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Wearable Sensors for Gait, Human Motion and Health Monitoring
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Wearable Sensors for Gait, Human Motion and Health Monitoring

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

Deadline for manuscript submissions: 30 August 2026 | Viewed by 1062

Special Issue Editor

Dr. Nicola Carbonaro

E-Mail Website
Guest Editor
Information Engineering Department and Research Center \"E. Piaggio”, University of Pisa, Largo L. Lazzerino, 1, 56122 Pisa, Italy
Interests: wearable sensors; human movement reconstruction; inertial sensors; rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of wearable sensing technologies is reshaping the monitoring and assessment of gait, human motion, and health-related parameters. This Special Issue aims to highlight recent progress in wearable sensors, smart sensing systems, and data-driven methodologies for continuous, remote, and personalized health monitoring. In the context of telemedicine and precision medicine, wearable devices enable unobtrusive, real-time acquisition of motion and physiological data, supporting early disease detection, rehabilitation monitoring, and individualized clinical decision-making.

This Special Issue welcomes original research and review articles addressing novel wearable sensor designs, multimodal sensing and data fusion strategies, and artificial intelligence (AI)-based algorithms for motion analysis and predictive health assessment. Particular emphasis is placed on robust and scalable solutions for real-world deployment, including energy-efficient architectures, long-term monitoring, and the translation of laboratory-based methods into daily-life and clinical environments.

Topics of interest include, but are not limited to, remote gait and posture analysis, activity recognition, early diagnosis and monitoring of movement disorders, rehabilitation and assistive technologies, digital biomarkers, and AI-enabled health analytics. By fostering interdisciplinary collaboration among sensor engineering, biomechanics, data science, and clinical research, this Special Issue seeks to advance next-generation wearable sensing technologies for accurate, reliable, and intelligent human motion and health monitoring.

Dr. Nicola Carbonaro
Guest Editor

Manuscript Submission Information

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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
  • gait analysis
  • human motion analysis
  • health monitoring
  • precision medicine
  • artificial intelligence
  • multimodal sensing
  • sensor fusion
  • digital biomarkers

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Published Papers (2 papers)

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16 pages, 1882 KB  
Article
Self-Powered Triboelectric Insole for Gait Asymmetry and Plantar Pressure Signatures in Rehabilitation Patients: A Cross-Sectional Study
by Perizat Kanabekova, Adeliya Anash, Pedro Morouco, Bekzhan Pirmakhanov and Gulnur Kalimuldina
Sensors 2026, 26(10), 3191; https://doi.org/10.3390/s26103191 - 18 May 2026
Viewed by 269
Abstract
(1) Background: Gait analysis technologies have advanced; however, traditional systems like optical motion capture are lab-bound and costly, limiting rehabilitation monitoring. This cross-sectional study evaluates self-powered triboelectric nanogenerator (TENG) insoles combined with IMU sensors to assess gait asymmetry, plantar pressure signatures, age effects [...] Read more.
(1) Background: Gait analysis technologies have advanced; however, traditional systems like optical motion capture are lab-bound and costly, limiting rehabilitation monitoring. This cross-sectional study evaluates self-powered triboelectric nanogenerator (TENG) insoles combined with IMU sensors to assess gait asymmetry, plantar pressure signatures, age effects and injury history in rehabilitation patients, aiming to enable portable, battery-free phenotyping. (2) Methods: Fifty-three patients (22 females, 31 males; age, 29 ± 26 years) from Astana clinics with trauma histories (e.g., spine, ankle, fractures) and 10 healthy references underwent a 2 min walk test (2MWT). TENG insoles captured plantar loading; ankle/knee IMUs measured spatiotemporal parameters (cadence, asymmetry). The data were normalized; the analyses used an ANOVA and correlations (Python 3.14.3). (3) Results: The TENG sensors showed force/frequency linearity (up to 10 V at 20 N). The cadence averaged 101 ± 10 steps/min, declining with age (r = −0.31, p = 0.03) and fractures (r = −0.23, p = 0.04). The asymmetry varied (−54% to +31%) without category differences. Flatfoot (55%) was linked to lateral loading shifts; condition-specific waveform signatures emerged (e.g., lateral heel in ankle issues). (4) TENG-IMU systems feasibly capture gait phenotypes in heterogeneous cohorts, supporting out-of-lab monitoring for personalized rehabilitation without batteries. Prospective validation is required for further practical implications. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion and Health Monitoring)
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13 pages, 1260 KB  
Article
An Exploratory Analysis of Postural Control in People with Type 2 Diabetes Mellitus Using a Smartphone IMU Sensor
by Trine Rolighed Thomsen, Sophia Pölhöšová, Asger Ahlmann Bech, Aksayan Arunanthy Mahalingasivam, Nicklas Højgaard-Hessellund Rasmussen and Anderson Souza Oliveira
Sensors 2026, 26(9), 2899; https://doi.org/10.3390/s26092899 - 6 May 2026
Viewed by 523
Abstract
Background: There is a growing need for highly accessible and simplified methods to track postural control in adults affected by neurodegenerative diseases. Therefore, the aim of this study was to assess the validity of smartphone-derived postural control analyses compared with traditional center-of-pressure (COP) [...] Read more.
Background: There is a growing need for highly accessible and simplified methods to track postural control in adults affected by neurodegenerative diseases. Therefore, the aim of this study was to assess the validity of smartphone-derived postural control analyses compared with traditional center-of-pressure (COP) measures in healthy adults and people with type 2 diabetes mellitus (T2DM). Methods: A total of 36 participants (21 controls, 15 T2DM) completed static postural testing during single- and double-leg stance, also with eyes open and eyes closed. Data from a smartphone attached to the lower back measured trunk acceleration (SP-ACC) concurrently with gold-standard center of pressure (COP). The root mean square (RMS) and movement velocity (MV) were extracted from both trunk acceleration and COP data. The effect of balance condition and groups were statistically evaluated using non-parametric statistical tests. Results: SP-ACC and COP metrics showed progressive sway increases with task difficulty in both groups (all p < 0.001). RMS-ACC demonstrated moderate-to-strong correlations with RMS-COP across conditions (r = 0.55–0.90). Compared with controls, the T2DM group exhibited significantly higher RMS-ACC in DLS-EC and SLS-EO (both p < 0.01) and higher MV-ACC in DLS-EO, SLS-EO, and SLS-EC (p = 0.04–<0.001), reflecting impaired postural control. Conclusions: Smartphone-based IMU assessments showed good agreement with COP analysis and detected condition-specific balance deficits in T2DM. These findings support smartphone-based IMU metrics as a promising tool for accessible and scalable balance screening in diabetes care. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion and Health Monitoring)
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