A wearable neural interface for detecting and decoding attempted hand movements in a person with tetraplegia

A Novel Screen-Printed Textile Interface for High-Density Electromyography Recording

Recording electrical muscle activity using a dense matrix of detection points (high-density electromyography, EMG) is of interest in a range of different applications, from human-machine interfacing to rehabilitation and clinical assessment. The wider application of high-density EMG is, however, limited as the clinical interfaces are not convenient for practical use (e.g., require conductive gel/cream). In the present study, we describe a novel dry electrode (TEX) in which the matrix of sensing pads is screen printed on textile and then coated with a soft polymer to ensure good skin-electrode contact. To benchmark the novel solution, an identical electrode was produced using state-of-the-art technology (polyethylene terephthalate with hydrogel, PET) and a process that ensured a high-quality sample. The two electrodes were then compared in terms of signal quality as well as functional application. The tests showed that the signals collected using PET and TEX were characterised by similar spectra, magnitude, spatial distribution and signal-to-noise ratio. The electrodes were used by seven healthy subjects and an amputee participant to recognise seven hand gestures, leading to similar performance during offline analysis and online control. The comprehensive assessment, therefore, demonstrated that the proposed textile interface is an attractive solution for practical applications.

Silver Nanoparticle-Decorated Multiwalled Carbon Nanotube Ink for Advanced Wearable Devices

Silver nanoparticles of average size 12-13 nm were successfully decorated on the surface of multiwalled carbon nanotubes (MWCNTs) through a scalable wet chemical method without altering the structure of the MWCNTs. Employing this Ag@MWCNT, a multifunctional room-temperature curable conductive ink was developed, with PEDOT:PSS as the conductive binder. Screen printing of the ink could yield conductive planar traces with a 9.5 μm thickness and a conductivity of 28.99 S/cm, minimal surface roughness, and good adhesion on Mylar and Kapton. The versatility of the ink for developing functional elements for printed electronics was demonstrated by fabricating prototypes of a wearable strain sensor, a smart glove, a wearable heater, and a wearable breath sensor. The printed strain sensor exhibited a massive sensing range for wearable applications, including an impressive 1332% normalized resistance change under a maximum stretchability of 23% with superior cyclic stability up to 10 000 cycles. The sensor also exhibited an impeccable gauge factor of 142 for a 5% strain (59 for 23%). Furthermore, the sensor was integrated into a smart glove that could flawlessly replicate a human finger's gestures with a minimal response time of 225-370 ms. Piezoresistive vibration sensors were also fabricated by printing the ink on Mylar, which was employed to fabricate a smart mask and a smart wearable patch to monitor variations in human respiratory and pulmonary cycles. Finally, an energy-efficient flexible heater was fabricated using the developed ink. The heater could generate a uniform temperature distribution of 130 °C at the expense of only 393 mW/cm² and require a minimum response time of 20 s. Thus, the unique formulation of Ag@MWCNT ink proved suitable for versatile devices for future wearable applications.

Sensing and decoding the neural drive to paralyzed muscles during attempted movements of a person with tetraplegia using a sleeve array

Motor neurons convey information about motor intent that can be extracted and interpreted to control assistive devices. However, most methods for measuring the firing activity of single neurons rely on implanted microelectrodes. Although intracortical brain-computer interfaces (BCIs) have been shown to be safe and effective, the requirement for surgery poses a barrier to widespread use that can be mitigated by instead using noninvasive interfaces. The objective of this study was to evaluate the feasibility of deriving motor control signals from a wearable sensor that can detect residual motor unit activity in paralyzed muscles after chronic cervical spinal cord injury (SCI). Despite generating no observable hand movement, volitional recruitment of motor units below the level of injury was observed across attempted movements of individual fingers and overt wrist and elbow movements. Subgroups of motor units were coactive during flexion or extension phases of the task. Single digit movement intentions were classified offline from the EMG power (RMS) or motor unit firing rates with median classification accuracies >75% in both cases. Simulated online control of a virtual hand was performed with a binary classifier to test feasibility of real-time extraction and decoding of motor units. The online decomposition algorithm extracted motor units in 1.2 ms, and the firing rates predicted the correct digit motion 88 ± 24% of the time. This study provides the first demonstration of a wearable interface for recording and decoding firing rates of motor units below the level of injury in a person with motor complete SCI.

Solving musculoskeletal biomechanics with machine learning

Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning (ML) applications. In this study, we challenged general ML algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets, where joint angles were the input and the muscle length and moment arms were the output, were generated by our previous phenomenological model based on the autogenerated polynomial structures. Both models achieved a similar level of errors: ANN model errors were 0.08 ± 0.05% for muscle lengths and 0.53 ± 0.29% for moment arms, and LGB model made similar errors-0.18 ± 0.06% and 0.13 ± 0.07%, respectively. LGB model reached the training goal with only 103 samples, while ANN required 106 samples; however, LGB models were about 39 times slower than ANN models in the evaluation. The sufficient performance of developed models demonstrates the future applicability of ML for musculoskeletal transformations in a variety of applications, such as in advanced powered prosthetics.

Bilateral symmetry in ankle-muscle activation in transtibial amputees

By 2020, over 2.2 million people in the United States will be living with an amputated lower limb. The functional impact of amputations presents significant challenges in daily living activities. While significant work has been done to develop smart prosthetics, for the long-term development of effective and robust myoelectric control systems for transtibial amputees, there is still much that needs to be understood regarding how extrinsic muscles of the lower limb are utilized post-amputation. In this study, we examined muscle activity between the intact and residual limbs of three transtibial amputees with the aim of identifying differences in voluntary recruitment patterns during a bilateral motor task. We report that while there is variability across subjects, there are consistencies in the muscle recruitment patterns for the same functional movement between the intact and the residual limb within each subject. These results provide insights for how symmetric activation in residual muscles can be characterized and used to develop myoelectric control strategies for prosthetic devices in transtibial amputees.