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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: 31 December 2025 | Viewed by 12078

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)

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Published Papers (6 papers)

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Research

30 pages, 3684 KiB  
Article
EEG-Based Engagement Monitoring in Cognitive Games
by Yusuf Ahmed, Martin Ferguson-Pell, Kim Adams and Adriana Ríos Rincón
Sensors 2025, 25(7), 2072; https://doi.org/10.3390/s25072072 - 26 Mar 2025
Viewed by 443
Abstract
Cognitive decline and dementia prevention are global priorities, with cognitive rehabilitation games showing potential to delay their onset or progression. However, these games require sufficient user engagement to be effective. Assessing the engagement through questionnaires is challenging for the individuals suffering from cognitive [...] Read more.
Cognitive decline and dementia prevention are global priorities, with cognitive rehabilitation games showing potential to delay their onset or progression. However, these games require sufficient user engagement to be effective. Assessing the engagement through questionnaires is challenging for the individuals suffering from cognitive decline due to age or dementia. This study aims to explore the relationship between game difficulty levels, three EEG engagement indices (β/(θ + α), β/α, 1/α), and the self-reported flow state scale score during video gameplay, and to develop an accurate machine learning algorithm for the classification of user states into high- and low-engagement. Twenty-seven participants (nine older adults) played a stunt plane video game while their EEG signals were recorded using EPOCX. They also completed the flow state scale for occupational tasks questionnaire after the easy, optimal, and hard levels of gameplay. Self-reported engagement scores significantly varied across the difficulty levels (p = 0.027), with the optimal level yielding the highest scores. Combining the three EEG indices achieved the best performance, with F1 scores of 89% (within-subject) and 81% (cross-subject). Engagement classification F1 scores were 90% for young adults and 85% for older adults. The findings provide preliminary data that supports using EEG data for engagement analysis in adults and older adults. Full article
(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)
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25 pages, 4125 KiB  
Article
Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar
by Sara Nakatani, Mondher Bouazizi and Tomoaki Ohtsuki
Sensors 2024, 24(21), 6809; https://doi.org/10.3390/s24216809 - 23 Oct 2024
Viewed by 1459
Abstract
Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a [...] Read more.
Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar’s coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation. Full article
(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)
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14 pages, 1836 KiB  
Article
Using the Nintendo™ Wii to Improve Physical Function and Reduce the Risk of Falls in Older Adults: A Randomized Controlled Clinical Trial
by María del Carmen Carcelén-Fraile, Agustín Aibar-Almazán, Fidel Hita-Contreras, Marcelina Sánchez-Alcalá, Ana Belén Parra-Díaz, Aday Infante-Guedes and Yolanda Castellote-Caballero
Sensors 2024, 24(19), 6358; https://doi.org/10.3390/s24196358 - 30 Sep 2024
Viewed by 1715
Abstract
(1) Background: Numerous exercise programs that improve physical capacity and the risk of falls in older adults have been proposed with varying degrees of success. A novel approach may be to use a video game system that uses real-time force feedback to train [...] Read more.
(1) Background: Numerous exercise programs that improve physical capacity and the risk of falls in older adults have been proposed with varying degrees of success. A novel approach may be to use a video game system that uses real-time force feedback to train older adults. The aim of this study was to evaluate the effects of a Nintendo™ Wii-based exercise program on physical function and risk of falls in older people. (2) Methods: This 12-week randomized controlled clinical trial involved 73 participants: 36 individuals participating in a control group (CG) and 37 in an experimental group (EG) participating in a combined program. Balance was measured using the Tinetti scale, flexibility was assessed with the back scratch test and the sit-and-reach test, and lower body strength was assessed with the 30 s chair stand-up test. (3) Results: The results of this study show significant improvements in balance, gait, flexibility, and strength of the lower limbs compared to a control group. (4) Conclusions: A Nintendo™ Wii-based exercise program for seniors produces improvements in the physical health of older adults. These improvements highlight the importance of integrating physical exercise through video games as an effective strategy to improve the general health and quality of life of older adults. Full article
(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)
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18 pages, 6115 KiB  
Article
Automated Detection of In-Home Activities with Ultra-Wideband Sensors
by Arsh Narkhede, Hayden Gowing, Tod Vandenberg, Steven Phan, Jason Wong and Andrew Chan
Sensors 2024, 24(14), 4706; https://doi.org/10.3390/s24144706 - 20 Jul 2024
Viewed by 1550
Abstract
As Canada’s population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an [...] Read more.
As Canada’s population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring. Full article
(This article belongs to the Special Issue Independent Living: Sensor-Assisted Intelligent Care and Healthcare)
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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 - 6 Feb 2024
Cited by 1 | Viewed by 1743
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 - 7 Dec 2023
Cited by 4 | Viewed by 4510
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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