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Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living

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

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 29483

Special Issue Editors

Dr. Susanna Spinsante

E-Mail Website
Guest Editor
Department of Information Engineering, Marche Polytechnic University, 60131 Ancona, Italy
Interests: electronic measurements; wearable sensors; ambient assisted living; depth sensors
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lorenzo Scalise

E-Mail Website
Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, Ancona, Italy
Interests: mechanical and thermal measurements; sensors; measurement methods; ambient assisted living; wearable sensors; optical sensors; biomedical sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ambient Assisted Living (AAL) encompasses systems, applications, and services that adopt sensing devices, measurement methods, and Information and communications technologies (ICT) to offer new products and solutions to address health and well-being needs, particularly for the aging population. The expected outcomes from the introduction of such a paradigm include a positive impact on older adults’ health-related quality of life (QoL), the management of health conditions, and improvements in overall well-being, whilst, at the same time, reducing the costs of healthcare provision.

Smart sensors and measurement methods aimed at objectively capturing and quantifying an individual’s health-related to life quality stands at the foundation of the above-mentioned paradigm, enabling the assessment of the impact of AAL-related solutions and the evaluation of their performance. Despite their importance, however, reliable and easy-to-quantify measures of health-related QoL of older adults are still missing, as well as measurement methods that can be easily integrated and applied into daily life routines, ensuring accuracy, reliability, and adherence.

This Special Issue aims to highlight advances in the development, testing, and modeling of smart wearable and ambient sensors, methods, and frameworks to measure health-related QoL and well-being of an individual, with a particular focus on the AAL domain and older adults. Topics include, but are not limited, to the following:

  • Minimally obtrusive methods to measure physiological and health-related parameters in living environments.
  • Metrics and methods to measure health-related quality of life of older adults
  • Accuracy of wearable and ambient sensors and the impact of measurement uncertainty on the performance of learning algorithms for AAL and well-being
  • Sensing solutions for health-related quality of life, targeting older adults
  • Metrological issues in health-related quality of life and well-being measurements
  • Smart sensing and advanced signal processing (i.e., machine learning) in AAL
  • Application-driven sensing solutions for living environments and AAL

Dr. Susanna Spinsante
Prof. Dr. Lorenzo Scalise
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Health-related quality of life
  • Smart sensors for AAL
  • Metrics for well-being
  • Measuring solutions for AAL
  • Measurement method for living environments

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Published Papers (4 papers)

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Research

47 pages, 9235 KiB  
Article
Flexible Virtual Reality System for Neurorehabilitation and Quality of Life Improvement
by Iulia-Cristina Stanica, Florica Moldoveanu, Giovanni-Paul Portelli, Maria-Iuliana Dascalu, Alin Moldoveanu and Mariana Georgiana Ristea
Sensors 2020, 20(21), 6045; https://doi.org/10.3390/s20216045 - 23 Oct 2020
Cited by 35 | Viewed by 7881
Abstract
As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms [...] Read more.
As life expectancy is mostly increasing, the incidence of many neurological disorders is also constantly growing. For improving the physical functions affected by a neurological disorder, rehabilitation procedures are mandatory, and they must be performed regularly. Unfortunately, neurorehabilitation procedures have disadvantages in terms of costs, accessibility and a lack of therapists. This paper presents Immersive Neurorehabilitation Exercises Using Virtual Reality (INREX-VR), our innovative immersive neurorehabilitation system using virtual reality. The system is based on a thorough research methodology and is able to capture real-time user movements and evaluate joint mobility for both upper and lower limbs, record training sessions and save electromyography data. The use of the first-person perspective increases immersion, and the joint range of motion is calculated with the help of both the HTC Vive system and inverse kinematics principles applied on skeleton rigs. Tutorial exercises are demonstrated by a virtual therapist, as they were recorded with real-life physicians, and sessions can be monitored and configured through tele-medicine. Complex movements are practiced in gamified settings, encouraging self-improvement and competition. Finally, we proposed a training plan and preliminary tests which show promising results in terms of accuracy and user feedback. As future developments, we plan to improve the system’s accuracy and investigate a wireless alternative based on neural networks. Full article
(This article belongs to the Special Issue Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living)
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21 pages, 5095 KiB  
Article
Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning
by Beiyu Lin and Diane J. Cook
Sensors 2020, 20(18), 5207; https://doi.org/10.3390/s20185207 - 12 Sep 2020
Cited by 4 | Viewed by 3544
Abstract
Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce [...] Read more.
Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm—Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)—to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual’s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident’s cognitive health diagnosis, with an accuracy of 0.84. Full article
(This article belongs to the Special Issue Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living)
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21 pages, 1454 KiB  
Article
A Robust and Real-Time Capable Envelope-Based Algorithm for Heart Sound Classification: Validation under Different Physiological Conditions
by Angelika Thalmayer, Samuel Zeising, Georg Fischer and Jens Kirchner
Sensors 2020, 20(4), 972; https://doi.org/10.3390/s20040972 - 11 Feb 2020
Cited by 18 | Viewed by 7981
Abstract
This paper proposes a robust and real-time capable algorithm for classification of the first and second heart sounds. The classification algorithm is based on the evaluation of the envelope curve of the phonocardiogram. For the evaluation, in contrast to other studies, measurements on [...] Read more.
This paper proposes a robust and real-time capable algorithm for classification of the first and second heart sounds. The classification algorithm is based on the evaluation of the envelope curve of the phonocardiogram. For the evaluation, in contrast to other studies, measurements on 12 probands were conducted in different physiological conditions. Moreover, for each measurement the auscultation point, posture and physical stress were varied. The proposed envelope-based algorithm is tested with two different methods for envelope curve extraction: the Hilbert transform and the short-time Fourier transform. The performance of the classification of the first heart sounds is evaluated by using a reference electrocardiogram. Overall, by using the Hilbert transform, the algorithm has a better performance regarding the F1-score and computational effort. The proposed algorithm achieves for the S1 classification an F1-score up to 95.7% and in average 90.5%. The algorithm is robust against the age, BMI, posture, heart rate and auscultation point (except measurements on the back) of the subjects. Full article
(This article belongs to the Special Issue Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living)
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30 pages, 10925 KiB  
Article
A Secure Occupational Therapy Framework for Monitoring Cancer Patients’ Quality of Life
by Md. Abdur Rahman, Md. Mamunur Rashid, Julien Le Kernec, Bruno Philippe, Stuart J. Barnes, Francesco Fioranelli, Shufan Yang, Olivier Romain, Qammer H. Abbasi, George Loukas and Muhammad Imran
Sensors 2019, 19(23), 5258; https://doi.org/10.3390/s19235258 - 29 Nov 2019
Cited by 21 | Viewed by 9079
Abstract
Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as “after cancer treatment”. Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a [...] Read more.
Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as “after cancer treatment”. Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patient’s situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from one’s home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient’s data available from an in-home secure monitoring system for a better understanding of a patient’s current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development. Full article
(This article belongs to the Special Issue Smart Sensors and Measurements Methods for Quality of Life and Ambient Assisted Living)
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