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Human-Centered Solutions for Ambient Assisted Living
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Human-Centered Solutions for Ambient Assisted Living

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

Deadline for manuscript submissions: 20 June 2025 | Viewed by 5595

Special Issue Editors

Dr. Silvia Imbesi

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Guest Editor
Department of Architecture, University of Ferrara, 44121 Ferrara, Italy
Interests: inclusive design; smart devices; design methodologies
Prof. Dr. Sofia Scataglini

E-Mail Website
Guest Editor
Department of Rehabilitation Sciences and Physiotherapy, Center for Health and Technology (CHaT), Faculty of Medicine and Health Sciences, University of Antwerp, 2000 Antwerpen, Belgium
Interests: human factors and ergonomics; AI in healthcare; digital human modeling; smart health; biomechanics; neuroengineering; cognitive modelling; computer vision; standardization; integrated product development; design for all
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giacinto Barresi

E-Mail Website
Guest Editor
Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, UK
Interests: neuroergonomics; biomedical robotics; human–robot interaction; human augmentation; rehabilitation technology; assistive technology; prosthetics; extended reality; digital health; gamification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue presents advances in human-Centered approaches for innovative solutions of ambient assisted living in healthcare and wellbeing.

Current advances in sensor technologies are opening new perspectives in the design of smart devices and spaces, enhancing the autonomy and quality of life of specific categories of fragile users needing constant personalized assistance.

Objects and environments enhanced with digital technologies allow the analysis and the recognition of a person’s habits, preferences, behaviors, and health status, making it possible for effective personalization of the developed solutions. Ambient assisted living looks to individual comfort, autonomy, and healthcare, using emerging technologies for the creation of smart living environments and promoting the engagement of people in taking advantage of smart solutions for daily monitoring, assistance, and medical care.

Moreover, the involvement of users in the design process, through co-design and other human-centered methodological practices, increases the level of acceptance, usability, and accessibility of the designed devices and services.

This Special Issue aims to collect accounts of recent advances in ambient assisted living with innovative applications in different research fields and novel methodologies for integrating quantitative and qualitative information aimed toward the satisfaction of people's needs. Both research papers and review articles submissions are welcome.

Dr. Silvia Imbesi
Dr. Sofia Scataglini
Prof. Dr. Giacinto Barresi
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

  • smart homes
  • smart environments, home automation,
  • activity monitoring devices and systems
  • biomonitoring and activity recognition
  • human-machine interaction and interfaces
  • human-environment interaction and interfaces
  • human-system interaction and interfaces
  • IoT and smart sensors in healthcare
  • mhealth and/or ehealth solutions for fragile users
  • sensors for assessing health and wellbeing
  • wearables and smart clothes
  • assistive robotics and social robotics
  • digital human models and digital twins for healthcare
  • connected care, digital health, phygital health
  • ubiquitous technology, pervasive technology

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Published Papers (2 papers)

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Research

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16 pages, 5147 KiB  
Article
FP-GCN: Frequency Pyramid Graph Convolutional Network for Enhancing Pathological Gait Classification
by Xiaoheng Zhao, Jia Li and Chunsheng Hua
Sensors 2024, 24(11), 3352; https://doi.org/10.3390/s24113352 - 23 May 2024
Viewed by 718
Abstract
Gait, a manifestation of one’s walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual’s health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in [...] Read more.
Gait, a manifestation of one’s walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual’s health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in inefficiencies in pathological gait classification. In this paper, we propose a Frequency Pyramid Graph Convolutional Network (FP-GCN), advocating to complement temporal analysis and further enhance spatial feature extraction. specifically, a spectral decomposition component is adopted to extract gait data with different time frames, which can enhance the detection of rhythmic patterns and velocity variations in human gait and allow a detailed analysis of the temporal features. Furthermore, a novel pyramidal feature extraction approach is developed to analyze the inter-sensor dependencies, which can integrate features from different pathways, enhancing both temporal and spatial feature extraction. Our experimentation on diverse datasets demonstrates the effectiveness of our approach. Notably, FP-GCN achieves an impressive accuracy of 98.78% on public datasets and 96.54% on proprietary data, surpassing existing methodologies and underscoring its potential for advancing pathological gait classification. In summary, our innovative FP-GCN contributes to advancing feature extraction and pathological gait recognition, which may offer potential advancements in healthcare provisions, especially in regions with limited access to medical resources and in home-care environments. This work lays the foundation for further exploration and underscores the importance of remote health monitoring, diagnosis, and personalized interventions. Full article
(This article belongs to the Special Issue Human-Centered Solutions for Ambient Assisted Living)
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Review

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34 pages, 1347 KiB  
Review
Effect of Music Based Therapy Rhythmic Auditory Stimulation (RAS) Using Wearable Device in Rehabilitation of Neurological Patients: A Systematic Review
by Sofia Scataglini, Zala Van Dyck, Véronique Declercq, Gitte Van Cleemput, Nele Struyf and Steven Truijen
Sensors 2023, 23(13), 5933; https://doi.org/10.3390/s23135933 - 26 Jun 2023
Cited by 3 | Viewed by 4084
Abstract
(1) Background: Even though music therapy is acknowledged to have positive benefits in neurology, there is still a lack of knowledge in the literature about the applicability of music treatments in clinical practice with a neurological population using wearable devices. (2) Methods: a [...] Read more.
(1) Background: Even though music therapy is acknowledged to have positive benefits in neurology, there is still a lack of knowledge in the literature about the applicability of music treatments in clinical practice with a neurological population using wearable devices. (2) Methods: a systematic review was conducted following PRISMA 2020 guidelines on the 29 October 2022, searching in five databases: PubMed, PEDro, Medline, Web of Science, and Science Direct. (3) Results: A total of 2964 articles were found, including 413 from PubMed, 248 from Web of Science, 2110 from Science Direct, 163 from Medline, and none from PEDro. Duplicate entries, of which there were 1262, were eliminated. In the first screening phase, 1702 papers were screened for title and abstract. Subsequently, 1667 papers were removed, based on population, duplicate, outcome, and poor study design. Only 15 studies were considered after 35 papers had their full texts verified. Results showed significant values of spatiotemporal gait parameters in music-based therapy rhythmic auditory stimulation (RAS), including speed, stride length, cadence, and ROM. (4) Conclusions: The current findings confirm the value of music-based therapy RAS as a favorable and effective tool to implement in the health care system for the rehabilitation of patients with movement disorders. Full article
(This article belongs to the Special Issue Human-Centered Solutions for Ambient Assisted Living)
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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: Smartphone as a Sensor: Overview, SWOT Analysis, and Proposal of Mobile Biomarkers
Authors: Alessio Antonini; Giacinto Barresi
Affiliation: Knowledge Media Institute, The Open University, Milton Keynes, UK; Bristol Robotics Laboratory (BRL) University of the West of England (UWE)
Abstract: Despite the proven results, digital health applications for supporting management struggle with adoption scale. Through an overview of the literature, this paper explores the hypothesis that costs related to the need for active use and extra sensor devices like smartwatches are, at present, contributing negatively to attrition, particularly in the prevention and monitoring of life-long conditions. As an alternative, smartphone passive monitoring could provide a viable strategy for life-long use, removing use and hardware-related costs - also exploiting the synergies between mobile health (mHealth) and Ambient Assisted Living (AAL). However, smartphone sensor tool kits are not tuned for diagnostics and, in general, their quality can greatly sway based on the model, maker, and generation. To this end, this contribution presents a meta-review of the current status of smartphone passive monitoring, clarifying the strengths, weaknesses, opportunities, and threats (SWOT analysis) of this approach, pervasively encompassing digital health, mHealth, and AAL. The result is then consolidated into a set of newly defined mobile biomarkers that abstract sensors and computational techniques that address the needs of supporting self-management.

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