Journal of NeuroEngineering and Rehabilitation最新文献

{"title":"Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding.","authors":"Kangchen Li, Huanwei Liang, Jialing Qiu, Xulan Zhang, Bobo Cai, Depeng Wang, Diming Zhang, Bingzhi Lin, Haijun Han, Geng Yang, Zhijing Zhu","doi":"10.1186/s12984-025-01655-3","DOIUrl":"10.1186/s12984-025-01655-3","url":null,"abstract":"<p><p>As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"118"},"PeriodicalIF":5.2,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107988/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144159665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions.","authors":"Leonardo Buscarini, Paola Romano, Elena Sofia Cocco, Carlo Damiani, Sanaz Pournajaf, Marco Franceschini, Francesco Infarinato","doi":"10.1186/s12984-025-01654-4","DOIUrl":"10.1186/s12984-025-01654-4","url":null,"abstract":"<p><p>In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning (ML) model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI) and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients' home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected on 7282 inpatients from electronic Medical Record, each record was categorized into Neurological Patients (NP, N = 3222) or Orthopedic Patients (OP, N = 4060). To identify the most suitable machine learning model, an extensive data preprocessing phase was conducted. This process involved variables recoding, scaling, and the evaluation of different dataset balancing methods to optimize model performance. Following a thorough review and comparison of algorithms commonly employed in the clinical-rehabilitative field, the Random Over Sampling (ROS) technique, in combination with the Random Forest (RF) machine learning model, was selected. Subsequently, a comprehensive hyperparameter tuning phase was performed using a grid search approach. The optimized model achieved an average accuracy of 98% for OP and 96% for NP, based on 10-fold cross-validation applied to the balanced training set (unrealistic scenario). When tested on the unbalanced dataset (real-world condition), the RF model maintained strong generalization performance, achieving 90% accuracy for OP and 83% for NP. This work points out the increasing importance of AI in medicine, especially in the realm of personalized rehabilitation. The use of such approaches could signify a transformative shift in healthcare. The integration of machine learning not only enhances the precision of treatment but also opens new possibilities for patient-centered care, improving outcomes and quality of care for individuals undergoing rehabilitation.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"117"},"PeriodicalIF":5.2,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12105185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.","authors":"Anruo Shen, Jingnan Sun, Xiaogang Chen, Xiaorong Gao","doi":"10.1186/s12984-025-01645-5","DOIUrl":"10.1186/s12984-025-01645-5","url":null,"abstract":"<p><strong>Background: </strong>Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying on self-report and clinician judgment via traditional assessment scales. EEG has emerged as a promising, non-invasive modality for capturing neural correlates of depression. However, most EEG-based machine learning diagnostic studies focus on boosting classification accuracy through complex algorithms and small, homogenous datasets. These black-box approaches often yield results that are difficult to interpret and poorly generalizable, making clinical translation impractical. Therefore there remains a critical need for models that are not only accurate but also transparent, robust, and grounded in the physiological properties of the data itself.</p><p><strong>Methods: </strong>We proposed a data-centric, interpretable framework for EEG-based depression severity grading. A hybrid feature selection method was used, combining p-value and SHapley Additive exPlanations (SHAP) methods to select features that are both independently significant and jointly informative. The system was trained and evaluated on a large-scale, multi-site resting-state EEG dataset, using random forest for both classification and regression tasks. The SHAP method, an explainable artificial intelligence technique, is also used post-hoc to infer the key electrophysiological features and key brain regions associated with MDD mechanism to further increase interpretability.</p><p><strong>Results: </strong>The proposed system achieved 74.5% (95% CI [70.97%, 78.80%], p < 0.001) ten-fold classification accuracy and a correlation coefficient of 0.56 (95% CI [0.407, 0.683], p < 0.001) for severity estimation. SHAP analysis identified consistent, clinically meaningful EEG features, particularly in the left parietal-occipital lobe. Through in-depth SHAP value analysis, we identified critical disease-related brain areas in the left occipital and parietal lobes, along with key features including relative beta power in the left parietal lobe, time-domain features at the parietal midline, 1/f intercept, left occipital relative beta power, and global brain alpha energy.</p><p><strong>Conclusion: </strong>This study proposes a data-centric, interpretable depression grading system built on large-scale, multi-center EEG data, using simple models and hybrid feature selection to emphasize explainability, generalizability and data fidelity. By shifting the focus from algorithmic complexity to data transparency and feature-level insight, the model offers a practical and trustworthy path toward real-world mental health assessment.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"116"},"PeriodicalIF":5.2,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144142769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors.","authors":"Siqi Cai, Xuyang Wei, Enze Su, Weifeng Wu, Haiqing Zheng, Longhan Xie","doi":"10.1186/s12984-025-01591-2","DOIUrl":"10.1186/s12984-025-01591-2","url":null,"abstract":"","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"115"},"PeriodicalIF":5.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12100879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of needs for an assistive robotic arm in individuals with tetraplegia: a mixed-methods approach.","authors":"N Hutmacher, A Bellwald, R Rätz, G Gruener, P Eichelberger, N Lutz, V Steiner, A M Raab","doi":"10.1186/s12984-025-01642-8","DOIUrl":"10.1186/s12984-025-01642-8","url":null,"abstract":"<p><strong>Background: </strong>A severe spinal cord injury (SCI) can profoundly affect an individual's physical abilities and social independence. For individuals living with tetraplegia, an assistive robotic arm offers the potential to restore some autonomy and reduce the need for constant assistance. However, current assistive technologies are often costly, impractical, and fail to meet the needs of those affected. This leads to high rates of abandonment and user frustration with the technology. The aim of this study was to identify the needs and expectations of both individuals with tetraplegia and their caregivers regarding an assistive robotic arm in performing everyday activities.</p><p><strong>Methods: </strong>A mixed-method approach was used, beginning with a focus group interview and followed by two online surveys; one aimed at individuals with tetraplegia and the other at caregivers. Qualitative analysis of the focus groups was performed using Focus Group Illustration Mapping. The online surveys were analyzed descriptively and qualitatively using structured content analysis.</p><p><strong>Results: </strong>A total of seven participants (individuals with tetraplegia, caregivers, physiotherapists, and an engineer) took part in the focus group interview. The online surveys were completed by 49 individuals with tetraplegia and nine caregivers. The results showed that the participants were open to using a robotic arm but none used one at the time of reporting. The participants reported that a robotic arm should assist in unilateral activities such as reaching, grasping, handling objects and body manipulation. The greatest need was reported for functions related to object manipulation and for contact with the person's body. The participants reported wanting control over the robotic arm via voice commands or with a joystick. Concerns were reported regarding costs, the weight and the space required for the robotic arm.</p><p><strong>Conclusions: </strong>In our study, individuals with tetraplegia reported that they would use assistive robotic arms for activities related to reaching, grasping, and object manipulation. Concerns regarding costs, weight and space requirements were reported. Our findings provide insights from a user perspective, informing future technical developments relevant to the tetraplegic population. However, generalizability might be reduced.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"113"},"PeriodicalIF":5.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing lower-limb rehabilitation: a scoping review of augmented reality environment.","authors":"Yuanyuan Liu, Qiong Zhang, Weiyi Li","doi":"10.1186/s12984-025-01643-7","DOIUrl":"10.1186/s12984-025-01643-7","url":null,"abstract":"<p><strong>Background: </strong>Lower-limb rehabilitation is crucial for restoring motor function in individuals with physical impairments; however, traditional rehabilitation approaches often encounter challenges such as limited resources and reduced patient motivation. Augmented reality (AR) offers an innovative approach by enriching rehabilitation with interactive and engaging experiences, thereby enhancing both motivation and treatment outcomes. AR environments enable patients to practice exercises in an immersive setting that emulates real-life scenarios, potentially increasing adherence and improving functional recovery.</p><p><strong>Methods: </strong>This scoping review analyzed 25 peer-reviewed studies on the use of AR within the \"Environment\" component of the Human-Computer-Environment system for lower-limb rehabilitation. We present a taxonomy of existing AR systems, categorizing them by rehabilitation tasks (content) and interaction modes (form), which identify both physical and virtual elements that contribute to a supportive AR environment.</p><p><strong>Discussion: </strong>The findings suggest that well-designed AR environments offer a flexible and cost-effective approach to various rehabilitation tasks. Customization is essential for addressing specific rehabilitation stages, including muscle strengthening, balance improvement, and gait training. The integration of multisensory feedback, such as visual, auditory, and haptic cues, enhances patient engagement and provides real-time performance monitoring. Effective AR environments must also account for the distinct needs of each limb, particularly for bilateral impairments, and ensure sufficient space for safe movement. By providing an individualized rehabilitation experience, AR environments have the potential to significantly improve patient motivation and outcomes. Future research should explore the integration of AR environments with assistive technologies, such as wearable devices and exoskeletons, to further enhance rehabilitation possibilities.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"114"},"PeriodicalIF":5.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12093737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Neuromusculoskeletal Modeling Pipeline: MATLAB-based model personalization and treatment optimization functionality for OpenSim.","authors":"Claire V Hammond, Spencer T Williams, Marleny M Vega, Di Ao, Geng Li, Robert M Salati, Kayla M Pariser, Mohammad S Shourijeh, Ayman W Habib, Carolynn Patten, Benjamin J Fregly","doi":"10.1186/s12984-025-01629-5","DOIUrl":"10.1186/s12984-025-01629-5","url":null,"abstract":"<p><p>Neuromusculoskeletal injuries including osteoarthritis, stroke, spinal cord injury, and traumatic brain injury affect roughly 19% of the U.S. adult population. Standardized interventions have produced suboptimal functional outcomes due to the unique treatment needs of each patient. Strides have been made to utilize computational models to develop personalized treatments, but researchers and clinicians have yet to cross the \"valley of death\" between fundamental research and clinical usefulness. This article introduces the Neuromusculoskeletal Modeling (NMSM) Pipeline, two MATLAB-based toolsets that add Model Personalization and Treatment Optimization functionality to OpenSim. The two toolsets facilitate computational design of individualized treatments for neuromusculoskeletal impairments through the use of personalized neuromusculoskeletal models and predictive simulation. The Model Personalization toolset contains four tools for personalizing 1) joint structure models, 2) muscle-tendon models, 3) neural control models, and 4) foot-ground contact models. The Treatment Optimization toolset contains three tools for predicting and optimizing a patient's functional outcome for different treatment options using a patient's personalized neuromusculoskeletal model and direct collocation optimal control methods. Support for user-defined cost, constraint, and model modification functions facilitate simulation of a vast number of possible treatments. An NMSM Pipeline use case is presented for an individual post-stroke with impaired walking function, where the goal was to predict how the subject's neural control could be changed to improve walking speed without increasing metabolic cost. First the Model Personalization toolset was used to develop a personalized neuromusculoskeletal model of the subject starting from a generic OpenSim full-body model and experimental walking data (video motion capture, ground reaction, and electromyography) collected from the subject at his self-selected speed. Next the Treatment Optimization toolset was used with the personalized model to predict how the subject could recruit existing muscle synergies more effectively to reduce muscle activation disparities between the paretic and non-paretic legs. The software predicted that the subject could increase his walking speed by 60% without increasing his metabolic cost per unit time by modifying existing muscle synergy recruitment. This hypothetical treatment demonstrates how NMSM Pipeline tools could allow researchers working collaboratively with clinicians to develop personalized neuromusculoskeletal models of individual patients and to perform predictive simulations for designing personalized treatments that maximize a patient's post-treatment functional outcome.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"112"},"PeriodicalIF":5.2,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety and effectiveness of non-invasive brain stimulation on mobility and balance function in children with cerebral palsy: a systematic review and meta-analysis.","authors":"Mengru Zhong, Yage Zhang, Jie Luo, Tingting Chen, Jingbo Zhang, Tingting Peng, Mingshan Han, Wen Le, Tingting Peng, Kaishou Xu","doi":"10.1186/s12984-025-01619-7","DOIUrl":"10.1186/s12984-025-01619-7","url":null,"abstract":"<p><strong>Background: </strong>Children with cerebral palsy (CP) experience significant mobility and balance impairments. Non-invasive brain stimulation (NIBS), including transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), has emerged as a potential therapeutic intervention. Nevertheless, the safety and effectiveness of NIBS in children with CP remain uncertain and require further investigation. This study aimed to evaluate the safety and effectiveness of NIBS in improving mobility and balance function in children with CP.</p><p><strong>Methods: </strong>Randomized controlled trials written in English were searched in five databases (PubMed, Embase, Scopus, Web of Science, and ProQuest), from the first available records in each database to April 2024. Statistical analysis focused on outcomes related to mobility and balance function immediately following intervention and one-month follow-up.</p><p><strong>Results: </strong>A total of 16 studies encompassing 346 children with CP, aged 3-14 years, were included. The meta-analysis indicated that NIBS is safe and well-tolerated [Risk Difference = 0.16, 95% CI - 0.01-0.33], with a low incidence of adverse events. Significant improvements were observed in mobility post-intervention and at one-month follow-up, particularly in Gross Motor Function Measure scores [standard mean difference (SMD) = 0.47 to 0.63, P < 0.05]. Gait parameters, including gait velocity (SMD = 1.28, P < 0.01) and stride length (SMD = 0.70, P = 0.01) showed immediate improvements. However, no significant improvements were found in balance post-tDCS or at follow-up.</p><p><strong>Conclusions: </strong>Our findings support the use of NIBS as a safe and feasible tool for enhancing mobility in children with CP, demonstrating both immediate and sustained improvements in gait parameters such as velocity and stride length. However, the impact on balance remains inconclusive. Future research should focus on extending follow-up periods, increasing sample sizes, and exploring tailored stimulation protocols to better understand the long-term efficacy and optimal application of NIBS in pediatric populations.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"111"},"PeriodicalIF":5.2,"publicationDate":"2025-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12087172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144093997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic detection of human gait events: a simple but versatile 3D algorithm.","authors":"Théo Vancanneyt, Camille Le Moal, Maxence Blard, Juliette Lenoir, Nicolas Roche, Céline Bonnyaud, Fabien Dubois","doi":"10.1186/s12984-025-01544-9","DOIUrl":"10.1186/s12984-025-01544-9","url":null,"abstract":"<p><strong>Background: </strong>Detecting Foot Strike and Foot Off events in human gait, which is cyclic yet variable, consistently requires expert correction. This subjective correction can reduce spatiotemporal parameters, joint kinematic and kinetic accuracy, regardless of the gait event detection algorithm used from the literature. Recently developed methods have combined existing algorithms to better capture this gait variability, using Ground Reaction Forces. However, those methods do not fully account for intra-individual variability, particularly in the case of multiple and simultaneous gait patterns.</p><p><strong>Method: </strong>We developed a deterministic algorithm called the Multi-Condition algorithm. This algorithm identifies the Foot Strike when the first of the foot markers loses its degrees of freedom and the Foot Off when the last of the foot markers regains its degrees of freedom.</p><p><strong>Results: </strong>This algorithm was tested on 819 C3D gait files from 9 healthy individuals and 50 individuals with stroke, multiple sclerosis, spinal cord injury, cerebral palsy, polio, neuromuscular disease or amputation. The Multi-Condition algorithm detected both Foot Strike and Foot Off within a range of three frames, which was more accurate and precise than the inter-rater variability of expert correction. Detection of gait events required only a few seconds, regardless of the pathology or gait pattern, even when considering intra-individual variability.</p><p><strong>Conclusion: </strong>Accurately identifying gait events is the first critical step in providing reliable gait analysis parameters for clinicians. The Multi-Condition algorithm aims to achieve deterministic consensus in the accurate and precise identification of gait events, regardless of the pathology or the gait pattern. To promote its adoption and ongoing testing, the Multi-Condition algorithm is available as an open-access resource.</p><p><strong>Ethical committee: </strong>The study was approved by the University of Paris-Saclay Research Ethics Committee (No. CER-Paris-Saclay-2024-35) and was performed in accordance with the Declaration of Helsinki.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"110"},"PeriodicalIF":5.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12077026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144011642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hidden Markov model-based similarity measure (HMM-SM) for gait quality assessment of lower-limb prosthetic users using inertial sensor signals.","authors":"Gabriel Ng, Jan Andrysek","doi":"10.1186/s12984-025-01638-4","DOIUrl":"10.1186/s12984-025-01638-4","url":null,"abstract":"<p><strong>Background: </strong>Gait quality indices, such as the Gillette Gait Index or Gait Profile Score (GPS), can provide clinicians with objective, straightforward measures to quantify gait pathology and monitor changes over time. However, these methods often require motion capture or stationary gait analysis systems, limiting their accessibility. Inertial sensors offer a portable, cost-effective alternative for gait analysis. This study aimed to evaluate a novel hidden Markov model-based similarity measure (HMM-SM) for assessing gait quality directly from gyroscope and accelerometer data captured by inertial sensors.</p><p><strong>Methods: </strong>Walking trials were conducted with 26 lower-limb prosthetic users and 30 able-bodied individuals, using inertial sensors placed at various lower body locations. We computed the HMM-SM score along with other established inertial sensor-based methods, including the Movement Deviation Profile, Dynamic Time Warping, IMU-based Gait Normalcy Index, and Multifeature Gait Score. Spearman correlations with the GPS, a validated measure of gait quality, were assessed, as well as correlations among the inertial sensor methods. Welch's t-tests were used to evaluate the ability to distinguish between prosthetic subgroups.</p><p><strong>Results: </strong>The HMM-SM and other inertial sensor-based methods demonstrated moderate-to-strong correlations with the GPS (0.49 <|r|< 0.77 for significant correlations). Comparisons between different measures highlighted key similarities and differences, both in correlations and in their ability to differentiate between subgroups. Overall, the pelvis and lower leg sensors achieved significant correlations and outperformed the upper leg sensors, which did not achieve significant correlations with the GPS for any of the signal-based measures.</p><p><strong>Conclusion: </strong>Results suggest inertial sensors located at the pelvis and lower leg provide valid markers for monitoring overall gait quality, offering the potential to develop nonobtrusive, wearable systems to facilitate long-term monitoring. Such systems could enhance rehabilitation by enabling continuous gait assessment that can be easily integrated in clinical and everyday settings.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"22 1","pages":"109"},"PeriodicalIF":5.2,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}