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  • 1.
    Buist, Mirka
    et al.
    Sahlgrens Univ Hosp, Ctr Adv Reconstruct Extrem CARE, S-43180 Mölndal, Sweden.;Bion Inst, Melbourne, Vic 3002, Australia.;Univ Melbourne, Med Bion Dept, Fitzroy, Vic 3065, Australia.;NeuroBioniX, Melbourne, Vic 3065, Australia..
    Damercheli, Shahrzad
    Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden.;Chalmers Univ Technol, Dept Microtechnol & Nanosci, S-41296 Gothenburg, Sweden..
    Zbinden, Jan
    Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
    Truong, Minh Tat Nhat
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics.
    Mastinu, Enzo
    Chalmers Univ Technol, Dept Elect Engn, S-41296 Gothenburg, Sweden..
    Ortiz-Catalan, Max
    Bion Inst, Melbourne, Vic 3002, Australia.;Univ Melbourne, Med Bion Dept, Fitzroy, Vic 3065, Australia.;NeuroBioniX, Melbourne, Vic 3065, Australia.;Prometei Pain Rehabil Ctr, UA-21018 Vinnytsia, Ukraine..
    Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation2024In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 32, p. 1515-1523Article in journal (Refereed)
    Abstract [en]

    Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.

  • 2.
    Herman, Pawel Andrzej
    et al.
    University of Ulster.
    Prasad, Girijesh
    University of Ulster.
    McGinnity, Thomas Martin
    University of Ulster.
    Comparative analysis of spectral approaches to feature extraction for EEG-based motor imagery classification2008In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, ISSN 1534-4320, Vol. 16, no 4, p. 317-326Article in journal (Refereed)
    Abstract [en]

    The quantification of the spectral content of electroencephalogram (EEG) recordings has a substantial role in clinical and scientific applications. It is of particular relevance in the analysis of event-related brain oscillatory responses. This work is focused on the identification and quantification of relevant frequency patterns in motor imagery (MI) related EEGs utilized for brain--computer interface (BCI) purposes. The main objective of the paper is to perform comparative analysis of different approaches to spectral signal representation such as power spectral density (PSD) techniques, atomic decompositions, time-frequency (t-f) energy distributions, continuous and discrete wavelet approaches, from which band power features can be extracted and used in the framework of MI classification. The emphasis is on identifying discriminative properties of the feature sets representing EEG trials recorded during imagination of either left-- or right-hand movement. Feature separability is quantified in the offline study using the classification accuracy (CA) rate obtained with linear and nonlinear classifiers. PSD approaches demonstrate the most consistent robustness and effectiveness in extracting the distinctive spectral patterns for accurately discriminating between left and right MI induced EEGs. This observation is based on an analysis of data recorded from eleven subjects over two sessions of BCI experiments. In addition, generalization capabilities of the classifiers reflected in their intersession performance are discussed in the paper..

  • 3.
    Kizyte, Asta
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics. KTH MoveAbil Lab.
    Lei, Yuchen
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH MoveAbil Lab.
    Wang, Ruoli
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics. KTH MoveAbil Lab.
    Influence of Input Features and EMG Type on Ankle Joint Torque Prediction With Support Vector Regression2023In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 31, p. 4286-4294Article in journal (Refereed)
    Abstract [en]

    Reliable and accurate EMG-driven prediction of joint torques are instrumental in the control of wearable robotic systems. This study investigates how different EMG input features affect the machine learning algorithm-based prediction of ankle joint torque in isometric and dynamic conditions. High-density electromyography (HD-EMG) of five lower leg muscles were recorded during isometric contractions and dynamic tasks. Four datasets (HD-EMG, HD-EMG with reduced dimensionality, features extracted from HD-EMG with Convolutional Neural Network, and bipolar EMG) were created and used alone or in combination with joint kinematic information for the prediction of ankle joint torque using Support Vector Regression. The performance was evaluated under intra-session, inter-subject, and inter-session cases. All HD-EMG-derived datasets led to significantly more accurate isometric ankle torque prediction than the bipolar EMG datasets. The highest torque prediction accuracy for the dynamic tasks was achieved using bipolar EMG or HD-EMG with reduced dimensionality in combination with kinematic features. The findings of this study contribute to the knowledge allowing an informed selection of appropriate features for EMG-driven torque prediction.

  • 4.
    Liu, Yixing
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Biomechanics. KTH, School of Engineering Sciences (SCI), Centres, BioMEx.
    Wang, Ruoli
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Biomechanics.
    Gutierrez-Farewik, Elena
    KTH, School of Engineering Sciences (SCI), Centres, BioMEx. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Biomechanics.
    A Muscle Synergy-Inspired Method of Detecting Human Movement Intentions Based on Wearable Sensor Fusion2021In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 29, p. 1089-1098Article in journal (Refereed)
    Abstract [en]

    Detecting human movement intentions is fundamental to neural control of robotic exoskeletons, as it is essential for achieving seamless transitions between different locomotion modes. In this study, we enhanced a muscle synergy-inspired method of locomotion mode identification by fusing the electromyography data with two types of data from wearable sensors (inertial measurement units), namely linear acceleration and angular velocity. From the finite state machine perspective, the enhanced method was used to systematically identify 2 static modes, 7 dynamic modes, and 27 transitions among them. In addition to the five broadly studied modes (level ground walking, ramps ascent/descent, stairs ascent/descent), we identified the transition between different walking speeds and modes of ramp walking at different inclination angles. Seven combinations of sensor fusion were conducted, on experimental data from 8 able-bodied adult subjects, and their classification accuracy and prediction time were compared. Prediction based on a fusion of electromyography and gyroscope (angular velocity) data predicted transitions earlier and with higher accuracy. All transitions and modes were identified with a total average classification accuracy of 94.5% with fused sensor data. For nearly all transitions, we were able to predict the next locomotion mode 300-500ms prior to the step into that mode.

  • 5.
    Zhang, Longbin
    et al.
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics. KTH MoveAbilty Lab.
    Soselia, Davit
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH MoveAbil Lab.
    Wang, Ruoli
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics. KTH MoveAbil Lab.
    Gutierrez-Farewik, Elena
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics. KTH MoveAbil Lab.
    Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks2023In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 31, p. 3722-3731Article in journal (Refereed)
    Abstract [en]

    Accurately predicting joint torque using wearable sensors is crucial for designing assist-as-needed exoskeleton controllers to assist muscle-generated torque and ensure successful task performance. In this paper, we estimated ankle dorsiflexion/plantarflexion, knee flexion/extension, hip flexion/extension, and hip abduction/adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks. The joint torque ground truth for model calibrating and training was obtained through inverse dynamics of captured motion data. A cluster approach that grouped movements based on characteristic similarity was implemented, and its ability to improve the estimation accuracy of both NMS and LSTM models was evaluated. We compared torque estimation accuracy of NMS and LSTM models in three cases: Pooled, Individual, and Clustered models. Pooled models used data from all 10 movements to calibrate or train one model, Individual models used data from each individual movement, and Clustered models used data from each cluster. Individual, Clustered and Pooled LSTM models all had relatively high joint torque estimation accuracy. Individual and Clustered NMS models had similarly good estimation performance whereas the Pooled model may be too generic to satisfy all movement patterns. While the cluster approach improved the estimation accuracy in NMS models in some movements, it made relatively little difference in the LSTM neural networks, which already had high estimation accuracy. Our study provides practical implications for designing assist-as-needed exoskeleton controllers by offering guidelines for selecting the appropriate model for different scenarios, and has potential to enhance the functionality of wearable exoskeletons and improve rehabilitation and assistance for individuals with motor disorders.

  • 6.
    Zhang, Longbin
    et al.
    KTH, School of Engineering Sciences (SCI), Centres, BioMEx. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Biomechanics.
    Soselia, Davit
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH, School of Engineering Sciences (SCI), Centres, BioMEx.
    Wang, Ruoli
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Biomechanics. KTH, School of Engineering Sciences (SCI), Centres, BioMEx.
    Gutierrez-Farewik, Elena
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics. KTH, School of Engineering Sciences (SCI), Centres, BioMEx.
    Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning2022In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 30, p. 600-609Article in journal (Refereed)
    Abstract [en]

    Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The ability to estimate joint torque during daily activities using wearable sensors is increasingly relevant in such settings. In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM) neural networks and transfer learning. LSTM models were trained with muscle electromyography signals and lower limb joint angles. Hip flexion/extension, hip abduction/adduction, knee flexion/extension and ankle dorsiflexion/plantarflexion torques were predicted. The LSTM models' performance in predicting torque was investigated in both intra-subject and inter-subject scenarios. Each scenario was further divided into intra-task and inter-task tests. We observed that LSTM models could predict lower limb joint torques during various activities accurately with relatively low error (root mean square error <= 0.14 Nm/kg, normalized root mean square error <= 8.7%) either through a uniform model or through ten separate models in intra-subject tests. Furthermore, a transfer learning technique was adopted in the inter-task and inter-subject tests to further improve the generalizability of LSTM models by pre-training a model on multiple subjects and/or tasks and transferring the learned knowledge to a target task/subject. Particularly in the inter-subject tests, we could predict joint torques accurately in several movements after training from only a few movements from new subjects.

  • 7.
    Zheng, Zhefen
    et al.
    Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China..
    Mo, Fuhao
    Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China..
    Liu, Tang
    Li, Xiaogai
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Neuronic Engineering.
    A Novel Neuromuscular Head-Neck Model and Its Application on Impact Analysis2021In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 29, p. 1394-1402Article in journal (Refereed)
    Abstract [en]

    Objective: Neck muscle activation plays an important role in maintaining posture and preventing trauma injuries of the head-neck system, levels of which are primarily controlled by the neural system. Thus, the present study aims to establish and validate a neuromuscular head-neck model as well as to investigate the effects of realistic neural reflex control on head-neck behaviors during impact loading. Methods: The neuromuscular head-neck model was first established based on a musculoskeletal model by including neural reflex control of the vestibular system and proprioceptors. Then, a series of human posture control experiments was implemented and used to validate the model concerning both joint kinematics of the cervical spine and neck muscle activations. Finally, frontal impact experiments of varying loading severities were simulated with the newly established model and compared with an original model to investigate the influences of the implanted neural reflex controllers on head-neck kinematic responses. Results: The simulation results using the present neuromuscular model showed good correlations with in-vivo experimental data while the original model even cannot reach a correct balance status. Furthermore, the vestibular reflex is noted to dominate the muscle activation in less severe impact loadings while both vestibular and proprioceptive controllers have a lot of effect in higher impact loading severity cases. Conclusions: In summary, a novel neuromuscular head-model was established and its application demonstrated the significance of the neural reflex control in predicting in vivo head-neck responses and preventing related injury risk due to impact loading.

  • 8.
    Zhou, Guang-Quan
    et al.
    Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China..
    Hua, Shi-Hao
    Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China..
    He, Yikang
    Southeast Univ, Zhongda Hosp, Dept Rehabil Med, Nanjing 210096, Peoples R China..
    Wang, Kai-Ni
    Southeast Univ, Sch Biol Sci & Med Engn, Nanjing 210096, Peoples R China..
    Zhou, Dandan
    Nanjing Univ Chinese Med, Affiliated Hosp Integrated Tradit Chinese & Wester, Dept Crit Care Med, Nanjing 210028, Peoples R China..
    Wang, Hongxing
    Southeast Univ, Zhongda Hosp, Dept Rehabil Med, Nanjing 210096, Peoples R China..
    Wang, Ruoli
    KTH, School of Engineering Sciences (SCI), Engineering Mechanics.
    Automatic Myotendinous Junction Identification in Ultrasound Images Based on Junction-Based Template Measurements2023In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 31, p. 851-862Article in journal (Refereed)
    Abstract [en]

    Tracking the myotendinous junction (MTJ) motion in consecutive ultrasound images is essential to assess muscle and tendon interaction and understand the mechanics' muscle-tendon unit and its pathological conditions during motion. However, the inherent speckle noises and ambiguous boundaries deter the reliable identification of MTJ, thus restricting their usage in human motion analysis. This study advances a fully automatic displacement measurement method for MTJ using prior shape knowledge on the Y-shape MTJ, precluding the influence of irregular and complicated hyperechoic structures in muscular ultrasound images. Our proposed method first adopts the junction candidate points using a combined measure of Hessian matrix and phase congruency, followed by a hierarchical clustering technique to refine the candidates approximating the position of the MTJ. Then, based on the prior knowledge of Y-shape MTJ, we finally identify the best matching junction points according to intensity distributions and directions of their branches using multiscale Gaussian templates and a Kalman filter. We evaluated our proposed method using the ultrasound scans of the gastrocnemius from 8 young, healthy volunteers. Our results present more consistent with the manual method in the MTJ tracking method than existing optical flow tracking methods, suggesting its potential in facilitating muscle and tendon function examinations with in vivo ultrasound imaging.

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