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Unobtrusive Monitoring of Mobility and Health during Everyday Life
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Unobtrusive Monitoring of Mobility and Health during Everyday Life

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

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 5791

Special Issue Editors

Prof. Dr. Claudine Lamoth

E-Mail Website
Guest Editor
University of Groningen; The Netherlands
Interests: medicine; wearable sensors; health; gait
Dr. Kim van Schooten

E-Mail Website
Guest Editor
Neuroscience Research Australia, University of New South Wales, Sydney, Australia
Interests: older adults; accidental falls; gait stability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world’s population is ageing, leading to growth in the number and proportion of older people. These demographic changes will increase pressure on health care systems, which necessitates a shift from assessments and treatment, towards the prevention of age-related disease and the support of a healthy lifestyle. This leads to a need for a personalized approach of monitoring, prevention and remote care, which takes place in one’s own environment.

Unobtrusive monitoring technology has the potential to provide solutions that support sustainable behavioural changes to reduce age-related health decline. Nowadays, a wide range of sensors is available to collect a continuous flow of information from people as they engage in everyday activities. These sensors collect for instance: movement, physical activity, sleep, heart rate, respiration, body temperature, blood pressure, glucose level, nutrition, cognition and social participation. Technological innovations for acquisition of information at different levels of function evolve rapidly, e.g. small wearable inertial measurement units that include accelerometers, gyroscope, magnetometer, barometer, temperature and ambient light sensors; sensors weaved or integrated into clothing, accessories and the living environment; small gadgets like smart watches, patches. These sensors are all available to monitor different bodily functions.

In our view, the main challenges remain to: 1) combine and extract relevant information from multiple sensors, 2) develop personalized algorithms to evaluate health and mobility status, 3) create awareness of their clinical utility, 4) include concepts of real-time motivational feedback, and 5) provide interventions that support sustainable behavioural changes. Overcoming these challenges requires interdisciplinary collaboration at the interface of technological, movement, gerontology, behavioural and social sciences. This special issue aims to connect researchers in these disciplines, addressing and discussing recent advances, innovative solutions and emerging issues in unobtrusive sensing of different domains of health, that support personalized, awareness and sustainable behaviour directed towards healthy ageing.

We will accept full-length research articles, short commentaries and reviews focused on monitoring of mobility and health in daily life to promote healthy ageing. Topics of interest include:

  • Innovative sensors to unobtrusively measure bodily functions
  • Algorithm development, machine learning and artificial intelligence for feature extraction of data obtained from free-living environments
  • Data fusion and aggregation and their applications for healthy ageing
  • Technical validation of sensor-based measurement in the free-living environment
  • Usability, acceptance and ethics of unobtrusive sensing systems in older people
  • Models for telemonitoring and remote care to support healthy behaviour and active ageing
  • Sensor-based interventions to promote healthy lifestyle of the ageing population

Prof. Claudine Lamoth
Dr. Kim van Schooten
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.

Published Papers (2 papers)

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21 pages, 4928 KiB  
Article
A Novel Walking Activity Recognition Model for Rotation Time Series Collected by a Wearable Sensor in a Free-Living Environment
by Raphaël Brard, Lise Bellanger, Laurent Chevreuil, Fanny Doistau, Pierre Drouin and Aymeric Stamm 
Sensors 2022, 22(9), 3555; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093555 - 07 May 2022
Cited by 5 | Viewed by 2119
Abstract
Solutions to assess walking deficiencies are widespread and largely used in healthcare. Wearable sensors are particularly appealing, as they offer the possibility to monitor gait in everyday life, outside a facility in which the context of evaluation biases the measure. While some wearable [...] Read more.
Solutions to assess walking deficiencies are widespread and largely used in healthcare. Wearable sensors are particularly appealing, as they offer the possibility to monitor gait in everyday life, outside a facility in which the context of evaluation biases the measure. While some wearable sensors are powerful enough to integrate complex walking activity recognition models, non-invasive lightweight sensors do not always have the computing or memory capacity to run them. In this paper, we propose a walking activity recognition model that offers a viable solution to this problem for any wearable sensors that measure rotational motion of body parts. Specifically, the model was trained and tuned using data collected by a motion sensor in the form of a unit quaternion time series recording the hip rotation over time. This time series was then transformed into a real-valued time series of geodesic distances between consecutive quaternions. Moving average and moving standard deviation versions of this time series were fed to standard machine learning classification algorithms. To compare the different models, we used metrics to assess classification performance (precision and accuracy) while maintaining the detection prevalence at the level of the prevalence of walking activities in the data, as well as metrics to assess change point detection capability and computation time. Our results suggest that the walking activity recognition model with a decision tree classifier yields the best compromise in terms of precision and computation time. The sensor that was used had purposely low computing and memory capacity so that reported performances can be thought of as the lower bounds of what can be achieved. Walking activity recognition is performed online, i.e., on-the-fly, which further extends the range of applicability of our model to sensors with very low memory capacity. Full article
(This article belongs to the Special Issue Unobtrusive Monitoring of Mobility and Health during Everyday Life)
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20 pages, 5388 KiB  
Article
Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults
by Gastón Márquez, Alejandro Veloz, Jean-Gabriel Minonzio, Claudio Reyes, Esteban Calvo and Carla Taramasco
Sensors 2022, 22(6), 2321; https://0-doi-org.brum.beds.ac.uk/10.3390/s22062321 - 17 Mar 2022
Cited by 7 | Viewed by 3045
Abstract
The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the [...] Read more.
The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior. Full article
(This article belongs to the Special Issue Unobtrusive Monitoring of Mobility and Health during Everyday Life)
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