IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

{"title":"Enhancing and Optimizing User-Machine Closed-Loop Co-Adaptation in Dynamic Myoelectric Interface","authors":"Wei Li;Ping Shi;Sujiao Li;Hongliu Yu","doi":"10.1109/TNSRE.2025.3558687","DOIUrl":"10.1109/TNSRE.2025.3558687","url":null,"abstract":"Co-adaptation interfaces, developed through user-machine collaboration, have the capacity to transform surface electromyography (sEMG) into control signals, thereby enabling external devices to facilitate or augment the sensory-motor capabilities of individuals with physical disabilities. However, the efficacy and reliability of myoelectric interfaces in untrained environments over extensive spatial range have not been thoroughly explored. We propose a user-machine closed-loop co-adaptation strategy, which consists of a multimodal progressive domain adversarial neural network (MPDANN), an augmented reality (AR) system and a scenario-based dynamic asymmetric training scheme. MPDANN employs both sEMG and Inertial Measurement Unit (IMU) data using dual-domain adversarial training, with the aim of facilitating knowledge transfer and enabling multi-source domain adaptation. The AR system allows users to perform 10 holographic object repositioning tasks in a stereoscopic mixed reality environment using a virtual prosthesis represented as an extension of the residual limb. The scenario-based dynamic asymmetric training scheme, which employs incremental learning in MPDANN and incremental training in the AR system, enables the continuous updating and optimization of the system parameters. A group of non-disable participants and two amputees performed a five-day offline data collection in multiple limb position conditions and a five-day real-time holographic object manipulation task. The average completion rate for subjects utilizing MPDANN reached <inline-formula> <tex-math>${83}.{37}% pm {2}.{50}%$ </tex-math></inline-formula> on the final day, marking a significant improvement compared to the other groups. These findings provide a novel approach to designing myoelectric interfaces with cross-scene recognition through user-machine closed-loop co-adaptation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1673-1684"},"PeriodicalIF":4.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10955275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803130","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":"Optimized Temporal Interference Stimulation Based on Convex Optimization: A Computational Study","authors":"Chao Geng;Yang Li;Long Li;Xiaoqi Zhu;Xiaohan Hou;Tian Liu","doi":"10.1109/TNSRE.2025.3558306","DOIUrl":"10.1109/TNSRE.2025.3558306","url":null,"abstract":"Temporal interference (TI) stimulation is a non-invasive method targeting deep brain regions by applying two pairs of high-frequency currents with a slight frequency difference to the scalp. However, optimizing electrode configurations for TI via computational modeling is challenging and time-consuming due to the non-convex nature of the optimization. We propose a convex optimization-based method (CVXTI) for optimizing TI electrode configurations. We decompose the TI optimization into two convex steps, enabling rapid determination of electrode pair configurations. CVXTI accommodates various optimization objectives by incorporating different objective functions, thereby enhancing the focality of the stimulation field. Performance analysis of CVXTI shows superior results compared to other methods, particularly in deep brain regions. Subject variability analysis on four individuals highlights the necessity of customized stimulus optimization. CVXTI leverages the distribution characteristics of the TI envelope electric field to optimize electrode configurations, enhancing the optimization efficiency.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1400-1410"},"PeriodicalIF":4.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10951111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803132","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":"Neuro-Modulation Analysis Based on Muscle Synergy Graph Neural Network in Human Locomotion","authors":"Ningjia Yang;Xuesi Li;Qi An;Jingsong Li;Shingo Shimoda","doi":"10.1109/TNSRE.2025.3557777","DOIUrl":"10.1109/TNSRE.2025.3557777","url":null,"abstract":"The coordination of muscles in human locomotion is commonly understood as the integration of motor modules known as muscle synergies. Recent research has delved into the adaptation of muscle synergies during the acquisition of new motor skills. However, the precise interplay between modulated muscle synergies during movement according to motion requirements remains unclear. Here, we aim to elucidate the alterations in locomotor synergies across various lower-limb motion strategies and motor tasks. Our findings reveal consistent weights of muscles in muscle synergies alongside varying timing activation aligned with specific motion requirements. It shows that spatial muscle synergies remain stable across different motor tasks, but humans adjusted the timing activation of these modules (temporal muscle synergies) to meet the motor requirements. To classify temporal muscle synergies and quantify connection weights for both self-connections and connections between muscle synergies, we employed a graph neural network. Our results demonstrate that muscle synergy 4, responsible for elevating the thigh to propel forward during the swing phase, experiences pronounced enhancement with changes in motion strategies. Furthermore, we observed a reduction in the self-connection of muscle synergy 2, implicated in stabilizing body posture, during motion tasks other than normal walking. Additionally, the connections between muscle synergy 2 and other synergies diminished, indicating more adaptation in muscle synergy 2 to achieve stabilization in more challenging motor tasks. The validity of these findings was verified through five-fold cross-validation, affirming the efficacy of our approach in elucidating neuro-modulation mechanisms in human locomotion. Our proposed methodology holds promising implications for the development of personalized training strategies, offering insights into the intricate interactions among different muscle synergies in accomplishing motor tasks.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1381-1391"},"PeriodicalIF":4.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784465","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":"Explainable Depression Classification Based on EEG Feature Selection From Audio Stimuli","authors":"Lixian Zhu;Rui Wang;Xiaokun Jin;Yuwen Li;Fuze Tian;Ran Cai;Kun Qian;Xiping Hu;Bin Hu;Yoshiharu Yamamoto;Björn W. Schuller","doi":"10.1109/TNSRE.2025.3557275","DOIUrl":"10.1109/TNSRE.2025.3557275","url":null,"abstract":"With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have been widely proposed. However, existing studies have mostly focused on the accuracy of depression recognition, ignoring the association between features and models. Additionally, there is a lack of research on the contribution of different features to depression recognition. To this end, this study introduces an innovative approach to depression detection using EEG data, integrating Ant-Lion Optimization (ALO) and Multi-Agent Reinforcement Learning (MARL) for feature fusion analysis. The inclusion of Explainable Artificial Intelligence (XAI) methods enhances the explainability of the model’s features. The Time-Delay Embedded Hidden Markov Model (TDE-HMM) is employed to infer internal brain states during depression, triggered by audio stimulation. The ALO-MARL algorithm, combined with hyper-parameter optimization of the XGBoost classifier, achieves high accuracy (93.69%), sensitivity (88.60%), specificity (97.08%), and F1-score (91.82%) on a auditory stimulus-evoked three-channel EEG dataset. The results suggest that this approach outperforms state-of-the-art feature selection methods for depression recognition on this dataset, and XAI elucidates the critical impact of the minimum value of Power Spectral Density (PSD), Sample Entropy (SampEn), and Rényi Entropy (Ren) on depression recognition. The study also explores dynamic brain state transitions revealed by audio stimuli, providing insights for the clinical application of AI algorithms in depression recognition.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1411-1426"},"PeriodicalIF":4.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947765","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772235","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":"Spike-Based Neuromorphic Model of Spasticity for Generation of Affected Neural Activity","authors":"Jin Yan;Qi Luo;Zhi Chen;Zeyu Wang;Xinliang Guo;Qing Xie;Denny Oetomo;Ying Tan;Chuanxin M. Niu","doi":"10.1109/TNSRE.2025.3557044","DOIUrl":"10.1109/TNSRE.2025.3557044","url":null,"abstract":"Spasticity is a common motor symptom that disrupt muscle contraction and hence movements. Proper management of spasticity requires identification of its origins and reasoning of the therapeutic plans. Challenges arise because spasticity might originate from elevated activity in both the cortical and sub-cortical pathways. No existing models (animal or computational) could cover all possibilities leading to spasticity, especially the peripheral causes such as hyperreflexia. To bridge this gap, this work develops a novel computational, spike-based neuromorphic model of spasticity, named NEUSPA. Rather than relying solely on a monosynaptic spinal loop comprising alpha motoneurons, sensory afferents, synapses, skeletal muscles, and muscle spindles, the NEUSPA model introduces two additional inputs: additive (ADD) and multiplicative (MUL). These inputs generate velocity-dependent EMG responses. The effectiveness of the NEUSPA model is validated using classic experiments from the literature and data collected from two post-stroke patients with affected upper-limb movements. The model is also applied to simulate two real-world scenarios that patients may encounter. Simulation results suggest that hyperreflexia due to extra inputs was sufficient to produce spastic EMG responses. However, EMG onsets were more sensitive to ADD inputs (slope =0.628, p <0.0001,> <tex-math>${}^{{2}} =0.96$ </tex-math></inline-formula>) compared to MUL inputs (slope =0.471, p <0.0001,> <tex-math>${}^{{2}} =0.92$ </tex-math></inline-formula>). Additionally, simulation of finger-pressing on a deformable object indicated that spasticity could increase the duration from 1.03s to 1.20s compared to a non-impaired condition. These results demonstrate that NEUSPA effectively synthesizes abnormal physiological data, facilitating decision-making and machine learning in neurorehabilitation.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1360-1371"},"PeriodicalIF":4.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772237","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":"Muscle Spindle Model-Based Non-Invasive Electrical Stimulation for Motion Perception Feedback in Prosthetic Hands","authors":"Qichuan Ding;Chenyu Tong;Dongxu Liu;Bicen Yan;Fei Wang;Shuai Han","doi":"10.1109/TNSRE.2025.3556726","DOIUrl":"10.1109/TNSRE.2025.3556726","url":null,"abstract":"Prosthetic hands offer significant benefits for patients with hand amputations by partially replicating the function of real hands. However, most current prosthetics lack sensory feedback on movement, leading to a gap in proprioception for users. To bridge this gap and approximate the natural experience of hand use, prosthetic hands must offer detailed motion feedback. This paper introduces a non-invasive electrical stimulation approach, which can provide motion perception feedback through modeling muscle spindles. By employing transcutaneous electrical nerve stimulation (TENS), the method generates artificial sensory signals associated with the movement of a prosthetic hand, potentially restoring a degree of proprioception for patients with hand amputations. We developed an experimental framework involving an electronic prosthetic hand, an electrical stimulator, and surface electrodes to assess our approach. Five able-body and three forearm amputees took part in our experiments. The experimental results indicated that the subjects were able to accurately discern the movement angle of the prosthetic hand, and when the sensory feedback was biomimetic, the subjects were able to identify the prosthetic hand movement state better than using a traditional encoding algorithm that only relied on the current stimulation intensity.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1316-1327"},"PeriodicalIF":4.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10946698","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763669","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":"AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on a Cue-Masked Paradigm","authors":"Keren Shi;Xu Liu;Xue Yuan;Haijie Shang;Ruiting Dai;Hanbin Wang;Yunfa Fu;Ning Jiang;Jiayuan He","doi":"10.1109/TNSRE.2025.3555542","DOIUrl":"10.1109/TNSRE.2025.3555542","url":null,"abstract":"Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-masked auditory attention paradigm to avoid information leakage before the experiment. To obtain high decoding accuracy with low latency, an end-to-end deep learning model, AADNet, was proposed to exploit the spatiotemporal information from the short time window of EEG signals. The results showed that with a 0.5-second EEG window, AADNet achieved an average accuracy of 93.46% and 91.09% in decoding auditory orientation attention (OA) and timbre attention (TA), respectively. It significantly outperformed five previous methods and did not need the knowledge of the original audio source. This work demonstrated that it was possible to detect the orientation and timbre of auditory attention from EEG signals fast and accurately. The results are promising for the real-time multi-property auditory attention decoding, facilitating the application of the neuro-steered hearing aids and other assistive listening devices.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1349-1359"},"PeriodicalIF":4.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763668","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":"StimEMG: An Electromyogram Recording System With Real-Time Removal of Time-Varying Electrical Stimulation Artifacts","authors":"Jiashun Zhao;Rui Yuan;Henry Shin;Run Ji;Yang Zheng","doi":"10.1109/TNSRE.2025.3555572","DOIUrl":"10.1109/TNSRE.2025.3555572","url":null,"abstract":"A closed-loop Functional Electrical Stimulation (FES) system that incorporates electromyogram (EMG) signal feedback provides more effective assistance to paralytic patients in maintaining and recovering their motor abilities. However, the closed-loop FES system with real-time adjustment of stimulation parameters tends to introduce time-varying stimulation artifacts in EMG signals, challenging the removal of stimulation artifacts that aims at more accurate monitoring of muscle contraction status. Therefore, an EMG acquisition system that embeds a stimulation artifact generation (SAG) circuit and the Recursive Least Squares (RLS) adaptive filter was developed in this study and named StimEMG. The SAG-RLS strategy was tested using the simulated contaminated EMG signals and the StimEMG system was tested in an experimental study with 8 subjects. Both the simulation and the experimental study showed that the SAG-RLS method obtained a higher correlation (R<inline-formula> <tex-math>${}^{{2}}text {)}$ </tex-math></inline-formula> between the denoised EMG and the corresponding clean EMG or EMG segments compared with the current Gram-Schmidt-based (GSB) method (simulation study, <inline-formula> <tex-math>$0.98pm 0.0044$ </tex-math></inline-formula> v.s. <inline-formula> <tex-math>$0.65pm 0.3217$ </tex-math></inline-formula>; experimental study, <inline-formula> <tex-math>$0.99pm 0.0024$ </tex-math></inline-formula> v.s. <inline-formula> <tex-math>$0.52pm 0.2105$ </tex-math></inline-formula>). Meanwhile, the SAG-RLS method can suppress stimulation artifact more effectively, resulting a higher signal-to-noise ratio (simulation study: <inline-formula> <tex-math>$12.83pm 2.1745$ </tex-math></inline-formula> v.s. <inline-formula> <tex-math>$1.54pm 1.3106$ </tex-math></inline-formula>) and higher noise rejection ratio (experimental study:<inline-formula> <tex-math>$2.32pm 0.7046$ </tex-math></inline-formula> v.s. <inline-formula> <tex-math>$1.92pm 0.8014$ </tex-math></inline-formula>). The significantly improved performance is speculated to result from the ability of the SAG unit to precisely and timely capture the variation of the stimulation artifacts caused by the change of stimulation parameters, unlike previous methods relying on the stability of the characteristic of stimulation artifacts in the contaminated EMG signals. The developed StimEMG system provides a robust EMG acquisition module for the closed-loop FES system.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"1305-1315"},"PeriodicalIF":4.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945518","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763672","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":"Optimizing Neural Recording Front-Ends Toward Enhanced Spike Sorting Accuracy in High-Channel-Count Systems","authors":"Yunzhu Chen;Xiaolin Yang;Georges Gielen;Carolina Mora Lopez","doi":"10.1109/TNSRE.2025.3574917","DOIUrl":"10.1109/TNSRE.2025.3574917","url":null,"abstract":"Spike sorting is a pivotal signal-processing technique used to extract information from raw extracellular recordings. Its performance is influenced by the characteristics of the neural recording front-end. This study explores how design choices in amplifiers, filters, and analog-to-digital converters (ADCs) affect the accuracy of well-established spike sorting algorithms. Our primary objective is to identify the minimal requirements that ensure high sorting accuracy while facilitating power- and area-efficient analog front-ends, which is especially needed for multi-channel recording-only applications. To achieve this, we use both synthetic and real datasets, serving as ground truth, processed through a generic MATLAB model of a neural recording front-end that simulates key electrical parameters impacting the signal integrity. These include the filter order and cutoff frequency, ADC resolution, ADC sampling frequency, and nonlinearity. Our findings indicate that optimal spike-sorting results are obtained with a 1st-order bandpass Butterworth filter ranging from 700 Hz to 7.5 kHz, coupled with an ADC that offers a 15-kHz sampling frequency at 8-bit resolution and no missing codes. These insights are crucial for designing high-channel-count neural interfaces where CMOS circuits must efficiently be optimized.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2180-2191"},"PeriodicalIF":4.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144183629","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":"Triple-E Principle: Leveraging Occam’s Razor for Dance Energy Expenditure Estimation","authors":"Kuan Tao;Kun Meng;Bingcan Gao;Junchao Yang;Junqiang Qiu","doi":"10.1109/TNSRE.2025.3574739","DOIUrl":"10.1109/TNSRE.2025.3574739","url":null,"abstract":"Objective: Dance, as a globally practiced physical activity, presents challenges in accurately assessing energy expenditure due to its diverse styles and tempos. Traditional methods, relying on empirical formulas within ActiGraph accelerometers, often result in significant biases. While multiple wearable sensors have been introduced to mitigate these biases, they increase model complexity. Methods: This study proposes the Triple-E principle—Effectiveness, Efficiency, and Extension—as a framework for developing state-of-the-art (SOTA) machine learning models aimed at accurately estimating energy expenditure, while minimizing model complexity and optimizing sensor placement. To validate the proposed approach, we recruited a cohort of 250 participants (mean age: 63.0 ± 6.0 years), each performing ballroom, aerobic, or square dance routines. Participants were fitted with ActiGraph wGT3X-BT accelerometers at five anatomical locations, along with the CORTEX MetaMax 3B gas analyzer for metabolic data collection. We analyzed 311 physiological signal sequences and 1,555 acceleration count sequences. Results: Empirical formulas were proved inaccurate for dance energy expenditure, with Mean Absolute Percentage Error (MAPE) exceeding 50% and Root Mean Squared Error (RMSE) surpassing 3.23. A bidirectional stepwise regression model incorporating heart rate or triaxial motion sequences from accelerometers achieved an average goodness-of-fit of 0.73, identifying optimal accelerometer sites based on Efficiency principle. A random forest regression model minimized errors to 5% (MAPE) and 0.33 (RMSE) with data from all sites. Notably, wrist accelerometers and heart rate alone provided sufficient accuracy (RMSE: 0.35-0.36), highlighting a trade-off between Effectiveness and Efficiency. A deep-learning network pipeline based on the Extension principle automatically extracted features, achieving an average RMSE to 0.15. Conclusion: This study introduces a pioneering quantitative and unified model assessment system. Thoroughly analyzed and validated in the context of dance, the research offers detailed explanations of the most effective, efficient, and extensive models.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"2088-2096"},"PeriodicalIF":4.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018125","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144181542","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}