Enhancing Prosthetic Control through High-Fidelity Myoelectric Mapping with Molecular Anchoring Technology.

Myoelectric control utilizes electrical signals generated from the voluntary contraction of remaining muscles in an amputee's stump to operate a prosthesis. Precise and agile control requires low-level myoelectric signals (below 10% of maximum voluntary contraction, MVC) from weak muscle contractions such as phantom finger or wrist movements, but imbalanced calcium concentration in atrophic skin can distort the signals. This is due to poor ionic-electronic coupling between skin and electrode, which often causes excessive muscle contraction, fatigue, and discomfort during delicate tasks. To overcome this challenge, a new strategy called molecular anchoring is developed to drive hydrophobic molecular effectively interact with and embed into stratum corneum for high coupling regions between ionic fluxes and electronic currents. The use of hydrophobic poly(N-vinyl caprolactam) gel has resulted in an interface impedance of 20 kΩ, which is 1/100 of a commercial acrylic-based electrode, allowing the detection of ultralow myoelectric signals (≈1.5% MVC) that approach human limits. With this molecular anchoring technology, amputees operate a prosthesis with greater dexterity, as phantom finger and wrist movements are predicted with 97.6% accuracy. This strategy provides the potential for a comfortable human-machine interface when amputees accomplish day-to-day tasks through precise and dexterous myoelectric control.

Towards Zero Retraining for Multiday Motion Recognition via a Fully Unsupervised Adaptive Approach and Fabric Myoelectric Armband.

Surface electromyogram pattern recognition (EMG-PR) requires time-consuming training and retraining procedures for long-term use, hindering the usability of myoelectric control. In this paper, we design a fabric myoelectric armband to reduce the electrode shifts. Furthermore, we propose a fully unsupervised adaptive approach called hybrid serial classifier (HSC) to eliminate the burden of retraining over multiply days. We investigated the performance of our approach with a dataset of ten types of forearm motion from ten male subjects over eight weeks (total ten days, including: from day 1 to day 7, day 14, day 28, day 56). The average inter-day classification accuracies of HSC without any new retraining data are 86.61% when trained exclusively with the first day's EMG data, and 94.77% when trained with other nine days' data. We compare our proposed HSC algorithm with linear discriminant analysis (LDA) without recalibration (BLDA) and supervised adaption LDA (ALDA) with just one trial of new retraining data. The inter-day classification accuracy of HSC is significantly higher than that of BLDA and ALDA. These results indicate that our novel armband sEMG device is feasible for long-term use in conjunction with the proposed HSC algorithm.

In-Situ Measuring sEMG and Muscle Shape Change with a Flexible and Stretchable Hybrid Sensor for Hand Gesture Recognition.

The accurate recognition of hand motion intentions is an essential prerequisite for efficient human-machine interaction (HMI) systems such as multifunctional prostheses and rehabilitation robots. Surface electromyography (sEMG) signals and muscle shape change (MSC) signals which are usually detected with different types of sensors have been used for human hand motion intention recognition. However, using different sensors to measure sEMG and MSC respectively, it would be inconvenient and add deploying difficulty for human-machine interaction systems. In this study, a novel flexible and stretchable sensor was fabricated with a nano gold conductive material, which could simultaneously sense both sEMG and MSC signals. Accordingly, a wireless signal acquisition device was developed to record both sEMG and MSC signals with the fabricated hybrid sensors. The performance of the proposed in-situ dual-mode signal measurement (IDSM) system was evaluated by the recording signal quality and the accuracy of hand gesture recognition. The results demonstrated that by using two pairs of the hybrid sensors, the proposed IDSM system could obtain two-channel sEMG at a noise level of about 0.89 µVrms and four-channel MSC with a resolution of about 0.1 Ω. For a recognition task of 11 classes of hand gestures, the results showed that only with two pairs of the hybrid sensors, the average accuracy over all the subjects was 95.6 ± 2.9%, which was about 7% higher than that with two-channel sEMG and six-channel accelerometer signals. These results suggest that the proposed IDSM method would be an efficient way to simplify the human-machine interaction system with fewer sensors for high recognition accuracy of hand motions.

Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction.

Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.

Highly Stable and Stretchable Conductive Films through Thermal-Radiation-Assisted Metal Encapsulation.

Stretchable conductors are the basic units of advanced flexible electronic devices, such as skin-like sensors, stretchable batteries and soft actuators. Current fabrication strategies are mainly focused on the stretchability of the conductor with less emphasis on the huge mismatch of the conductive material and polymeric substrate, which results in stability issues during long-term use. Thermal-radiation-assisted metal encapsulation is reported to construct an interlocking layer between polydimethylsiloxane (PDMS) and gold by employing a semipolymerized PDMS substrate to encapsulate the gold clusters/atoms during thermal deposition. The stability of the stretchable conductor is significantly enhanced based on the interlocking effect of metal and polymer, with high interfacial adhesion (>2 MPa) and cyclic stability (>10 000 cycles). Also, the conductor exhibits superior properties such as high stretchability (>130%) and large active surface area (>5:1 effective surface area/geometrical area). It is noted that this method can be easily used to fabricate such a stretchable conductor in a wafer-scale format through a one-step process. As a proof of concept, both long-term implantation in an animal model to monitor intramuscular electric signals and on human skin for detection of biosignals are demonstrated. This design approach brings about a new perspective on the exploration of stretchable conductors for biomedical applications.

Spasticity Assessment Based on the Maximum Isometrics Voluntary Contraction of Upper Limb Muscles in Post-stroke Hemiplegia.

Background: The assessment of muscle properties is an essential prerequisite in the treatment of post-stroke patients with limb spasticity. Most existing spasticity assessment approaches do not consider the muscle activation with voluntary contraction. Including voluntary movements of spastic muscles may provide a new way for the reliable assessment of muscle spasticity. Objective: In this study, we investigated the effectiveness and reliability of maximum isometrics voluntary contraction (MIVC) based method for spasticity assessment in post-stroke hemiplegia. Methods: Fourteen post-stroke hemiplegic patients with arm spasticity were asked to perform two tasks: MIVC and passive isokinetic movements. Three biomechanical signals, torque, position, and time, were recorded from the impaired and non-impaired arms of the patients. Three features, peak torque, keep time of the peak torque, and rise time, were computed from the recorded MIVC signals and used to evaluate the muscle voluntary activation characteristics, respectively. For passive movements, two features, the maximum resistance torque and muscle stiffness, were also obtained to characterize the properties of spastic stretch reflexes. Subsequently, the effectiveness and reliability of the MIVC-based spasticity assessment method were evaluated with spearman correlation analysis and intra class correlation coefficients (ICCs) metrics. Results: The results indicated that the keep time of peak torque and rise time in the impaired arm were higher in comparison to those in the contralateral arm, whereas the peak torque in the impaired side was significantly lower than their contralateral arm. Our results also showed a significant positive correlation (r = 0.503, p = 0.047) between the keep time (tk) and the passive resistant torque. Furthermore, a significantly positive correlation was observed between the keep time (tk) and the muscle stiffness (r = 0.653, p = 0.011). Meanwhile, the ICCs for intra-time measurements of MIVC ranged between 0.815 and 0.988 with one outlier. Conclusion: The findings from this study suggested that the proposed MIVC-based approach would be promising for the reliable and accurate assessment of spasticity in post-stroke patients.

A Pilot Study on Using Forcemyography to Record Upper-limb Movements for Human-machine Interactive Control.

Forcemyography (FMG) is a useful method to record real-time body motions, which has application potentials for human-machine interactive control. The FMG registers the change of force distribution in the normal direction on muscle surface during limb movements, and the body motions can be recognized by decoding the FMG patterns. In this study, we used FMG to record upper-limb movements and evaluated the influence of different configurations of signal channel and feature on motion classification performances. A four-channel wearable FMG acquisition system was developed to record seven upper-limb movements on each of six able-bodied subjects. The preliminary results showed that the signal channel number has significant influence on motion classification performance; however, the influence of signal feature number on motion classification was insignificant. In addition, the influence of channel combination and feature combination were also discussed in this paper. This work would support the application potential of FMG for body motion recording and may provide useful instructions for the application of FMG in human-machine interactive control.

Flexible Non-contact Electrodes for Bioelectrical Signal Monitoring.

Physiological signals such as ECG and EMG are closely related with human health and a long term monitoring of the physiological signals would be beneficial to detect possible disorders at the early stage. The wet electrodes currently used in the clinics require adhesives gels to record high-quality signals, which might cause discomfort of the patients and introduce some risk of skin allergy. Non-contact capacitive electrodes that can be operated without skin contact have been developed in previous studies, but these electrodes were rigid with electronic components on the back, which might not be an optimal solution for long term healthcare monitoring. In this study, a flexible non-contact electrode without any electronic components on both sides was designed for the long term acquisition of bioelectrical signals to maximize subject comfort. The flexible electrode was made up of multi-layer flexible printed circuits (FPC) materials and could be bent according to the local shape to achieve better non-contact capacitive coupling with the skin. The performance of the proposed flexible electrode was compared with that of the conventional wet electrodes in different signal recording conditions. The results showed that the proposed non-contact flexible electrode can capture high quality ECG and EMG signals, and its performance was comparable to the commonly used wet electrodes. This study might provide a reliable solution with great patient comfort for the long-term monitoring of biological signals.

A Novel Flexible Sensor for Muscle Shape Change Monitoring in Limb Motion Recognition.

Human limb movement intent recognition fundamentally provides the control mechanism for assistive devices such as exoskeleton and limb prosthesis. While different biopotential signals have been utilized for limb movement intent decoding, they seldom could account for spatial information associated with changes in muscle shape that could also be used to characterize the limb motor intent. Therefore, this study developed a novel nano gold stretchable-flexible sensor that captures spatial information associated with the muscle shape change signal (MSCS) during different muscle activation patterns. The novel sensor consists of 2-channels to acquire MSCS at a sampling rate of 125 Hz, corresponding to multiple classes of upper limb movements acquired across six able-bodied subjects. By utilizing the linear discriminant analysis algorithm on the acquired data with a single extracted feature, an overall average motion decoding accuracy of 90.9% was achieved. In addition, the waveform analysis results show that the novel sensor's recordings were less affected by external interferences, thus yielding high quality signals. This study is the first to utilize nano gold stretchable-flexible material for sensor fabrication in pattern recognition of upper limb movement intent, which may facilitate the development of effective assistive devices.

Normalization factor for the assessment of elbow spasticity with passive stretch measurement: maximum torque VS. body weight.

Spasticity of the elbow was generally assessed by repeated passive stretch movement, including the modified Ashworth Scale (MAS) from physiotherapist, and biomechanics analysis of the movement. The MAS-based method depends on the subjective evaluations and the performance of biomechanics analysis assessment is affected by the individual difference. Therefore, the normalization to reduce the individual difference for the assessment of spasticity is very important. In this study, the elbow spasticity was assessed with MAS by one skillful physiotherapist and biomechanics measurements during repetitive passive isokinetic movements at velocity of 60 degree$/$second. 20 post-stroke patients with elbow spasticity caused by hemorrhagic cerebral damage were divided into three groups according to the MAS grades (MAS $=1, 1+$, 2). The torque and position were recorded when the patients extension their elbows passively. The mean stiffness and the mean torque features of the passive isokinetic were calculated. Two normalization factors for biomechanics analysis assessment were investigated: body weight normalization factor and maximum isometrics volunteer contraction normalization factor. Spearman correlation analysis was used to investigate the relationship between the features and spasticity grades. The results showed that the correlation between MAS and two biomechanics features (mean stiffness, mean torque) were significant improved. For mean stiffness feature, the correlation coefficients were $-0.313, -0.563$ and -0.603 individually for non-normalization, body weight normalization and maximum isometrics volunteer contraction normalization. For mean torque feature, the correlation coefficients were $-0.260, -0.523$ and -0.691, respectively. These results suggest that the normalization methods would be helpful for the assessment of spasticity in biomechanics and will be a necessary way of spasticity estimation in clinical methods.

Quadruple H-Bonding Cross-Linked Supramolecular Polymeric Materials as Substrates for Stretchable, Antitearing, and Self-Healable Thin Film Electrodes.

Herein, we report a de novo chemical design of supramolecular polymer materials (SPMs-1-3) by condensation polymerization, consisting of (i) soft polymeric chains (polytetramethylene glycol and tetraethylene glycol) and (ii) strong and reversible quadruple H-bonding cross-linkers (from 0 to 30 mol %). The former contributes to the formation of the soft domain of the SPMs, and the latter furnishes the SPMs with desirable mechanical properties, thereby producing soft, stretchable, yet tough elastomers. The resulting SPM-2 was observed to be highly stretchable (up to 17 000% strain), tough (fracture energy ∼30 000 J/m2), and self-healing, which are highly desirable properties and are superior to previously reported elastomers and tough hydrogels. Furthermore, a gold, thin film electrode deposited on this SPM substrate retains its conductivity and combines high stretchability (∼400%), fracture/notch insensitivity, self-healing, and good interfacial adhesion with the gold film. Again, these properties are all highly complementary to commonly used polydimethylsiloxane-based thin film metal electrodes. Last, we proceed to demonstrate the practical utility of our fabricated electrode via both in vivo and in vitro measurements of electromyography signals. This fundamental understanding obtained from the investigation of these SPMs will facilitate the progress of intelligent soft materials and flexible electronics.