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Sensor Systems for Gesture Recognition (3rd Edition)

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

Deadline for manuscript submissions: closed (20 January 2026) | Viewed by 34631

Special Issue Editor

Prof. Dr. Giovanni Saggio

E-Mail Website
Guest Editor
Department of Electronic Engineering, University of Tor Vergata Rome, 00133 Rome, Italy
Interests: wearable sensors; brain–computer interface; motion tracking; gait analysis; sensory glove; biotechnologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Gesture recognition (GR) aims to interpret human gestures by means of math algorithms. Its achievement will have widespread applications in a number of different fields, with impacts that can help or meaningfully improve our quality of life.

In the real world, GR can interpret communication meanings at a distance or can “translate” sign language into written sentences or a synthetized voice. In a virtual reality (VR) and augmented reality (AR) world, GR enables navigation and/or interaction, for instance, with the user interface (UI) of a smart TV controlled by hand gestures.

The possible applications are countless, and we can mention just a few. In the health field, GR allows us to augment the motion capabilities of people with disabilities or to support surgeons in surgical settings. In gaming, GR frees gamers from input devices, such as their keyboards, mouse, and joysticks. In the automotive industry, GR allows drivers to control car appliances (see BMW 7 Series). In cinematography, GR is used to computer generate effects or creatures. In everyday life, GR is the means to interact with smartphone apps (see uSens, Inc. and Gestigon GmbH, for example). In human–robot interactions, GR keeps the operator in safe conditions, while his/her gestures become the remote commands for tele-operating a robot. GR facilitates music creation too, converting human movements into sounds.

GR is achieved through (1) data acquisition, (2) the identification of patterns, and (3) interpretation (each of these phases can consist of different stages).

Data can be acquired by means of sensor systems based on different measurement principles, such as mechanical, magnetic, optic, acoustic, and inertial principles, or hybrid sensors. Within this frame, optical technologies are historically the most explored ones (since 1870, when animal movements were analyzed via picture sequences) and represent the current state of the art. However, optical technologies are expensive and require a dedicated room and skilled personnel. Therefore, non-optical technologies, in particular those based on wearable sensors, are becoming increasingly more important.

In order to obtain GR, different methods can be adopted for data segmentation, feature extraction, and classification. These methods highly depend on the type of data (according to the adopted type of sensor system) and the type of gestures to be recognized.

The (supervised on unsupervised) recognition of patterns in data, i.e., regularities, arrangements, and characteristics, can be approached by machine learning or heuristics and can be linked to artificial intelligence (AI).

In sum, sensor systems for gesture recognition deal with an ensemble of topics that can singularly or jointly be accessed and that represent a great opportunity for further development, with widespread potential applications.

This call for papers invites technical contributions to a Sensors Special Issue providing an up-to-date overview on “Sensor Systems for Gesture Recognition”. This Special Issue will deal with theory, solutions, and innovative applications. Potential topics include, but are not limited to, the following:

  • Sensor systems;
  • Gesture recognition;
  • Gesture recognition technologies;
  • Gesture extraction methods;
  • Gesture detection sensors;
  • Wearable sensors;
  • Human tracking;
  • Human postures and movements;
  • Motion detection and tracking;
  • Hand gesture recognition;
  • Sign language recognition;
  • Gait analysis;
  • Remote controlling;
  • Pattern recognition for gesture recognition;
  • Machine learning for gesture recognition;
  • Applications of gesture recognition;
  • Algorithms for gesture recognition.

Prof. Dr. Giovanni Saggio
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

  • sensor systems
  • gesture recognition
  • gesture recognition technologies
  • gesture extraction methods
  • gesture detection sensors
  • wearable sensors
  • human tracking
  • human postures and movements
  • motion detection and tracking
  • hand gesture recognition
  • sign language recognition
  • gait analysis
  • remote controlling
  • pattern recognition for gesture recognition
  • machine learning for gesture recognition
  • applications of gesture recognition
  • algorithms for gesture recognition

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Related Special Issue

Published Papers (15 papers)

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Research

25 pages, 2277 KB  
Article
Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization
by Anjia Ye, Ananda Maiti, Matthew Schmidt and Scott J. Pedersen
Sensors 2026, 26(8), 2468; https://doi.org/10.3390/s26082468 - 17 Apr 2026
Viewed by 356
Abstract
Occupational sedentary behavior presents a public health risk, yet current interventions often rely on subjective self-reports or context-blind prompts. This study validates a privacy-preserving, edge-computing time-of-flight (ToF) sensor that detects postural states and quantifies therapeutic exercise gestures in real time. The dual-sensor architecture [...] Read more.
Occupational sedentary behavior presents a public health risk, yet current interventions often rely on subjective self-reports or context-blind prompts. This study validates a privacy-preserving, edge-computing time-of-flight (ToF) sensor that detects postural states and quantifies therapeutic exercise gestures in real time. The dual-sensor architecture distinguishes between sitting, standing, and absence, while capturing rapid sit-to-stand repetitions suitable for active-break interventions. In this paper, a laboratory study (N = 7) evaluated the system against ground truth comprising activPAL3 accelerometry and video analysis. Across 378 postural events, the sensor achieved high temporal fidelity (mean absolute error < 1.6 s) and 100% sensitivity in counting exercise repetitions. The system differentiated workstation occupancy from physical absence. These findings demonstrate that ToF sensing matches the accuracy of video analysis without privacy concerns while offering the contextual awareness required for just-in-time, adaptive workplace interventions. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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36 pages, 14443 KB  
Article
Personalized Wrist–Forearm Static Gesture Recognition Using the Vicara Kai Controller and Convolutional Neural Network
by Jacek Szedel
Sensors 2026, 26(5), 1700; https://doi.org/10.3390/s26051700 - 8 Mar 2026
Viewed by 376
Abstract
Predefined, user-independent gesture sets do not account for individual differences in movement patterns and physical limitations. This study presents a personalized wrist–forearm static gesture recognition system for human–computer interaction (HCI) using the Vicara KaiTM wearable controller and a convolutional neural network (CNN). [...] Read more.
Predefined, user-independent gesture sets do not account for individual differences in movement patterns and physical limitations. This study presents a personalized wrist–forearm static gesture recognition system for human–computer interaction (HCI) using the Vicara KaiTM wearable controller and a convolutional neural network (CNN). Unlike the system based on fixed, predefined gestures, the proposed approach enables users to define and train their own gesture sets. During gesture recording, users may either select a gesture pattern from a predefined prompt set or create their own natural, unprompted gestures. A dedicated software framework was developed for data acquisition, preprocessing, model training, and real-time recognition. The developed system was evaluated by optimizing the parameters of a lightweight CNN and examining the influence of sequentially applied changes to the input and network pipelines, including resizing the input layer, applying data augmentation, experimenting with different dropout ratios, and varying the number of learning samples. The performance of the resulting network setup was assessed using confusion matrices, accuracy, and precision metrics for both original gestures and gestures smoothed using the cubic Bézier function. The resulting validation accuracy ranged from 0.88 to 0.94, with an average test-set accuracy of 0.92 and macro precision of 0.92. The system’s resilience to rapid or casual gestures was also evaluated using the receiver operating characteristic (ROC) method, achieving an Area Under the Curve (AUC) of 0.97. The results demonstrate that the proposed approach achieves high recognition accuracy, indicating its potential for a range of practical applications. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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22 pages, 3288 KB  
Article
An Intelligent Real-Time System for Sentence-Level Recognition of Continuous Saudi Sign Language Using Landmark-Based Temporal Modeling
by Adel BenAbdennour, Mohammed Mukhtar, Osama Almolike, Bilal A. Khawaja and Abdulmajeed M. Alenezi
Sensors 2026, 26(5), 1652; https://doi.org/10.3390/s26051652 - 5 Mar 2026
Viewed by 626
Abstract
A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) [...] Read more.
A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) and the scarcity of real-time, sentence-level translation systems. This paper presents a real-time system for sentence-level recognition of continuous SSL and direct mapping to natural spoken Arabic. The proposed system operates end-to-end on live video streams or pre-recorded content, extracting spatio-temporal landmark features using the MediaPipe Holistic framework. For classification, the input feature vector consists of 225 features derived from hand and body pose landmarks. These features are processed by a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset to perform direct sentence-level classification over a vocabulary of 50 continuous Arabic sign language sentences, supported by an idle-based segmentation mechanism that enables natural, uninterrupted signing. Experimental evaluation demonstrates robust generalization: under a Leave-One-Signer-Out (LOSO) cross-validation protocol, the model attains a mean sentence-level accuracy of 94.2%, outperforming the fixed signer-independent split baseline of 92.07%, while maintaining real-time performance suitable for interactive use. To enhance linguistic fluency, an optional post-recognition refinement stage is incorporated using a large language model (LLM), followed by text-to-speech synthesis to produce audible Arabic output; this refinement operates strictly as post-processing and is not included in the reported recognition accuracy metrics. The results demonstrate that direct sentence-level modeling, combined with landmark-based feature extraction and real-time segmentation, provides an effective and practical solution for continuous SSL sentence recognition in real-time. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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21 pages, 4919 KB  
Article
A Wearable Haptic Feedback System for Arm-Swing Amplitude Modulation During Overground Walking in Older Adults
by Ines Khiyara, Ben Sidaway and Babak Hejrati
Sensors 2026, 26(5), 1532; https://doi.org/10.3390/s26051532 - 28 Feb 2026
Viewed by 497
Abstract
Reduced arm swing frequently occurs in older adults and is associated with declined gait performance. Experimental studies have demonstrated that restricting arm swing decreases stride length and walking speed, whereas deliberately increasing arm swing can improve these gait parameters. This study evaluated whether [...] Read more.
Reduced arm swing frequently occurs in older adults and is associated with declined gait performance. Experimental studies have demonstrated that restricting arm swing decreases stride length and walking speed, whereas deliberately increasing arm swing can improve these gait parameters. This study evaluated whether a wearable haptic feedback system could effectively increase arm-swing amplitude and assess its effects on spatiotemporal gait outcomes during overground walking. Using a within-subject repeated-measures design, twelve community-dwelling older adults (6 males/6 females; 75.8±6.5 years) completed three no-feedback conditions (Baseline, Exaggerated, Fast) and six feedback conditions varying Direction (Forward, Backward, Combined) and target Magnitude (+100%, +200% of the Baseline). The arm-swing angle was estimated in real time from upper-arm inertial measurement unit (IMU) sensors; targets were defined for peak Forward flexion and/or peak Backward extension, and vibrotactile cues were delivered when the corresponding peak failed to reach the target. The arm range of motion (ROM) increased significantly across conditions, with the largest increase during Feedback (+229%), exceeding Exaggerated (+120%) and Fast (+64%) (all p<0.001). Walking speed and stride length also increased during Feedback relative to the Baseline (p<0.001). Within feedback conditions, the arm ROM showed independent main effects of the Direction and Magnitude, whereas gait outcomes were primarily influenced by Direction. Arm-swing symmetry was largely preserved, with the smallest variability during Feedback. These findings support the feasibility of vibrotactile feedback to enhance arm swing and improve gait outcomes in older adults. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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20 pages, 7504 KB  
Article
A Novel Dataset for Gait Activity Recognition in Real-World Environments
by John C. Mitchell, Abbas A. Dehghani-Sanij, Shengquan Xie and Rory J. O’Connor
Sensors 2026, 26(3), 833; https://doi.org/10.3390/s26030833 - 27 Jan 2026
Viewed by 690
Abstract
Falls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled [...] Read more.
Falls are a prominent issue in society and the second leading cause of unintentional death globally. Traditional gait analysis is a process that can aid in identifying factors that increase a person’s risk of falling through determining their gait parameters in a controlled environment. Advances in wearable sensor technology and analytical methods such as deep learning can enable remote gait analysis, increasing the quality of the collected data, standardizing the process between centers, and automating aspects of the analysis. Real-world gait analysis requires two problems to be solved: high-accuracy Human Activity Recognition (HAR) and high-accuracy terrain classification. High accuracy HAR has been achieved through the application of powerful novel classification techniques to various HAR datasets; however, terrain classification cannot be approached in this way due to a lack of suitable datasets. In this study, we present the Context-Aware Human Activity Recognition (CAHAR) dataset: the first activity- and terrain-labeled dataset that targets a full range of indoor and outdoor terrains, along with the common gait activities associated with them. Data were captured using Inertial Measurement Units (IMUs), Force-Sensing Resistor (FSR) insoles, color sensors, and LiDARs from 20 healthy participants. With this dataset, researchers can develop new classification models that are capable of both HAR and terrain identification to progress the capabilities of wearable sensors towards remote gait analysis. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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27 pages, 80350 KB  
Article
Pose-Based Static Sign Language Recognition with Deep Learning for Turkish, Arabic, and American Sign Languages
by Rıdvan Yayla, Hakan Üçgün and Mahmud Abbas
Sensors 2026, 26(2), 524; https://doi.org/10.3390/s26020524 - 13 Jan 2026
Cited by 1 | Viewed by 1171
Abstract
Advancements in artificial intelligence have significantly enhanced communication for individuals with hearing impairments. This study presents a robust cross-lingual Sign Language Recognition (SLR) framework for Turkish, American English, and Arabic sign languages. The system utilizes the lightweight MediaPipe library for efficient hand landmark [...] Read more.
Advancements in artificial intelligence have significantly enhanced communication for individuals with hearing impairments. This study presents a robust cross-lingual Sign Language Recognition (SLR) framework for Turkish, American English, and Arabic sign languages. The system utilizes the lightweight MediaPipe library for efficient hand landmark extraction, ensuring stable and consistent feature representation across diverse linguistic contexts. Datasets were meticulously constructed from nine public-domain sources (four Arabic, three American, and two Turkish). The final training data comprises curated image datasets, with frames for each language carefully selected from varying angles and distances to ensure high diversity. A comprehensive comparative evaluation was conducted across three state-of-the-art deep learning architectures—ConvNeXt (CNN-based), Swin Transformer (ViT-based), and Vision Mamba (SSM-based)—all applied to identical feature sets. The evaluation demonstrates the superior performance of contemporary vision Transformers and state space models in capturing subtle spatial cues across diverse sign languages. Our approach provides a comparative analysis of model generalization capabilities across three distinct sign languages, offering valuable insights for model selection in pose-based SLR systems. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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14 pages, 2169 KB  
Article
Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm
by María Isabel Pavas Vivas, Diego Alejandro Arturo, Stefania Peñuela Arango, Jhon Alexander Quiñones-Preciado and Lessby Gomez-Salazar
Sensors 2026, 26(2), 360; https://doi.org/10.3390/s26020360 - 6 Jan 2026
Viewed by 898
Abstract
Background: The integration of markerless motion capture systems such as OpenCap with force platforms expands the possibilities of biomechanical analysis in low-cost environments; however, it requires robust temporal synchronization procedures in the absence of shared hardware triggers. Objective: To develop and validate an [...] Read more.
Background: The integration of markerless motion capture systems such as OpenCap with force platforms expands the possibilities of biomechanical analysis in low-cost environments; however, it requires robust temporal synchronization procedures in the absence of shared hardware triggers. Objective: To develop and validate an automatic synchronization algorithm based on heel kinematic events to align OpenCap data with force platform signals during lower-limb functional exercises. Methods: Thirty normal-weight adult women (18–45 years) were evaluated while performing between 11 and 14 functional tasks (60° and 90° squats, lunges, sliding variations, and step exercises), yielding 330 motion records. Kinematics were estimated using OpenCap (four iPhone 12 cameras at 60 Hz), and kinetics were recorded using BTS P6000 force platforms synchronized with an OptiTrack system (Gold Standard). The algorithm detected heel contact from the filtered vertical coordinate and aligned this event with the initial rise in vertical ground reaction force. Validation against the Gold Standard was performed in 20 squat repetitions (10 at 60° and 10 at 90°) using Pearson correlation, RMSE, and MAE of the time-normalized and amplitude-normalized (0–1) vertical ground reaction force (vGRF). Results: The algorithm successfully synchronized 92.5% of the 330 records; the remaining cases showed kinematic noise or additional steps that prevented robust event detection. During validation, correlations were r = 0.85 (60°) and r = 0.81 (90°), with Root Mean Square Error (RMSE) < 0.17 and Mean Absolute Error (MAE) < 0.14, values representing less than 0.1% of the peak force. Conclusions: The heel-contact-based algorithm allows accurate synchronization of OpenCap and force platform signals during lower-limb functional exercises, achieving performance comparable to hardware-synchronized systems. This approach facilitates the integration of markerless motion capture in clinical, sports, and occupational settings where advanced dynamic analysis is required with limited infrastructure. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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24 pages, 2830 KB  
Article
Real-Time Radar-Based Hand Motion Recognition on FPGA Using a Hybrid Deep Learning Model
by Taher S. Ahmed, Ahmed F. Mahmoud, Magdy Elbahnasawy, Peter F. Driessen and Ahmed Youssef
Sensors 2026, 26(1), 172; https://doi.org/10.3390/s26010172 - 26 Dec 2025
Viewed by 1109
Abstract
Radar-based hand motion recognition (HMR) presents several challenges, including sensor interference, clutter, and the limitations of small datasets, which collectively hinder the performance and real-time deployment of deep learning (DL) models. To address these issues, this paper introduces a novel real-time HMR framework [...] Read more.
Radar-based hand motion recognition (HMR) presents several challenges, including sensor interference, clutter, and the limitations of small datasets, which collectively hinder the performance and real-time deployment of deep learning (DL) models. To address these issues, this paper introduces a novel real-time HMR framework that integrates advanced signal pre-processing, a hybrid convolutional neural network–support vector machine (CNN–SVM) architecture, and efficient hardware deployment. The pre-processing pipeline applies filtration, squared absolute value computation, and normalization to enhance radar data quality. To improve the robustness of DL models against noise and clutter, time-series radar signals are transformed into binarized images, providing a compact and discriminative representation for learning. A hybrid CNN-SVM model is then utilized for hand motion classification. The proposed model achieves a high classification accuracy of 98.91%, validating the quality of the extracted features and the efficiency of the proposed design. Additionally, it reduces the number of model parameters by approximately 66% relative to the most accurate recurrent baseline (CNN–GRU–SVM) and by up to 86% relative to CNN–BiLSTM–SVM, while achieving the highest SVM test accuracy of 92.79% across all CNN–RNN variants that use the same binarized radar images. For deployment, the model is quantized and implemented on two System-on-Chip (SoC) FPGA platforms—the Xilinx Zynq ZCU102 Evaluation Kit and the Xilinx Kria KR260 Robotics Starter Kit—using the Vitis AI toolchain. The system achieves end-to-end accuracies of 96.13% (ZCU102) and 95.42% (KR260). On the ZCU102, the system achieved a 70% reduction in execution time and a 74% improvement in throughput compared to the PC-based implementation. On the KR260, it achieved a 52% reduction in execution time and a 10% improvement in throughput relative to the same PC baseline. Both implementations exhibited minimal accuracy degradation relative to a PC-based setup—approximately 1% on ZCU102 and 2% on KR260. These results confirm the framework’s suitability for real-time, accurate, and resource-efficient radar-based hand motion recognition across diverse embedded environments. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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17 pages, 3211 KB  
Article
From Static to Dynamic: Complementary Roles of FSR and Piezoelectric Sensors in Wearable Gait and Pressure Monitoring
by Sara Sêco, Vítor Miguel Santos, Sara Valvez, Beatriz Branquinho Gomes, Maria Augusta Neto and Ana Martins Amaro
Sensors 2025, 25(23), 7377; https://doi.org/10.3390/s25237377 - 4 Dec 2025
Cited by 3 | Viewed by 1556
Abstract
Objective: Plantar pressure abnormalities have a significant impact on mobility and quality of life. Real-time pressure monitoring is essential in clinical and rehabilitation settings for assessing patient progress and refining treatment protocols. Instrumental and particularly smart insoles offer a promising solution by collecting [...] Read more.
Objective: Plantar pressure abnormalities have a significant impact on mobility and quality of life. Real-time pressure monitoring is essential in clinical and rehabilitation settings for assessing patient progress and refining treatment protocols. Instrumental and particularly smart insoles offer a promising solution by collecting biomechanical data during daily activities. However, determining the optimal combination of sensor type, number, and placement remains a key challenge for ensuring accurate and reliable measurements. This study proposes a methodology for identifying the most appropriate sensor technology for wearable insoles, with a focus on data accuracy, system efficiency, and practical applicability. Additionally, it examines the correlation between sensor signals and material behavior during compression testing. Methods: Two insole prototypes underwent compression testing: one equipped with a Force Sensitive Resistor (FSR) sensor and one with a piezoelectric sensor, both positioned at the heel. Three trials per prototype assessed consistency and repeatability. Real-time data acquisition utilized a microcontroller system, and signals were processed using a sixth-order Butterworth low-pass filter with a 5 Hz cutoff frequency to reduce noise. Results: FSR sensors demonstrated stable static responses but saturated rapidly beyond 20 N, with performance degradation observed after repeated loading cycles. Piezoelectric sensors exhibited excellent dynamic sensitivity with sharp voltage peaks but proved unable to measure sustained static pressure. Conclusions: FSR sensors are well-suited for static postural assessment and continuous pressure monitoring, while piezoelectric sensors excel in dynamic gait analysis. This comparative framework establishes a foundation for developing future smart insole systems that deliver accurate, real-time rehabilitation monitoring. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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21 pages, 10003 KB  
Article
Differentiating Human Falls from Daily Activities Using Machine Learning Methods Based on Accelerometer and Altimeter Sensor Fusion Feature Engineering
by Krunoslav Jurčić and Ratko Magjarević
Sensors 2025, 25(23), 7220; https://doi.org/10.3390/s25237220 - 26 Nov 2025
Cited by 1 | Viewed by 1322
Abstract
This paper presents a detailed analysis of signal data acquired from wearable sensors such as accelerometers and barometric altimeters for human activity recognition, with an emphasis on fall detection. This research addressed two types of activity recognition tasks: a binary classification problem between [...] Read more.
This paper presents a detailed analysis of signal data acquired from wearable sensors such as accelerometers and barometric altimeters for human activity recognition, with an emphasis on fall detection. This research addressed two types of activity recognition tasks: a binary classification problem between activities of daily living (ADLs) and simulated fall activities and a multiclass classification problem involving five different activities (running, walking, sitting down, jumping, and falling). By combining features derived from both sensors, traditional machine models such as random forest, support vector machine, XGBoost, logistic regression, and majority voter models were used for both classification problems. All of the aforementioned methods generally produced better results using combined features of both sensors compared to single-sensor models, highlighting the potential of sensor fusion approaches for fall detection. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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27 pages, 4773 KB  
Article
Micro Gesture Recognition with Multi-Dimensional Feature Fusion and CQ-MobileNetV3 Using FMCW Radar
by Wei Xue, Rui Wang, Jianyun Wei and Li Liu
Sensors 2025, 25(22), 6949; https://doi.org/10.3390/s25226949 - 13 Nov 2025
Viewed by 1118
Abstract
Radar-based gesture recognition technology has gained increasing attention in the context of contactless human–computer interaction (HCI). Micro gestures have smaller motion amplitudes and shorter duration compared with traditional gestures, which increases the difficulty of motion feature extraction. In addition, improving recognition accuracy while [...] Read more.
Radar-based gesture recognition technology has gained increasing attention in the context of contactless human–computer interaction (HCI). Micro gestures have smaller motion amplitudes and shorter duration compared with traditional gestures, which increases the difficulty of motion feature extraction. In addition, improving recognition accuracy while maintaining low computational and storage costs is also a challenge. In this paper, a micro gesture recognition method combining multi-dimensional feature fusion and a lightweight CQ-MobileNetV3 network is proposed. For feature extraction, the range–time map, velocity–time map, and angle–time map of gestures are first constructed. Then, normalization and adaptive filtering are performed to refine the three maps. Finally, the three refined maps are fused to form a range–velocity–angle–time map, which can accurately describe the motion characteristics of gestures. For recognition, a lightweight CQ-MobileNetV3 network is designed. First, the network structure of MobileNetV3 is optimized to reduce computational complexity. Then, the improved convolutional block attention module (CBAM) and the improved self-attention (SA) module are constructed and integrated into different bottleneck blocks to improve recognition accuracy. A series of experiments are conducted with a 77 GHz frequency-modulated continuous wave (FMCW) radar. The results indicate that CQ-MobileNetV3 achieves a recognition accuracy of 97.16% for 14 micro gestures, with a parameter count of 0.207 M and a computational complexity of 0.027 GFLOPs, surpassing several other deep neural networks. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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13 pages, 1560 KB  
Article
Towards a Lightweight Arabic Sign Language Translation System
by Mohammed Algabri, Mohamed A. Mekhtiche, Mohamed A. Bencherif and Fahman Saeed
Sensors 2025, 25(17), 5504; https://doi.org/10.3390/s25175504 - 4 Sep 2025
Cited by 1 | Viewed by 2409
Abstract
There is a pressing need to build a sign-to-text translation system to simplify communication between deaf and non-deaf people. This study investigates the building of a high-performance, lightweight sign language translation system suitable for real-time applications. Two Saudi Sign Language datasets are used [...] Read more.
There is a pressing need to build a sign-to-text translation system to simplify communication between deaf and non-deaf people. This study investigates the building of a high-performance, lightweight sign language translation system suitable for real-time applications. Two Saudi Sign Language datasets are used for evaluation. We also investigate the effects of the number of signers and number of repetitions in sign language datasets. To this end, eight experiments are conducted in both signer-dependent and signer-independent modes. A comprehensive ablation study is presented to study the impacts of model components, network depth, and the size of the hidden dimension. The best accuracies achieved are 97.7% and 90.7% for the signer-dependent and signer-independent modes, respectively, using the KSU-SSL dataset. Similarly, the model achieves 98.38% and 96.22% for signer-dependent and signer-independent modes using the ArSL dataset. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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11 pages, 1540 KB  
Article
Extraction of Clinically Relevant Temporal Gait Parameters from IMU Sensors Mimicking the Use of Smartphones
by Aske G. Larsen, Line Ø. Sadolin, Trine R. Thomsen and Anderson S. Oliveira
Sensors 2025, 25(14), 4470; https://doi.org/10.3390/s25144470 - 18 Jul 2025
Viewed by 1863
Abstract
As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of [...] Read more.
As populations age and workforces decline, the need for accessible health assessment methods grows. The merging of accessible and affordable sensors such as inertial measurement units (IMUs) and advanced machine learning techniques now enables gait assessment beyond traditional laboratory settings. A total of 52 participants walked at three speeds while carrying a smartphone-sized IMU in natural positions (hand, trouser pocket, or jacket pocket). A previously trained Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM)-based machine learning model predicted gait events, which were then used to calculate stride time, stance time, swing time, and double support time. Stride time predictions were highly accurate (<5% error), while stance and swing times exhibited moderate variability and double support time showed the highest errors (>20%). Despite these variations, moderate-to-strong correlations between the predicted and experimental spatiotemporal gait parameters suggest the feasibility of IMU-based gait tracking in real-world settings. These associations preserved inter-subject patterns that are relevant for detecting gait disorders. Our study demonstrated the feasibility of extracting clinically relevant gait parameters using IMU data mimicking smartphone use, especially parameters with longer durations such as stride time. Robustness across sensor locations and walking speeds supports deep learning on single-IMU data as a viable tool for remote gait monitoring. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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25 pages, 4082 KB  
Article
Multi-Scale Attention Fusion Gesture-Recognition Algorithm Based on Strain Sensors
by Zhiqiang Zhang, Jun Cai, Xueyu Dai and Hui Xiao
Sensors 2025, 25(13), 4200; https://doi.org/10.3390/s25134200 - 5 Jul 2025
Cited by 1 | Viewed by 1409
Abstract
Surface electromyography (sEMG) signals are commonly employed for dynamic-gesture recognition. However, their robustness is often compromised by individual variability and sensor placement inconsistencies, limiting their reliability in complex and unconstrained scenarios. In contrast, strain-gauge signals offer enhanced environmental adaptability by stably capturing joint [...] Read more.
Surface electromyography (sEMG) signals are commonly employed for dynamic-gesture recognition. However, their robustness is often compromised by individual variability and sensor placement inconsistencies, limiting their reliability in complex and unconstrained scenarios. In contrast, strain-gauge signals offer enhanced environmental adaptability by stably capturing joint deformation processes. To address the challenges posed by the multi-channel, temporal, and amplitude-varying nature of strain signals, this paper proposes a lightweight hybrid attention network, termed MACLiteNet. The network integrates a local temporal modeling branch, a multi-scale fusion module, and a channel reconstruction mechanism to jointly capture local dynamic transitions and inter-channel structural correlations. Experimental evaluations conducted on both a self-collected strain-gauge dataset and the public sEMG benchmark NinaPro DB1 demonstrate that MACLiteNet achieves recognition accuracies of 99.71% and 98.45%, respectively, with only 0.22M parameters and a computational cost as low as 0.10 GFLOPs. Extensive experimental results demonstrate that the proposed method achieves superior performance in terms of accuracy, efficiency, and cross-modal generalization, offering a promising solution for building efficient and reliable strain-driven interactive systems. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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21 pages, 5202 KB  
Article
Real-Time American Sign Language Interpretation Using Deep Learning and Keypoint Tracking
by Bader Alsharif, Easa Alalwany, Ali Ibrahim, Imad Mahgoub and Mohammad Ilyas
Sensors 2025, 25(7), 2138; https://doi.org/10.3390/s25072138 - 28 Mar 2025
Cited by 23 | Viewed by 17186
Abstract
Communication barriers pose significant challenges for the Deaf and Hard-of-Hearing (DHH) community, limiting their access to essential services, social interactions, and professional opportunities. To bridge this gap, assistive technologies leveraging artificial intelligence (AI) and deep learning have gained prominence. This study presents a [...] Read more.
Communication barriers pose significant challenges for the Deaf and Hard-of-Hearing (DHH) community, limiting their access to essential services, social interactions, and professional opportunities. To bridge this gap, assistive technologies leveraging artificial intelligence (AI) and deep learning have gained prominence. This study presents a real-time American Sign Language (ASL) interpretation system that integrates deep learning with keypoint tracking to enhance accessibility and foster inclusivity. By combining the YOLOv11 model for gesture recognition with MediaPipe for precise hand tracking, the system achieves high accuracy in identifying ASL alphabet letters in real time. The proposed approach addresses challenges such as gesture ambiguity, environmental variations, and computational efficiency. Additionally, this system enables users to spell out names and locations, further improving its practical applications. Experimental results demonstrate that the model attains a mean Average Precision (mAP@0.5) of 98.2%, with an inference speed optimized for real-world deployment. This research underscores the critical role of AI-driven assistive technologies in empowering the DHH community by enabling seamless communication and interaction. Full article
(This article belongs to the Special Issue Sensor Systems for Gesture Recognition (3rd Edition))
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