From brain to movement: Wearables-based motion intention prediction across the human nervous system

Tang, Chenyu; Xu, Zhenyu; Occhipinti, Edoardo; Yi, Wei; Xu, Muzi; Kumar, Sanjeev; Virk, G.S.; Gao, Shuo; Occhipinti, Luigi G.

doi:10.1016/j.nanoen.2023.108712

Cited by 11 publications

(5 citation statements)

References 104 publications

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“…Its maximum regulation times remain in the range of 20-200 ms for 17 out of 18 cases (it is 360 ms only in case 5). These results are comparable to the reaction times obtained for EMG-based systems (130-300 ms) [ 38 – 41 ] and significantly better than the low-cost force sensor’s performance (410-1280 ms) [ 42 , 43 ]. However, the results are worse in most cases than for the ultrasensitive pressure sensors (43 ms) [ 44 ].…”

Section: Resultssupporting

confidence: 76%

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Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor

Falkowski,

Jeznach

2024

J NeuroEngineering Rehabil

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Exoskeleton-aided active rehabilitation is a process that requires sensing and acting upon the motion intentions of the user. Typically, force sensors are used for this. However, they increase the weight and cost of these wearable devices. This paper presents the methodology for detecting users’ intentions only with encoders integrated with the drives. It is unique compared to other algorithms, as enables active kinesiotherapy while adding no sensory systems. The method is based on comparing the measured motion with the one computed with the idealised model of the multibody system. The investigation assesses the method’s performance and its robustness to model and measurement inaccuracies, as well as patients’ unintended motions. Moreover, the PID parameters are selected to provide the optimal regulation based on the dynamics requirements. The research proves the presented concept of the control approach. For all the tests with the final settings, the system reacts to a change in the user’s intention below one second and minimises the changes in proportion between the system’s acceleration and the generated user’s joint torque. The results are comparable to those obtained by EMG-based systems and significantly better than low-cost force sensors.

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Section: Resultssupporting

confidence: 76%

“…Nevertheless, the proposed method does not increase the costs of the exoskeleton by adding EMG measuring systems or expensive sensors [ 38 – 41 , 44 ]. Neither does it increase the mass and bulkiness of the structure as for the budget force sensors [ 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor

Falkowski,

Jeznach

2024

J NeuroEngineering Rehabil

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“…[289] Researchers are continually exploring the specifics of natural neuronal pulses, and more precise instruments are consistently being developed. [290] It is known that most neuronal signal pulses range from 0.1 to 100 Hz. Gradually increasing pulse frequencies and reducing durations at different recovery stages can better facilitate neural recovery.…”

Section: Efficacymentioning

confidence: 99%

Advances in Material‐Assisted Electromagnetic Neural Stimulation

Sun,

Xiao,

Chen

et al. 2024

Advanced Materials

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Bioelectricity plays a crucial role in organisms, being closely connected to neural activity and physiological processes. Disruptions in the nervous system can lead to chaotic ionic currents at the injured site, causing disturbances in the local cellular microenvironment, impairing biological pathways, and resulting in a loss of neural functions. Electromagnetic stimulation has the ability to generate internal currents, which can be utilized to counter tissue damage and aid in the restoration of movement in paralyzed limbs. By incorporating implanted materials, electromagnetic stimulation can be targeted more accurately, thereby significantly improving the effectiveness and safety of such interventions. Currently, there have been significant advancements in the development of numerous promising electromagnetic stimulation strategies with diverse materials. This review provides a comprehensive summary of the fundamental theories, neural stimulation modulating materials, material application strategies, and pre‐clinical therapeutic effects associated with electromagnetic stimulation for neural repair. It offers a thorough analysis of current techniques that employ materials to enhance electromagnetic stimulation, as well as potential therapeutic strategies for future applications.This article is protected by copyright. All rights reserved

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“…In myoelectric control systems, sEMG signals are used to decipher the user's movement intentions, serving as input to control prosthetic limbs or exoskeletons 16 . The sEMG signal is generated approximately 80 ms before motion is exerted, primarily due to the electromechanical delay (EMD) inherent in these signals 17,18 . This characteristic allows myoelectric control systems to deliver timely assistance with reduced delays.…”

Section: Challenges Of Myoelectric Controlsmentioning

confidence: 99%

Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion

Currier,

FU,

Bayat

et al. 2024

Preprint

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Pattern recognition-based myoelectric control schemes aim to classify surface electromyographic (sEMG) signals generated by skeletal muscles for use in prosthetics or wearable robots. Many factors impact the EMG signal quality and reliability, such as motion artifacts, electrode shift, and reduced conductivity over time, which calls for robust pattern recognition-based myoelectric control schemes. The manifold hypothesis – a breakdown of high-dimensional space to a lower-dimensional representation to explain how the higher-dimensional space operates – provides a framework to discover the representation or manifold of multimodal biological signals. This paper presents a pattern recognition-based myoelectric control scheme that creates compressed latent space representations using a contrastive variational autoencoder (VAE) with an integrated classifier. The VAE model was designed, trained with a secondary dataset of hand gestures, and validated subjectwise as well as for the group. The individual subject data yielded distinct and separable latent spaces with high classification accuracy ranging from 75.1\% to 91.7\%. Further, the model was optimized to improve classification accuracy, reaching 87.45\% to 97.49\%. The model architecture was generalizable across the subjects, and the compressed latent space achieved high performance when the representation was separable and distinct. The proposed VAE model and latent space representation demonstrate its feasibility and utility for use in myoelectric controls.

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From brain to movement: Wearables-based motion intention prediction across the human nervous system

Cited by 11 publications

References 104 publications

Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor

Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor

Advances in Material‐Assisted Electromagnetic Neural Stimulation

Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion

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