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Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition

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

Deadline for manuscript submissions: closed (20 February 2026) | Viewed by 13883

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Dr. Alessandro Leone

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International Center for the Assessment of Nutritional Status (ICANS), Department of Food Environmental and Nutritional Sciences (DeFENS), University of Milan, Via Mangiagalli 25, 20133 Milan, Italy
Interests: food consumption; dietary pattern; Mediterranean diet; body composition; obesity; metabolic syndrome; eating behavior; nutritional epidemiology; plant-based foods
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Dr. Gabriele Rescio

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National Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Interests: ambient assisted living; active & healthy ageing technologies; wearable sensors; signal processing; artificial intelligence
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Dear Colleagues,

Human motion analysis and gait analysis have traditionally been performed in laboratories under controlled conditions using expensive equipment. Wearable sensors present an easy-to-use and cheap way to perform both gait and human motion analysis, including in healthcare scenarios, where monitoring is vital.

Wearable sensors are an increasingly popular method for the quantification of performance and workload with mechanical and physiological parameters. A wide range of wearable sensors are commercially available, and when applied to gait analysis or motion analysis, they can provide kinetic and kinematic features, thus representing useful tools for clinicians, researchers and caregivers in real-life contexts.

Wearable smart devices and services can be applied in microelectronics, new sensing technologies and materials, transducers, signal processing, big data, cloud computing and artificial intelligence tools, making them attractive in biomechanics contexts for both real-life and real-time analysis.

This Special Issue will study the design, implementation, testing, benchmarking and use of wearable sensors and related infrastructures and services, including ambient assisted living, ambient intelligence and IoT paradigms, and reframe the sense of “Smart Living” to ensure inclusion, safety, comfort, high-quality care and healthcare and environmental sustainability. The Special Issue will cover technological issues related to the integration of hardware and processing aspects into wearable smart devices for motion analysis and health monitoring, including mobile, edge, fog and cloud computing.

We invite papers that include, among others, the following topics:

Posture and gait analysis;
Human daily motion analysis;
Gait analysis of elderly and disabled people;
Home care motion sensing and analysis;
Wearable sensors and related techniques for medical decision-making;
Wearable sensors and related techniques for motor diagnosis;
Wearable sensors and related techniques for human gait recognition;
Sensing technologies for ambulatory human motion analysis;
Advanced sensor signal processing;
Health monitoring systems;
Industry-related wearable sensors;
Innovative applications of wearable sensor systems.

Dr. Alessandro Leone
Dr. Gabriele Rescio
Guest Editors

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Keywords

wearable sensors
gait analysis
human motion
biomechanics
health monitoring
home care
assisted living
signal processing
cloud computing

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Related Special Issue

Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring in Sensors (12 articles)

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Research

33 pages, 9075 KB

Open AccessArticle

Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames

by Yuanzhi Zhuo, Adrian Pranata, Chi-Tsun Cheng and Toh Yen Pang

Sensors 2026, 26(8), 2500; https://doi.org/10.3390/s26082500 - 18 Apr 2026

Viewed by 222

Abstract

Knee joint moment is an important biomechanical parameter for sports assessment, rehabilitation monitoring, and human–machine interaction. However, direct measurement is often restricted to laboratory-based settings. Surface electromyography (sEMG) offers a non-invasive alternative for indirect joint moment estimation, but many existing deep learning models remain too computationally demanding for potential wearable edge deployment. To address this gap, this study proposes Topo2DCNN-LSTM, a lightweight two-dimensional (2D) convolutional neural network model, designed for sagittal-plane knee flexion moment estimation. The model used a feature-based sequential representation, transforming raw sEMG signals into compact Root Mean Square (RMS) feature frames. The input was processed by a lightweight 2D convolutional neural network (CNN) encoder and paired with long short-term memory (LSTM) units. The model was trained on a public walking dataset of healthy subjects with synchronized sEMG and joint kinetics at two treadmill speeds. When compared with selected deep learning baselines, the quantized model achieved a mean RMS Error of 0.088 ± 0.020 Nm/kg at 1.2 m/s and 0.114 ± 0.034 Nm/kg at 1.8 m/s. On a SparkFun Thing Plus–SAMD51, it achieved an average inference latency of 28 ms using 71,316 bytes of random-access memory (RAM) and 257,172 bytes of flash. These results support its use as a proof of concept for personalized unilateral knee moment estimation with isolated on-device inference feasibility under resource-constrained and limited walking conditions. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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18 pages, 2665 KB

Open AccessArticle

Dynamic Gait Stability Estimated Using One or Two Inertial Measurement Units Worn on the Human Body

by Haoyun Peng, Shogo Okamoto, Hiroki Watanabe and Yasuhiro Akiyama

Sensors 2026, 26(4), 1211; https://doi.org/10.3390/s26041211 - 12 Feb 2026

Cited by 1 | Viewed by 500

Abstract

The margin of stability (MoS) is a metric used to assess dynamic postural stability during walking. Although MoS is typically computed from optical motion capture data, previous studies have shown that MoS can be approximated from six-axis kinematic signals—linear acceleration and angular velocity—measured by inertial measurement units (IMUs). With IMU-equipped devices such as smartphones and smartwatches becoming widespread, it is increasingly common for individuals to carry two or more such devices in daily life. This study aimed to identify combinations of two body locations that most effectively predict MoS. IMU sensors were attached to ten body locations while participants walked on a treadmill. Principal motion analysis, a reductive regression method for multidimensional time-series data, was employed for MoS prediction, and cross-validation was used for reliable model evaluation. Appropriate combinations of two IMU sensors achieved mean errors of approximately 30 mm and 11 mm in anterior and mediolateral MoS, respectively, compared with reference values derived from optical motion capture. These errors were comparable to the intrinsic standard deviations of MoS, suggesting that IMU-based MoS estimation is sufficiently accurate for the classification of individuals at high fall risk. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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14 pages, 6436 KB

Open AccessArticle

Development and Validation of an Algorithm for Foot Contact Detection in High-Dynamic Sports Movements Using Inertial Measurement Units

by Stefano Di Paolo, Margherita Mendicino, José Miguel Palha de Araújo dos Santos, Eline Nijmeijer, Pieter Heuvelmans, Francesco Della Villa, Alli Gokeler, Anne Benjaminse and Stefano Zaffagnini

Sensors 2026, 26(3), 988; https://doi.org/10.3390/s26030988 - 3 Feb 2026

Viewed by 658

Abstract

Precise foot contact detection (FCD) is essential for accurate biomechanical analysis in sport performance, injury prevention, and rehabilitation. This study developed and validated an inertial measurement units (IMUs)-based algorithm for FCD during sports movements. Thirty-four healthy athletes (22.8 ± 4.1 years old) performed 90° changes of direction and sprints with deceleration. Data were collected via a force platform (AMTI, 1000 Hz) and a full-body IMU suit (MTw Awinda, Movella, 60 Hz). Two IMU-based algorithms relying on pelvis vertical velocity (PVV) and resultant foot acceleration (RFA), respectively, were tested to detect initial contact (IC) and toe-off (TO). Force platform data served as the gold standard for comparison. Agreement was quantified through median offset and interquartile range (IQR); the influence of task, sex, leg, speed, and acceleration was investigated. The PVV algorithm showed higher offset than RFA for IC detection (16.7 ms vs. 10.2 ms) with comparable IQR and a substantially higher offset for TO (102.8 ms vs. 20.4 ms). Minimal influence of co-factors emerged (variance < 10%). Results were sensibly improved by combining PVV and RFA, for both IC (5.6 [70.4] ms) and TO (20.4 [78.7] ms). This algorithm offers a robust, portable alternative to force platforms, enabling accurate footstep detection and analysis of complex, sports movements in real-world environments, enhancing the ecological validity of sport assessments. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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15 pages, 1587 KB

Open AccessArticle

Accuracy and Inter-Subject Variability of Gait Event Detection Methods Based on Optical and Inertial Motion Capture

by Vinicius Cavassano Zampier, Morten Bilde Simonsen, Fabio Augusto Barbieri and Anderson Souza Oliveira

Sensors 2025, 25(24), 7652; https://doi.org/10.3390/s25247652 - 17 Dec 2025

Viewed by 949

Abstract

Gait events (instant of heel strikes and instant of toe-offs) are essential for extracting spatiotemporal parameters and segmenting biological signals (electromyography (EMG) and electroencephalography (EEG)) based on gait cycle. While force platforms and optical motion capture (OMC) are ideal for identifying GE, inertial measurement units (IMUs) are more applicable. This study compared the accuracy and variability from IMU- and OMC-based gait event detection methods compared with gold-standard ground reaction force (GRF) detection. Seventeen healthy adults (31 ± 8 years) walked along a 10 m walkway instrumented with force plates. Foot kinematics were recorded using two retro-reflective markers on each foot and an IMU on the sacrum. Gait events were identified using two OMC-based (OMC1, OMC2) and two IMU-based (IMU1, IMU2) algorithms. Accuracy was evaluated using root-mean-square error (RMSE) relative to GRF, and within-subject variability was assessed using coefficient of variation (CoV). The results from the instant of heel strikes, OMC1 yielded a lower RMSE (14 ms) than IMU1 (50 ms) and IMU2 (61 ms) (p < 0.001). For the instant of toe-offs, OMC1 demonstrated a lower RMSE (17 ms), differing from IMU1 (54 ms) and IMU2 (74 ms) (p < 0.001). IMU2 exhibited greatest variability (CoV = 24 ms) compared with OMC1 (7 ms) and IMU1 (9 ms) (p < 0.001). Our results highlight lower accuracy and higher variability in gait event detection using sacrum-mounted IMUs. Despite its convenience, researchers should consider the limitations of using IMUs for EMG/EEG data segmentation. Future studies validating gait event detection methods should report some type of variability metric. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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14 pages, 827 KB

Open AccessArticle

Sensor Fusion for Enhancing Motion Capture: Integrating Optical and Inertial Motion Capture Systems

by Hailey N. Hicks, Howard Chen and Sara A. Harper

Sensors 2025, 25(15), 4680; https://doi.org/10.3390/s25154680 - 29 Jul 2025

Cited by 3 | Viewed by 2690

Abstract

This study aimed to create and evaluate an optimization-based sensor fusion algorithm that combines Optical Motion Capture (OMC) and Inertial Motion Capture (IMC) measurements to provide a more efficient and reliable gap-filling process for OMC measurements to be used for future research. The proposed algorithm takes the first and last frame of OMC data and fills the rest with gyroscope data from the IMC. The algorithm was validated using data from twelve participants who performed a hand cycling task with an inertial measurement unit (IMU) placed on their hand, forearm, and upper arm. The OMC tracked a cluster of reflective markers that were placed on top of each IMU. The proposed algorithm was evaluated with simulated gaps of up to five minutes. Average total root-mean-square errors (RMSE) of <1.8° across a 5 min duration were observed for all sensor placements for the cyclic upper limb motion pattern used in this study. The results demonstrated that the fusion of these two sensing modalities is feasible and shines light on the possibility of more field-based studies for human motion analysis. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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17 pages, 4555 KB

Open AccessArticle

Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment

by Gabriele Rescio, Elisa Sciurti, Lucia Giampetruzzi, Anna Maria Carluccio, Luca Francioso and Alessandro Leone

Sensors 2025, 25(5), 1618; https://doi.org/10.3390/s25051618 - 6 Mar 2025

Cited by 6 | Viewed by 3230

Abstract

Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require precise placement by trained personnel. Wearable sEMG systems integrating textile electrodes have been proposed to improve usability; however, they often suffer from poor skin–electrode coupling, leading to higher impedance, motion artifacts, and reduced signal quality. To address these limitations, we propose a preliminary model of smart socks, integrating biocompatible hybrid polymer electrodes positioned over the target muscles. Compared with commercial Ag/AgCl electrodes, these hybrid electrodes ensure lower the skin–electrode impedance, enhancing signal acquisition (19.2 ± 3.1 kΩ vs. 27.8 ± 4.5 kΩ for Ag/AgCl electrodes). Moreover, to the best of our knowledge, this is the first wearable system incorporating hydrogel-based electrodes in a sock specifically designed for the analysis of lower limb muscles, which are crucial for evaluating conditions such as sarcopenia, fall risk, and gait anomalies. The system incorporates a lightweight, wireless commercial module for data pre-processing and transmission. sEMG signals from the Gastrocnemius and Tibialis muscles were analyzed, demonstrating a strong correlation (R = 0.87) between signals acquired with the smart socks and those obtained using commercial Ag/AgCl electrodes. Future studies will further validate its long-term performance under real-world conditions and with a larger dataset. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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18 pages, 4518 KB

Open AccessArticle

Running Parameter Analysis in 400 m Sprint Using Real-Time Kinematic Global Navigation Satellite Systems

by Keisuke Onodera, Naoto Miyamoto, Kiyoshi Hirose, Akiko Kondo, Wako Kajiwara, Hiroshi Nakano, Shunya Uda and Masaki Takeda

Sensors 2025, 25(4), 1073; https://doi.org/10.3390/s25041073 - 11 Feb 2025

Cited by 1 | Viewed by 2710

Abstract

Accurate measurement of running parameters, including the step length (SL), step frequency (SF), and velocity, is essential for optimizing sprint performance. Traditional methods, such as 2D video analysis and inertial measurement units (IMUs), face limitations in precision and practicality. This study introduces and evaluates two methods for estimating running parameters using real-time kinematic global navigation satellite systems (RTK GNSS) with 100 Hz sampling. Method 1 identifies mid-stance phases via vertical position minima, while Method 2 aligns with the initial contact (IC) events through vertical velocity minima. Two collegiate sprinters completed a 400 m sprint under controlled conditions, with RTK GNSS measurements validated against 3D video analysis and IMU data. Both methods estimated the SF, SL, and velocity, but Method 2 demonstrated superior accuracy, achieving a lower RMSE (SF: 0.205 Hz versus 0.291 Hz; SL: 0.143 m versus 0.190 m) and higher correlation with the reference data. Method 2 also exhibited improved performance in curved sections and detected stride asymmetries with higher consistency than Method 1. These findings highlight RTK GNSS, particularly the velocity minima approach, as a robust, drift-free, single-sensor solution for detailed per-step sprint analysis in outdoor conditions. This approach offers a practical alternative to IMU-based methods and enables training optimization and performance evaluation. Full article

(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)

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