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Sensors
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Sensors for Unsupervised Mobility Assessment and Rehabilitation
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Sensors for Unsupervised Mobility Assessment and Rehabilitation

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2108

Special Issue Editor

Dr. Philippe Terrier

E-Mail Website
Guest Editor
Haute-Ecole Arc Santé, University of Applied Sciences and Arts Western Switzerland, 2000 Neuchâtel, Switzerland
Interests: inertial sensors; nonlinear gait analysis; aging; fall risk

Special Issue Information

Dear Colleagues,

Mobility deficits are common due to factors like aging, musculoskeletal issues, neurological disorders, and trauma. These impairments affect daily activities and quality of life. Traditional gait assessments in supervised settings (laboratories or clinics) may miss critical events occurring sporadically or over time—such as mobility fluctuations, rare instability episodes, or responses to interventions.

Advancements in sensor technology now enable ecological mobility assessments through continuous monitoring in daily environments. Unsupervised assessments enhance the detection of clinically relevant events and empower individuals by involving them in their care through personal devices like smartphones and wearables.

This Special Issue explores innovations in sensors for unsupervised mobility assessment in rehabilitation. We invite contributions on how these technologies improve diagnostic accuracy, personalize rehabilitation, and enhance outcomes for those with mobility impairments.

We welcome the submission of original research articles, review papers, and case studies that have not been published elsewhere.

Dr. Philippe Terrier
Guest Editor

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

  • ecological assessment methods in mobility
  • unsupervised mobility assessment in aging populations and older adults
  • wearable sensors and mobile health devices
  • data processing and algorithms for unsupervised data
  • remote monitoring and tele-rehabilitation systems
  • patient engagement through personal sensing devices
  • addressing challenges in data privacy and security

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Published Papers (2 papers)

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Research

16 pages, 2378 KiB  
Article
Detection and Severity Assessment of Parkinson’s Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks
by Sayyed Mostafa Mostafavi, Shovito Barua Soumma, Daniel Peterson, Shyamal H. Mehta and Hassan Ghasemzadeh
Sensors 2025, 25(11), 3421; https://doi.org/10.3390/s25113421 - 29 May 2025
Viewed by 365
Abstract
Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of [...] Read more.
Parkinson’s disease (PD) is the second-most common neurodegenerative disease. With more than 20,000 new diagnosed cases each year, PD affects millions of individuals worldwide and is most prevalent in the elderly population. The current clinical methods for the diagnosis and severity assessment of PD rely on the visual and physical examination of subjects and identifying key disease motor signs and symptoms such as bradykinesia, rigidity, tremor, and postural instability. In the present study, we developed a method for the diagnosis and severity assessment of PD using Gramian Angular Fields (GAFs) in combination with deep Convolutional Neural Networks (CNNs). Our model was applied to PD gait signals captured using pressure sensors embedded into insoles. Our results indicated an accuracy of 98.6%, a true positive rate (TPR) of 99.2%, and a true negative rate (TNR) of 98.5%, showcasing superior classification performance for PD diagnosis compared to the methods used in recent studies in the literature. The estimation of disease severity scores using gait signals showed a high accuracy for the Hoehn and Yahr score as well as the Timed Up and Go (TUG) test score (R2 > 0.8), while we achieved a lower prediction performance for the Unified Parkinson’s Disease Rating Scale (UPDRS) and its motor component (UPDRSM) scores (R2 < 0.2). These results were achieved using gait signals recorded in time windows as small as 10 s, which may pave the way for shorter, more accessible assessment tools for diagnosis and severity assessment of PD. Full article
(This article belongs to the Special Issue Sensors for Unsupervised Mobility Assessment and Rehabilitation)
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23 pages, 1286 KiB  
Article
Validity of Linear and Nonlinear Measures of Gait Variability to Characterize Aging Gait with a Single Lower Back Accelerometer
by Sophia Piergiovanni and Philippe Terrier
Sensors 2024, 24(23), 7427; https://doi.org/10.3390/s24237427 - 21 Nov 2024
Cited by 2 | Viewed by 1289
Abstract
The attractor complexity index (ACI) is a recently developed gait analysis tool based on nonlinear dynamics. This study assesses ACI’s sensitivity to attentional demands in gait control and its potential for characterizing age-related changes in gait patterns. Furthermore, we compare ACI with classical [...] Read more.
The attractor complexity index (ACI) is a recently developed gait analysis tool based on nonlinear dynamics. This study assesses ACI’s sensitivity to attentional demands in gait control and its potential for characterizing age-related changes in gait patterns. Furthermore, we compare ACI with classical gait metrics to determine its efficacy relative to established methods. A 4 × 200 m indoor walking test with a triaxial accelerometer attached to the lower back was used to compare gait patterns of younger (N = 42) and older adults (N = 60) during normal and metronome walking. The other linear and non-linear gait metrics were movement intensity, gait regularity, local dynamic stability (maximal Lyapunov exponents), and scaling exponent (detrended fluctuation analysis). In contrast to other gait metrics, ACI demonstrated a specific sensitivity to metronome walking, with both young and old participants exhibiting altered stride interval correlations. Furthermore, there was a significant difference between the young and old groups (standardized effect size: −0.77). Additionally, older participants exhibited slower walking speeds, a reduced movement intensity, and a lower gait regularity. The ACI is likely a sensitive marker for attentional load and can effectively discriminate age-related changes in gait patterns. Its ease of measurement makes it a promising tool for gait analysis in unsupervised (free-living) conditions. Full article
(This article belongs to the Special Issue Sensors for Unsupervised Mobility Assessment and Rehabilitation)
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