Rehabilitation Engineering Research Articles

A Bionic Skin for Health Management: Excellent Breathability, In Situ Sensing, and Big Data Analysis.

Developing an intelligent wearable system is of great significance to human health management. An ideal health-monitoring patch should possess key characteristics such as high air permeability, moisture-wicking function, high sensitivity, and a comfortable user experience. However, such a patch that encompasses all these functions is rarely reported. Herein, an intelligent bionic skin patch for health management is developed by integrating bionic structures, nano-welding technology, flexible circuit design, multifunctional sensing functions, and big data analysis using advanced electrospinning technology. By controlling the preparation of nanofibers and constructing bionic secondary structures, the resulting nanofiber membrane closely resembles human skin, exhibiting excellent air/moisture permeability, and one-side sweat-wicking properties. Additionally, the bionic patch is endowed with a high-precision signal acquisition capabilities for sweat metabolites, including glucose, lactic acid, and pH; skin temperature, skin impedance, and electromyographic signals can be precisely measured through the in situ sensing electrodes and flexible circuit design. The achieved intelligent bionic skin patch holds great potential for applications in health management systems and rehabilitation engineering management. The design of the smart bionic patch not only provides high practical value for health management but also has great theoretical value for the development of the new generation of wearable electronic devices.

Why Consider Geomorphology in River Rehabilitation?

River rehabilitation and ecological engineering are becoming critical issues for improving river status when ecological habitats and connectivity have been altered by human pressures. Amongst the range of existing rehabilitation options, some specifically focus on rebuilding fluvial forms and improving physical processes. The aim of this contribution is to illustrate how geomorphological expertise and process-based thinking contribute to river rehabilitation success. This semantic contribution is intended to feed the rehabilitation debate, particularly concerning the design of actions and the proposed references for monitoring target reaches and evaluating rehabilitation effects empirically. This article is also based on lessons learned from practical cases, mainly in gravel-bed rivers. Geomorphic understanding is needed at a local level to achieve an adequate diagnosis of river functioning, estimate human impacts and potential remnant river responsiveness, and to assess the gains and risks from rehabilitation, as well as to appraise success or failure through several pre- and post-project assessment strategies. Geomorphological studies can also be upscaled in a top-down manner (from high-order controls to small-scale processes, understanding detailed processes in their regional or basin-wide context), providing large-scale information at the regional, national, or even global level, information that can be used to diagnose the health of riverscapes in relation to local site-specific contexts. As such, geomorphological studies support strategic planning and prioritization of rehabilitation works according to specific contexts and river responsiveness, so as to move from opportunistic to objective-driven strategies.

Design and Implementation on Multi-Function Smart Wheelchair

With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair.

Bending stiffness tunability of biomimetic scale covered surfaces via scales orientations

Biomimetic fish scale structures on soft substrates can provide tailorable and tunable mechanical stiffness. When distributed over a reference 2D flat substrate, their simultaneous in-plane and out of plane sliding kinematics give rise to highly intricate geometrically dictated nonlinear and asymmetric behavior. Existing studies which focus on beam with filamentous structures, only partially reveal these complexities. Using finite element based numerical simulations, in this work we reveal the kinematics and bending mechanics of these structures. Scales are treated as stiff and rigid with flat plate-like geometry. We discover five distinct engagement patterns determined by the dihedral angles and overlap of the scales. These complexities are reflected in the multi-axial moment–curvature responses for bending revealing a rich landscape of tailorable and variable stiffness that can be exploited to create new types of flexible substrates which can significantly impact evolving frontiers of segmented armors, soft robotics, rehabilitation engineering and medium scale aerospace vehicles.

Can a previously co-designed device be used by others? A service evaluation of the use of the Sativex spray holder for individuals with multiple sclerosis

Purpose Co-design has previously been used to design custom assistive devices, involving the end user in the process to ensure the device meets their needs. From devices previously created, designs could be re-used and modified to meet variations in the needs of other individuals with similar clinical needs. This service evaluation explored the re-usability of a holder for helping administer the spray medication Sativex, for individuals with multiple sclerosis. Methods This evaluation was conducted in a UK based Rehabilitation Engineering NHS department. Five individuals who were currently prescribed Sativex trialled the device and provided feedback to further customise the device. Questionnaires evaluated the satisfaction and impact of the devices provided. The resources to provide the devices were calculated. Results Three of the five individuals who trialled the Sativex spray holder were using long term. Modifications to the shape of the holder were made due to differences in hand strength and dexterity from the initial user. Results indicated high satisfaction with the device and service provided, with improvements in the individuals’ competence, adaptability and self-esteem. The mean cost of providing and modifying the device was £78.62. Conclusions The previously co-designed Sativex spray holder was used by other individuals, demonstrating how a co-design framework can be used to identify user needs and modifications to previous designs and then implement design changes. The wider use of the device helped off-set the initial costs associated with co-designing devices. Further work is required to explore how other devices could be modified to meet individual needs.

An adaptive cross-class transfer learning framework with two-level alignment

Because of insufficient data, cross-class transfer learning has potential prospects in motor imagery electroencephalogram (MI-EEG) based rehabilitation engineering, and it has not been effectively addressed to reduce cross-class variability between source and target domains and find the best class-to-class transitive correspondence (CCTC). In this paper, we propose an adaptive cross-class transfer learning (ACTL) framework with two-level alignment (TLA) for MI decoding. By using the fast partitioning around medoids method, the central samples of each class are acquired from the source domain and target domain respectively. Then, they are applied to construct the central alignment matrix to realize the 1st-level domain alignment for any possible CCTC case. The 2nd-level subject alignment is performed by Euclidean alignment for each class of aligned source domain. Furthermore, the central samples of the target domain together with the two-level aligned source domain are mapped into tangent space, the resulting features are fed to a teacher convolutional neural network (TCNN), it and the transferred student CNN (SCNN) are trained successively, and their parameters of distillation loss are optimized automatically by a scaling-based grid search method. Experiments are conducted on a public MI-EEG dataset with multiple subjects and MI-tasks, the proposed framework can find the optimal SCNN associated with the best CCTC, which achieves a statistically significant average classification accuracy of 86.37%. The results suggest that TLA is helpful for increasing the distribution similarity between different domains, and the knowledge distillation embedded in ACTL framework greatly simplifies the SCNN and outperforms the complicated TCNN.

A Sign Language Learning Application for Children with Hearing Difficulties

The purpose of this paper is to develop a mobile application to support both sign language and literacy skills among children with hearing difficulties as part of rehabilitation engineering and to implement its basic functionality. This study will make it possible to support children with hearing difficulties in learning written language in combination with sign language, thereby emphasizing the importance of the latter, and in learning and communicating with their parents who use spoken language. This application has the following functions: (1) to register sign language clips acquired from a mobile device's camera by attaching tags to them, (2) to save multiple sign language clips using sets and tags, and (3) to reproduce and play back sequences of the saved sign language clips. Because of a preliminary evaluation experiment, the application was highly evaluated by the collaborators, and most of the negative comments were attributed to the small amount of registered data and its inability to support use in complex situations. Based on this, we consider that the basic concept of this application has been successfully realized.

Long-term locked knee ankle foot orthosis use: A perspective overview of iatrogenic biomechanical and physiological perils.

A knee ankle foot orthosis (KAFO) may be prescribed to the person with severe neuromusculoskeletal impairment of the lower limb to promote walking stability. The locked knee ankle foot orthosis (L-KAFO) is among the KAFO's routinely prescribed; however, long-term use of the L-KAFO is associated with musculoskeletal (arthrogenic and myogenic) and integumentary changes, and gait asymmetry with increased energy expenditure. Consequently, the risk of developing low back pain, osteoarthritis of the lower limbs and spinal joints, skin dermatitis, and ulceration increases, all of which impact quality of life. This article synthesizes the iatrogenic biomechanical and physiological perils of long-term L-KAFO use. It promotes using recent advances in rehabilitation engineering to improve daily activities and independence for proper patient groups.

Gas-permeable and stretchable on-skin electronics based on a gradient porous elastomer and self-assembled silver nanowires

On-skin electronics offers a noninvasive method for continuous and stable signal monitoring, which has played a significant role in healthcare and rehabilitation engineering. However, most present on-skin electronicses are highly restricted by their gas permeability and stretchability due to the materials, fabrication process, and cost. This study develops gas-permeable and stretchable on-skin electronics fabricated via a gradient porous elastomer and self-assembled silver nanowires. The introduction of a gradient porous structure offers the elastomer substrate a good water vapor permeability of 3073.3 g day−1 m−2, which is much higher than the average evaporation rate of water vapor on human skin (432 g day−1m−2). Additionally, the examined electronic device has no adverse effects on the skin and possesses good stretchability and flexibility (110%, 0.07778 MPa). In addition, it offers excellent fatigue resistance (0.70% electrical resistance change within 1000 stretch cycles). The electronic device achieves conformal and comfortable contact with human skin for a long period of wearing and accomplishes high-quality biopotential measurement with a high signal-to-noise ratio (SNR) during electromyography (EMG) signal detection. The proposed simple fabrication process may supply inspiration for new on-skin electronics used in biological signal detection and rehabilitation engineering.

Surface EMG decomposition based on innervation zone mapping and an LMMSE framework

Muscle motor units (MUs) can provide valuable information on neuromuscular control and muscle diseases, and electromyogram (EMG) decomposition is an effective method for reconstructing MUs. In this paper, a novel decomposition method based on innervation zone mapping (IZM) and a linear minimum mean square error estimation (LMMSE) framework is proposed for high-density surface EMG (HD-sEMG) decomposition. First, initial discharges were selected according to the IZM at each discharge. Then, the LMMSE framework and multistep iterative process were employed to update and estimate the innervation pulse train (IPT). Each discharge in the IPT was then classified into an individual MU action potential train (MUAPT) according to the IZM at each discharge. Finally, the MUAPTs with the same IZMs at the initial discharge were merged. The method based on the IZM and the LMMSE framework (IZM-LMMSE) was validated on both simulated and experimental data. By comparing the decomposition results of IZM-LMMSE and K-means clustering-modified CKC (KmCKC), the IZM-LMMSE algorithm can obtain more MUs and higher accuracy in most cases. Moreover, the experimental results show that the ratio of all common discharges obtained by IZM-LMMSE and KmCKC is 0.94 ± 0.01 % (mean ± std), which proves the effectiveness of the IZM-LMMSE method. The decomposition method has wide application prospects in rehabilitation engineering and clinical diagnosis.

Experimental investigation on the compressive mechanical properties of the non-aqueous reacting polymer with AE and DIC techniques

As a new type of non-excavation repair material, non-aqueous reacting polymer (NARP) is increasingly adopted in rehabilitation engineering. Properly evaluating the compressive mechanical properties of NARP with different densities is crucial for optimizing the repair design in engineering practices. However, due to a lack of experiments, the mechanism of the compressive mechanical properties of NARP influenced by the density is still not clear, and the constitutive model applicable for NARP with a wide range of density has also not been established yet. In this study, the compression characteristics of NARP with different densities under uniaxial compressive loading were comprehensively investigated, by using the acoustic emission (AE) and digital image correlation (DIC) techniques. Consequently, a statistical damage constitutive model considering the effect of density was established. The results showed that both the compressive yield strength and the elastic modulus of the NARP were strongly correlated with the density, and the correlated relationships can be characterized by two separate quadratic polynomial functions. The damage pattern of the NARP mainly changed from a ductile mode with low-density (ρ < 0.4 g/cm3) to a brittle mode with high-density (ρ > 0.4 g/cm3). The high-density NARP tends to exhibit a significant compressive brittleness. The AE signals were detected throughout the uniaxial compression experiments and the released AE activity was stronger at the same strain with a higher polymer density. Both the pre- and post- yield stages of NARP materials with different densities can be well characterized by using the proposed statistical damage model, being capable of considering the effect of density. The findings on the deformation and damage mechanism, together with the established constitutive model, have significant implications for the design of NARP in engineering practices.

Elbow Joint Stiffness Functional Scales Based on Hill's Muscle Model and Genetic Optimization.

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified "Reach and retrieve" task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill's muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.

Discernment on assistive technology for the care and support requirements of older adults and differently-abled individuals.

Assistive technology for the differently abled and older adults has made remarkable achievements in providing rehabilitative, adaptive, and assistive devices. It provides huge assistance for people with physical impairments to lead a better self-reliant daily life, in terms of mobility, education, rehabilitation, etc. This technology ranges from simple hand-held devices to complex robotic accessories which promote the individual's independence. This study aimed at identifying the assistance required by differently-abled individuals, and the solutions proposed by different researchers, and reviewed their merits and demerits. It provides a detailed discussion on the state of art assistive technologies, their applications, challenges, types, and their usage for rehabilitation. The study also identifies different unexplored research areas related to assistive technology that can improve the daily life of individuals and advance the field. Despite their high usage, assistive technologies have some limitations which have been briefly described in the study. This review, therefore, can help understand the utilization, and pros and cons of assistive devices in rehabilitation engineering and assistive technologies.

Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.

The role of human experience when making sense of brain monitoring: an interdisciplinary case study to assess wearable, non-invasive, brain-monitoring devices for rehabilitation

ABSTRACT Wearable, non-invasive, brain-monitoring technologies could take research outside the laboratory, to make discoveries about the human brain in different, and sometimes more relevant, settings for the specific research. While this could lead to new scientific discoveries, there is a need to understand how individuals benefit from and make sense of interactions with such technology. This study considered patient experiences with wearable, non-invasive, brain-monitoring devices in a rehabilitation context. The research project Patient-Centric Engineering in Rehabilitation (PACER) provided examples from research practice to assess and discuss the potential for using such devices in patient-centric rehabilitation for lower limb amputees. Project findings suggest that concepts from technology mediation theory strengthen the discussion by describing how relations with the technology differ, depending on whether you are a health professional, researcher or patient, which is an essential understanding when assessing novel technologies for practical applications.

Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint.

In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.

Portable Dynamometer-Based Measurement of Pelvic Floor Muscle Force

In attempts to improve the quality of life of women, continuous projects are sought between rehabilitation intervention and engineering. Using the knowledge of the pelvic floor muscle (PFM) physiology, assessment and training methods are developed to reduce lower urinary tract symptoms such as urinary incontinence. Therefore, this paper covers the design and implementation of a portable vaginal dynamometer. A PFM probe is designed, 3D printed, assembled, and tested in ten women to assess its acceptability and usability. The feedback from the usability study is used to optimize the PFM probe design. A vaginal dynamometer is developed based on the designed PFM probe, then tested for linearity, repeatability, hysteresis, noise and heat effect, and power consumption. The variability between the different produced PFM probe prototypes is evaluated. Force measurements are made using a load cell. Wireless communication is performed through a Bluetooth low energy transceiver v5.0, with a corresponding interface on both computer and smartphone. The device operates at a 3.3V supply and achieves a power consumption of 49.5 mW in operating mode. Two PFM probe sizes are designed to accommodate different vaginal hiatus sizes, based on usability study feedback. The proposed system allows the physiotherapist to wirelessly monitor variation in pelvic floor muscle force during assessment and/or training. The testing results showed that the newly designed system has the potential to measure the PFM function in functional conditions such as the standing position.

Less is More: Brain Functional Connectivity Empowered Generalisable Intention Classification with Task-relevant Channel Selection.

Electroencephalography (EEG) signals are gaining popularity in Brain-Computer Interface (BCI)-based rehabilitation and neural engineering applications thanks to their portability and availability. Inevitably, the sensory electrodes on the entire scalp would collect signals irrelevant to the particular BCI task, increasing the risks of overfitting in machine learning-based predictions. While this issue is being addressed by scaling up the EEG datasets and handcrafting the complex predictive models, this also leads to increased computation costs. Moreover, the model trained for one set of subjects cannot easily be adapted to other sets due to inter-subject variability, which creates even higher over-fitting risks. Meanwhile, despite previous studies using either convolutional neural networks (CNNs) or graph neural networks (GNNs) to determine spatial correlations between brain regions, they fail to capture brain functional connectivity beyond physical proximity. To this end, we propose 1) removing task-irrelevant noises instead of merely complicating models; 2) extracting subject-invariant discriminative EEG encodings, by taking functional connectivity into account. Specifically, we construct a task-adaptive graph representation of the brain network based on topological functional connectivity rather than distance-based connections. Further, non-contributory EEG channels are excluded by selecting only functional regions relevant to the corresponding intention. We empirically show that the proposed approach outperforms the state-of-the-art, with around 1% and 11% improvements over CNN-based and GNN-based models, on performing motor imagery predictions. Also, the task-adaptive channel selection demonstrates similar predictive performance with only 20% of raw EEG data, suggesting a possible shift in direction for future works other than simply scaling up the model.

IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors

IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors

IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information