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Open AccessArticle

Study on the Influence of Protector Design on the Biomechanical Characteristics of Knee Joint Movement

by Jiaxin Zhao, Xupeng Wang, Lingxiao Xi, Xinran Cheng, Jihyun Bae and Yongwei Li

Sensors 2026, 26(7), 2168; https://doi.org/10.3390/s26072168 - 31 Mar 2026

Viewed by 460

Abstract

To investigate how knee joint protector design affects the biomechanical characteristics of knee motion under various activities, this pilot study (n = 5) examined how knee joint protector design modulates knee biomechanics across walking, jogging, squatting, and sit-to-stand tasks using optical motion [...] Read more.

To investigate how knee joint protector design affects the biomechanical characteristics of knee motion under various activities, this pilot study (n = 5) examined how knee joint protector design modulates knee biomechanics across walking, jogging, squatting, and sit-to-stand tasks using optical motion capture and AnyBody musculoskeletal modeling (FullBody_GRFPrediction). We quantified knee flexion kinematics, model-estimated joint reaction forces and moments, and model-estimated muscle activity of eight lower-limb muscles under four conditions with different levels of structural constraint: no protector (Pro.off), a conventional sleeve-type protector (Pro.a), a segmented support protector (Pro.b), and a wrapping fixation protector (Pro.c). The biomechanical protective performance of the knee joint protector was task- and phase-dependent. The results showed that Pro.a optimized muscle activation. Pro.b increased sagittal-plane design but increased joint loading and muscle activity. Pro.c induced noticeable distal compensation along the kinetic chain. The findings revealed that protector effects were task-dependent. Dynamic tasks mainly affected coronal-plane stability parameters, whereas quasi-static tasks more clearly altered sagittal load distribution. In this study, biomechanical protective performance is defined as reduced knee joint loading without disproportionate increases in model-estimated muscle activity or excessive loss of functional knee flexion range. Under this definition, greater structural constraint did not consistently produce a more favorable biomechanical profile. These results provide a feasibility baseline for task-specific protector evaluation and motivate confirmatory studies with larger cohorts and experimental validation. This study provides theoretical and methodological insights to guide future design and optimization of knee joint protectors. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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13 pages, 1546 KB

Open AccessArticle

Specificity of Pairing Afferent and Efferent Activity for Inducing Neural Plasticity with an Associative Brain–Computer Interface

by Kirstine Schultz Dalgaard, Emma Rahbek Lavesen, Cecilie Sørenbye Sulkjær, Andrew James Thomas Stevenson and Mads Jochumsen

Sensors 2026, 26(2), 549; https://doi.org/10.3390/s26020549 - 14 Jan 2026

Cited by 1 | Viewed by 695

Abstract

Brain–computer interface-based (BCI) training induces neural plasticity and promotes motor recovery in stroke patients by pairing movement intentions with congruent electrical stimulation of the affected limb, eliciting somatosensory afferent feedback. However, this training can potentially be refined further to enhance rehabilitation outcomes. It [...] Read more.

Brain–computer interface-based (BCI) training induces neural plasticity and promotes motor recovery in stroke patients by pairing movement intentions with congruent electrical stimulation of the affected limb, eliciting somatosensory afferent feedback. However, this training can potentially be refined further to enhance rehabilitation outcomes. It is not known how specific the afferent feedback needs to be with respect to the efferent activity from the brain. This study investigated how corticospinal excitability, a marker of neural plasticity, was modulated by four types of BCI-like interventions that varied in the specificity of afferent feedback relative to the efferent activity. Fifteen able-bodied participants performed four interventions: (1) wrist extensions paired with radial nerve peripheral electrical stimulation (PES) (matching feedback), (2) wrist extensions paired with ulnar nerve PES (non-matching feedback), (3) wrist extensions paired with sham radial nerve PES (no feedback), and (4) palmar grasps paired with radial nerve PES (partially matching feedback). Each intervention consisted of 100 pairings between visually cued movements and PES. The PES was triggered based on the peak of maximal negativity of the movement-related cortical potential associated with the visually cued movement. Before, immediately after, and 30 min after the intervention, transcranial magnetic stimulation-elicited motor-evoked potentials were recorded to assess corticospinal excitability. Only wrist extensions paired with radial nerve PES significantly increased the corticospinal excitability with 57 ± 49% and 65 ± 52% immediately and 30 min after the intervention, respectively, compared to the pre-intervention measurement. In conclusion, maximizing the induction of neural plasticity with an associative BCI requires that the afferent feedback be precisely matched to the efferent brain activity. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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18 pages, 1686 KB

Open AccessArticle

InertialMov: Machine Learning Test Based on Inertial Sensors to Predict Mobility Impairment in Low Back Pain Patients

by Jeremy Carlosama, Luis Zhinin-Vera, Cesar Guevara, Carolina Cadena-Morejón, Diego Almeida-Galárraga, Lenin Ramírez-Cando, Kevin R. Landázuri, Andrés Tirado-Espín, Patricia Acosta-Vargas and Fernando Villalba-Meneses

Sensors 2025, 25(21), 6665; https://doi.org/10.3390/s25216665 - 1 Nov 2025

Cited by 1 | Viewed by 992

Abstract

Low back pain (LBP) is one of the leading causes of disability in the world's population, yet there are limitations in providing an objective clinical assessment due to its widespread nature. In this work, five machine learning models (LightGBM, XGBoost, HistGradientBoosting, GradientBoosting, and [...] Read more.

Low back pain (LBP) is one of the leading causes of disability in the world's population, yet there are limitations in providing an objective clinical assessment due to its widespread nature. In this work, five machine learning models (LightGBM, XGBoost, HistGradientBoosting, GradientBoosting, and StackingRegressor) were compared to predict trunk mobility based on inertial sensor data. There were 77 individuals with a total of 2160 movement samples of flexion–extension, rotation, and lateralization. Synthetic data augmentation and normalization were performed to be able to work with the data efficiently. Mean absolute error (MAE), mean square error (MSE), and R2 were used to evaluate model performance. Additionally, ANOVA and Tukey’s HSD were used to assess the statistical significance of the models. GradientBoostingRegressor was found to produce the lowest error and statistical significance in flexion–extension and lateralization, while StackingRegressor produced the best error in rotation. The results indicate that inertial sensors and machine learning (ML) can be applied to predict mobility, facilitating personalized rehabilitation and reducing costs. The present study demonstrates that predictive trunk motion modeling can facilitate clinical monitoring and help reduce socioeconomic limitations in patients. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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18 pages, 1509 KB

Open AccessArticle

Augmented Feedback in Post-Stroke Gait Rehabilitation Derived from Sensor-Based Gait Reports—A Longitudinal Case Series

by Gudrun M. Johansson and Fredrik Öhberg

Sensors 2025, 25(10), 3109; https://doi.org/10.3390/s25103109 - 14 May 2025

Cited by 2 | Viewed by 1952

Abstract

Wearable sensors are increasingly used to provide objective quantification of spatiotemporal and kinematic parameters post-stroke. This study aimed to evaluate the practical value of sensor-based gait reports in delivering augmented feedback and informing the development of home training programmes following a 2-week supervised [...] Read more.

Wearable sensors are increasingly used to provide objective quantification of spatiotemporal and kinematic parameters post-stroke. This study aimed to evaluate the practical value of sensor-based gait reports in delivering augmented feedback and informing the development of home training programmes following a 2-week supervised intensive intervention after stroke. Four patients with chronic stroke were assessed on four occasions (pre- and post-intervention, 3-month, and 6-month follow-ups) using clinical gait tests, during which a portable sensor-based system recorded kinematic data. The meaningfulness of individual changes in gait parameters was interpreted based on established minimal detectable change values (MDC). Three participants improved their gait speed, joint angles, and/or cadence in the Ten-Metre Walk Test, and three participants improved their walking distance in the Six-Minute Walk Test. The improvements were most evident at the 3-month follow-up (with the most obvious changes above MDC estimates) and indicated the reappearance of normal gait patterns, adjustments of gait patterns, or a combination of both. Participants showed interest in and understanding of the information derived from the gait reports (ratings of 5–10 out of 10). In conclusion, augmented feedback derived from gait reports provides a valuable complement to traditional clinical assessments used in stroke rehabilitation to optimize treatment outcomes. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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13 pages, 3512 KB

Open AccessArticle

Measuring Lower-Limb Kinematics in Walking: Wearable Sensors Achieve Comparable Reliability to Motion Capture Systems and Smartphone Cameras

by Peiyu Ma, Qingyao Bian, Jin Min Kim, Khalid Alsayed and Ziyun Ding

Sensors 2025, 25(9), 2899; https://doi.org/10.3390/s25092899 - 4 May 2025

Cited by 5 | Viewed by 2889

Abstract

Marker-based, IMU-based (6-axis IMU), and smartphone-based (OpenCap) motion capture methods are commonly used for motion analysis. The accuracy and reliability of these methods are crucial for applications in rehabilitation and sports training. This study compares the accuracy and inter-operator reliability of inverse kinematics [...] Read more.

Marker-based, IMU-based (6-axis IMU), and smartphone-based (OpenCap) motion capture methods are commonly used for motion analysis. The accuracy and reliability of these methods are crucial for applications in rehabilitation and sports training. This study compares the accuracy and inter-operator reliability of inverse kinematics (IK) solutions obtained from these methods, aiming to assist researchers in selecting the most appropriate system. For most lower limb inverse kinematics during walking motion, the IMU-based method and OpenCap show comparable accuracy to marker-based methods. The IMU-based method demonstrates higher accuracy in knee angle (5.74 ± 0.80 versus 7.36 ± 3.14 deg, with p = 0.020) and ankle angle (7.47 ± 3.91 versus 8.20 ± 3.00 deg, with p = 0.011), while OpenCap shows higher accuracy than IMU in pelvis tilt (5.49 ± 2.22 versus 4.28 ± 1.47 deg, with p = 0.013), hip adduction (6.10 ± 1.35 versus 4.06 ± 0.78 deg, with p = 0.019) and hip rotation (6.09 ± 1.74 versus 4.82 ± 2.30 deg, with p = 0.009). The inter-operator reliability of the marker-based method and the IMU-based method shows no significant differences in most motions except for hip adduction (evaluated by the intraclass correlation coefficient-ICC, 0.910 versus 0.511, with p = 0.016). In conclusion, for measuring lower-limb kinematics, wearable sensors (6-axis IMUs) achieve comparable accuracy and reliability to the gold standard, marker-based motion capture method, with lower equipment requirements and fewer movement constraints during data acquisition. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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17 pages, 3439 KB

Open AccessArticle

A Novel Approach for Visual Speech Recognition Using the Partition-Time Masking and Swin Transformer 3D Convolutional Model

by Xiangliang Zhang, Yu Hu, Xiangzhi Liu, Yu Gu, Tong Li, Jibin Yin and Tao Liu

Sensors 2025, 25(8), 2366; https://doi.org/10.3390/s25082366 - 8 Apr 2025

Cited by 9 | Viewed by 3207

Abstract

Visual speech recognition is a technology that relies on visual information, offering unique advantages in noisy environments or when communicating with individuals with speech impairments. However, this technology still faces challenges, such as limited generalization ability due to different speech habits, high recognition [...] Read more.

Visual speech recognition is a technology that relies on visual information, offering unique advantages in noisy environments or when communicating with individuals with speech impairments. However, this technology still faces challenges, such as limited generalization ability due to different speech habits, high recognition error rates caused by confusable phonemes, and difficulties adapting to complex lighting conditions and facial occlusions. This paper proposes a lip reading data augmentation method—Partition-Time Masking (PTM)—to address these challenges and improve lip reading models’ performance and generalization ability. Applying nonlinear transformations to the training data enhances the model’s generalization ability when handling diverse speakers and environmental conditions. A lip-reading recognition model architecture, Swin Transformer and 3D Convolution (ST3D), was designed to overcome the limitations of traditional lip-reading models that use ResNet-based front-end feature extraction networks. By adopting a strategy that combines Swin Transformer and 3D convolution, the proposed model enhances performance. To validate the effectiveness of the Partition-Time Masking data augmentation method, experiments were conducted on the LRW video dataset using the DC-TCN model, achieving a peak accuracy of 92.15%. The ST3D model was validated on the LRW and LRW1000 video datasets, achieving a maximum accuracy of 56.1% on the LRW1000 dataset and 91.8% on the LRW dataset, outperforming current mainstream lip reading models and demonstrating superior performance on challenging easily confused samples. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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16 pages, 5668 KB

Open AccessArticle

Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis

by Bingfei Fan, Luobin Zhang, Shibo Cai, Mingyu Du, Tao Liu, Qingguo Li and Peter Shull

Sensors 2025, 25(7), 1976; https://doi.org/10.3390/s25071976 - 22 Mar 2025

Cited by 8 | Viewed by 5985

Abstract

Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases [...] Read more.

Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases power consumption and computational complexity. The relationship between sampling frequencies and accuracies remains unclear. We thus investigated the specific influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds and recommended sufficient sampling rates. Seventeen healthy subjects wore IMUs on their thigh, shank, and foot and performed walking (1.2 m/s) and running (2.2 m/s) trials on a treadmill, and a motion testbed with an IMU was used to mimic high-frequency cyclic human movements up to 3.0 Hz. Four widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture (OMC) orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. Further, accelerometer sampling rates exceeding 100 Hz even resulted in decreased accuracy because excessive orientation updates using distorted accelerations and angular velocity introduced more error than merely using angular velocity. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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Review

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33 pages, 1744 KB

Open AccessReview

Wearable Devices for the Quantitative Assessment of Knee Joint Function After Anterior Cruciate Ligament Injury or Reconstruction: A Scoping Review

by Oliwia Ptaszyk, Tarek Boutefnouchet, Gerard Cummins, Jin Min Kim and Ziyun Ding

Sensors 2025, 25(18), 5837; https://doi.org/10.3390/s25185837 - 18 Sep 2025

Cited by 5 | Viewed by 6556

Abstract

Anterior cruciate ligament (ACL) injury and reconstruction (ACLR) are associated with biomechanical deficits and reinjury risk. Wearable devices offer promising tools for objective assessment of knee joint function. This scoping review aimed to map the use of wearable devices in quantifying knee outcomes [...] Read more.

Anterior cruciate ligament (ACL) injury and reconstruction (ACLR) are associated with biomechanical deficits and reinjury risk. Wearable devices offer promising tools for objective assessment of knee joint function. This scoping review aimed to map the use of wearable devices in quantifying knee outcomes following ACL injury or reconstruction, and to evaluate their clinical readiness and methodological quality. Eligible studies were human, English-language studies in ACL/ACLR populations or healthy cohorts assessing ACL-relevant knee outcomes with wearable devices. MEDLINE (Ovid), Embase (Ovid), APA PsycInfo (Ovid), PubMed, and Scopus were searched up to 27 August 2025. Data on devices, tasks, participants, outcomes, and validation were extracted, and an adapted technology readiness level (TRL) mapping was applied. Thirty-two studies met the inclusion criteria. Inertial measurement units (IMUs) were used most often for kinematics. Standalone accelerometers quantified pivot-shift features, while force-sensing insoles captured bilateral loading. Electromagnetic trackers and electrogoniometers served as higher-precision comparators but were workflow-limited. Reporting of calibration and criterion validation was inconsistent. TRL bands clustered at 3–6, and none reached clinical integration. We propose task-matched sampling, transparent calibration, criterion validation, pairing with patient-reported outcome measures (PROMs), and multi-site workflow trials to progress towards routine care. Full article

(This article belongs to the Special Issue Sensors for Biomechanical and Rehabilitation Engineering)

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