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Sensing for Healthy Ageing and Wellbeing

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

Deadline for manuscript submissions: closed (1 March 2022) | Viewed by 7912

Special Issue Editors


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Guest Editor
BCN-MedTech, Universitat Pompeu Fabra, 08002 Barcelona, Spain
Interests: multilevel and dynamic analysis of interactions between processes at the body, organ, and tissue levels; how these interactions can be driven by, or related to, body motion and subject behaviour, with particular interest in the impact that relaxation disciplines can have on human movement

E-Mail Website
Guest Editor
Departamento De Ingeniería Mecánica, Universidad Nacional de Colombia, Medellín 050000, Colombia
Interests: the biomechanics of transfemoral amputees which involves the mechanical response of soft and hard tissues, skin tribology and the development of devices for its measurement, and finite lement modeling; the development of porous materials for cell culture

Special Issue Information

Dear Colleagues,

Healthy ageing should be an aim in modern society. Following the increase in life expectancy due to improvements in medicine and technology, the prevalence of age-related disorders is increasing. Further, such conditions are appearing earlier in life, even in adolescence, introducing disturbances into gait, posture, and general daily activities. These disorders can appear in individuals with a previously diagnosed musculoskeletal lesion as well as in able-bodied persons. Thus, there is a disparity between increases in life expectancy and wellbeing.

To understand this phenomenon, we must remember that life is a dynamic system in which several factors interact with each other creating non-linear scenarios that require extensive specialized data for proper analysis. For instance, bad posture in young age can influence development during adolescence and present fully only years later. Muskuloskeletal lesions can modify the gait and posture of subjects, promoting the development of other disorders. Finally, aging itself can modify the coordination of body movement and result in reduced wellbeing.

Given the great variety of scenarios present, the collection of related data requires specialized sensors that are able to capture daily activities inside and outside the laboratory without interfering with subjects’ lives.

Topic of interest for this Special Issue include, but are not limited to

  • Sensors for daily activity monitoring;
  • Tools and algoritmes for activity recognition with a focus on sensing;
  • Wearable sensors.

Dr. Simone Tassani
Prof. Dr. Juan Ramírez
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

  • healthy ageing
  • wearable sensors
  • daily activity monitoring
  • wellbeing

Published Papers (3 papers)

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Research

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16 pages, 1338 KiB  
Article
Eigenbehaviour as an Indicator of Cognitive Abilities
by Angela A. Botros, Narayan Schuetz, Christina Röcke, Robert Weibel, Mike Martin, René M. Müri and Tobias Nef
Sensors 2022, 22(7), 2769; https://0-doi-org.brum.beds.ac.uk/10.3390/s22072769 - 04 Apr 2022
Cited by 1 | Viewed by 1808
Abstract
With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability [...] Read more.
With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively—it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition. Full article
(This article belongs to the Special Issue Sensing for Healthy Ageing and Wellbeing)
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23 pages, 3638 KiB  
Article
Validity, Reliability and Sensitivity to Change of Three Consumer-Grade Activity Trackers in Controlled and Free-Living Conditions among Older Adults
by Kaja Kastelic, Marina Dobnik, Stefan Löfler, Christian Hofer and Nejc Šarabon
Sensors 2021, 21(18), 6245; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186245 - 17 Sep 2021
Cited by 5 | Viewed by 3139
Abstract
Wrist-worn consumer-grade activity trackers are popular devices, developed mainly for personal use. This study aimed to explore the validity, reliability and sensitivity to change of movement behaviors metrics from three activity trackers (Polar Vantage M, Garmin Vivoactive 4s and Garmin Vivosport) in controlled [...] Read more.
Wrist-worn consumer-grade activity trackers are popular devices, developed mainly for personal use. This study aimed to explore the validity, reliability and sensitivity to change of movement behaviors metrics from three activity trackers (Polar Vantage M, Garmin Vivoactive 4s and Garmin Vivosport) in controlled and free-living conditions when worn by older adults. Participants (n = 28; 74 ± 5 years) underwent a videotaped laboratory protocol while wearing all three trackers. On a separate occasion, participants (n = 17 for each of the trackers) wore one (randomly assigned) tracker and a research-grade activity monitor ActiGraph wGT3X-BT simultaneously for six consecutive days. Both Garmin trackers showed excellent performance for step counts, with a mean absolute percentage error (MAPE) below 20% and intraclass correlation coefficient (ICC2,1) above 0.90 (p < 0.05). The MAPE for sleep time was within 10% for all the trackers tested, while it was far beyond 20% for all other movement behaviors metrics. The results suggested that all three trackers could be used for measuring sleep time with a high level of accuracy, and both Garmin trackers could also be used for step counts. All other output metrics should be used with caution. The results provided in this study could be used to guide choice on activity trackers aiming for different purposes—individual use, longitudinal monitoring or in clinical trial setting. Full article
(This article belongs to the Special Issue Sensing for Healthy Ageing and Wellbeing)
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Review

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17 pages, 262 KiB  
Review
Review of Studies on Older Drivers’ Behavior and Stress—Methods, Results, and Outlook
by Yanning Zhao and Toshiyuki Yamamoto
Sensors 2021, 21(10), 3503; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103503 - 18 May 2021
Cited by 5 | Viewed by 2336
Abstract
This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers [...] Read more.
This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers or considers participants according to various age groups. Nevertheless, the definition of ‘older driver’ varies not only across studies but also across different government reports. Although questionnaire surveys are widely used to affordably obtain massive data in a short time, they lack objectivity. In contrast, biomedical information can increase the reliability of a driving stress assessment when collected in environments such as driving simulators and on-road experiments. Various studies determined that driving behavior and stress remain stable regardless of age, whereas others reported degradation of driving abilities and increased driving stress among older drivers. Instead of age, many researchers recommended considering other influencing factors, such as gender, living area, and driving experience. To mitigate bias in findings, this literature review suggests a hybrid method by applying surveys and collecting on-road/in-vehicle data. Full article
(This article belongs to the Special Issue Sensing for Healthy Ageing and Wellbeing)
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