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Independent Living: Sensor-Assisted Intelligent Care and Healthcare
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Independent Living: Sensor-Assisted Intelligent Care and Healthcare

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

Deadline for manuscript submissions: 15 June 2024 | Viewed by 2654

Special Issue Editors

Prof. Dr. Lili Liu

E-Mail Website
Guest Editor
School of Public Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: ageing; dementia; rehabilitation; health technologies
Dr. Antonio Miguel-Cruz

E-Mail Website
Guest Editor
Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: the acceptance, adoption, and usability of technologies; the implementation and validation of technologies; the design and development of ICT-based platforms to monitor and use data analytics to predict healthy aging trajectories

Special Issue Information

Dear Colleagues,

Sensors are physical devices or technologies embedded in an environment that detect signals as part of a platform such as a mobile app. Sensors can be wearable or ambient and thus are user-friendly, in that they require minimal effort. A wide range of sensors can now have an impact on health outcomes across the lifespan, particularly among older adults. Such technologies have potential to maximize autonomy and independence, while minimizing risks to privacy. “Intelligent” care can enhance healthcare decisions while supporting service providers in their health interventions. Recent developments make sensors affordable, accessible, and versatile. However, there is minimal evidence on the implementation of these technologies in health with tangible outcomes.

Prof. Dr. Lili Liu
Dr. Antonio Miguel-Cruz
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

  • wearable sensors
  • ambient sensors
  • types of sensors (vision and imaging, radiation, temperature, motion, humidity, electrical contract, pressure, etc.)
  • Internet of Things (IoT)
  • apps (mobile applications)
  • zero-effort technology (ZET)
  • healthcare implementation
  • usability and adoption
  • technologies for indoor localization and tracking, e.g., Ultra-wide Band (UWB)
  • micro-electromechanical systems (MEMS) / nano-electromechanical systems (NEMS)

Published Papers (2 papers)

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Research

22 pages, 1683 KiB  
Article
Automatic Radar-Based Step Length Measurement in the Home for Older Adults Living with Frailty
by Parthipan Siva, Alexander Wong, Patricia Hewston, George Ioannidis, Jonathan Adachi, Alexander Rabinovich, Andrea W. Lee and Alexandra Papaioannou
Sensors 2024, 24(4), 1056; https://doi.org/10.3390/s24041056 - 06 Feb 2024
Viewed by 734
Abstract
With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place, predicting risk factors such as falls and hospitalization and providing early interventions are important. Much of the work [...] Read more.
With an aging population, numerous assistive and monitoring technologies are under development to enable older adults to age in place. To facilitate aging in place, predicting risk factors such as falls and hospitalization and providing early interventions are important. Much of the work on ambient monitoring for risk prediction has centered on gait speed analysis, utilizing privacy-preserving sensors like radar. Despite compelling evidence that monitoring step length in addition to gait speed is crucial for predicting risk, radar-based methods have not explored step length measurement in the home. Furthermore, laboratory experiments on step length measurement using radars are limited to proof-of-concept studies with few healthy subjects. To address this gap, a radar-based step length measurement system for the home is proposed based on detection and tracking using a radar point cloud followed by Doppler speed profiling of the torso to obtain step lengths in the home. The proposed method was evaluated in a clinical environment involving 35 frail older adults to establish its validity. Additionally, the method was assessed in people’s homes, with 21 frail older adults who had participated in the clinical assessment. The proposed radar-based step length measurement method was compared to the gold-standard Zeno Walkway Gait Analysis System, revealing a 4.5 cm/8.3% error in a clinical setting. Furthermore, it exhibited excellent reliability (ICC(2,k) = 0.91, 95% CI 0.82 to 0.96) in uncontrolled home settings. The method also proved accurate in uncontrolled home settings, as indicated by a strong consistency (ICC(3,k) = 0.81 (95% CI 0.53 to 0.92)) between home measurements and in-clinic assessments. Full article
(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)
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12 pages, 2906 KiB  
Article
Breaking the Fatigue Cycle: Investigating the Effect of Work-Rest Schedules on Muscle Fatigue in Material Handling Jobs
by Karla Beltran Martinez, Milad Nazarahari and Hossein Rouhani
Sensors 2023, 23(24), 9670; https://doi.org/10.3390/s23249670 - 07 Dec 2023
Viewed by 1511
Abstract
Muscle fatigue has proven to be a main factor in developing work-related musculoskeletal disorders. Taking small breaks or performing stretching routines during a work shift might reduce workers’ fatigue. Therefore, our objective was to explore how breaks and/or a stretching routine during a [...] Read more.
Muscle fatigue has proven to be a main factor in developing work-related musculoskeletal disorders. Taking small breaks or performing stretching routines during a work shift might reduce workers’ fatigue. Therefore, our objective was to explore how breaks and/or a stretching routine during a work shift could impact muscle fatigue and body kinematics that might subsequently impact the risk of work-related musculoskeletal disorder (WMSD) risk during material handling jobs. We investigated muscle fatigue during a repetitive task performed without breaks, with breaks, and with a stretching routine during breaks. Muscle fatigue was detected using muscle activity (electromyography) and a validated kinematic score measured by wearable sensors. We observed a significant reduction in muscle fatigue between the different work–rest schedules (p < 0.01). Also, no significant difference was observed between the productivity of the three schedules. Based on these objective kinematic assessments, we concluded that taking small breaks during a work shift can significantly reduce muscle fatigue and potentially reduce its consequent risk of work-related musculoskeletal disorders without negatively affecting productivity. Full article
(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Skeleton-based Privacy-Preserving Visual Sensor for Senior Care and Remote Patient Monitoring
Authors: Jie Liang; Andrew Au; Minghua Chen; Cyrus Chan; Jiannan Zheng; Zachary DeVries; Ying Xiao; and Paeton Dhesi
Affiliation: AltumView Systems Inc., Canada
Abstract: This paper introduces the AltumView Sentinare smart activity sensor, which uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only stick figure animations are transmitted instead of videos. The sensor received CES 2021 Innovation Award Honoree, and is one of only three fall detection devices selected by Amazon for its Alexa Together emergency service. We will discuss the features of the system, as well as lessons learned from its applications in different countries and different senior care settings.

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