliminary results related to the home automation subsystem, autonomous robot navigation and mood estimation through machine learning prior to the final system integration, which will be discussed in future works. The main goal is to improve their mental well-being during their daily household activities. The system is composed of ambient intelligence with intelligent sensors, actuators and a robotic platform that interacts with the user. A test smart home system was set up in which the sensors, actuators and robotic platform were integrated and tested. For artificial intelligence applied to mood prediction, we used machine learning to classify several physiological signals into different moods. In robotics, it was concluded that the ROS autonomous navigation stack and its autodocking algorithm were not reliable enough for this task, while the robot’s autonomy was sufficient. Semantic navigation, artificial intelligence and computer vision alternatives are being sought. Full article
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
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23 pages, 8589 KiB  
Article
A Collaborative Application for Assisting the Management of Household Plastic Waste through Smart Bins: A Case of Study in the Philippines
by Navjot Sidhu, Alberto Pons-Buttazzo, Andrés Muñoz and Fernando Terroso-Saenz
Sensors 2021, 21(13), 4534; https://doi.org/10.3390/s21134534 - 1 Jul 2021
Cited by 14 | Viewed by 12042
Abstract
The management and collection of household waste often represents a demanding task for elderly or impaired people. In particular, the increasing generation of plastic waste at home may pose a problem for these groups, as this type of waste accumulates very rapidly and [...] Read more.
The management and collection of household waste often represents a demanding task for elderly or impaired people. In particular, the increasing generation of plastic waste at home may pose a problem for these groups, as this type of waste accumulates very rapidly and occupies a considerable amount of space. This paper proposes a collaborative infrastructure to monitor household plastic waste. It consists of simple smart bins using a weight scale and a smart application that forecasts the amount of plastic generated for each bin at different time horizons out of the data provided by the smart bins. The application generates optimal routes for the waste-pickers collaborating in the system through a route-planning algorithm. This algorithm takes into account the predicted amount of plastic of each bin and the waste-picker’s location and means of transport. This proposal has been evaluated by means of a simulated scenario in Quezon City, Philippines, where severe problems with plastic waste have been identified. A set of 176 experiments have been performed to collect data that allow representing different user behaviors when generating plastic waste. The results show that our proposal enables waste-pickers to collect more than the 80% of the household plastic-waste bins before they are completely full. Full article
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
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22 pages, 23195 KiB  
Article
Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis
by Alexey Kashevnik, Walaa Othman, Igor Ryabchikov and Nikolay Shilov
Sensors 2021, 21(11), 3771; https://doi.org/10.3390/s21113771 - 29 May 2021
Cited by 7 | Viewed by 3506
Abstract
Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: [...] Read more.
Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application. Full article
(This article belongs to the Special Issue Sensor and Assistive Technologies for Smart Life)
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