Kyphosis refers to an abnormal increase in the forward curvature of the spine. Among the various types of kyphosis, postural kyphosis, also known as postural roundback, is the most prevalent. The condition arises from poor posture habits, such as slouching, leaning back in chairs and carrying heavy school bags, established during childhood, gradually weakening the muscles and soft tissues of the back. Over time, postural kyphosis can progress, resulting in a chronic deformity and persistent back pain. It typically becomes evident during adolescence, particularly in females. These effects can significantly impact the quality of life for individuals afflicted by the condition, both during their adolescent years and into adulthood. Among the treatment options available, bracing is frequently employed to prevent the progression of the deformity and facilitate correction. The Milwaukee brace, in particular, has been proven as an effective brace for subjects with postural kyphosis. However, one of the challenges associated with its use is wearer discomfort, which can contribute to reduced compliance and treatment efficacy. In this study, a soft textile brace with lightweight pneumatic paddings specifically designed for correcting the posture of individuals with postural kyphosis was developed. In order to evaluate the effectiveness of this new brace, subjects with the condition was recruited to participate in a wear trial. During the trial, head and shoulder posture were assessed including coronal head tilt angle, coronal shoulder angle, sagittal head tilt angle, craniovertebral angle and sagittal shoulder angle. The results revealed significant improvements in sagittal shoulder angle, the subjects wearing the pneumatic padding brace, compared to those without any bracing. These findings suggest that the newly developed brace holds promise for effectively managing postural kyphosis, as it demonstrated positive effects on improving rounded shoulder. By providing more comfort and potentially improving compliance, this brace offers a potential solution to enhance the overall treatment experience for individuals with postural kyphosis.

We present an innovative approach for hand landmark detection using a personalized soft wearable robot hand, facilitating task-oriented training in stroke rehabilitation. Our paper introduces the Ring-reinforced Soft Actuator (RSA), accommodating different hand sizes and enabling flexion and extension movements. Individually controlled finger actuators assist stroke patients in various grasping tasks. We also propose an automated method for landmark detection and joint measurement, enabling efficient development of customfit wearable robot hands. Additionally, we developed a wireless task board for seamless interaction with the soft robotic hand and placement of training objects. Evaluation with four stroke subjects demonstrated significant improvements in upper limb functions (FMA), hand motor abilities (ARAT, BBT), and maximum grip/pinch strength after 20 sessions of task-oriented training. These improvements persisted for at least three months post-training, highlighting the system's potential to enhance stroke rehabilitation and promote hand motor recovery. The personalized design allows for frequent hand practice and easy integration into regular rehabilitation routines.

Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities. Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites, neglecting multilevel pathological analysis of the overall nervous system and target organs. This has led to restrictions on current therapeutic approaches. In this paper, we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective, covering the central nervous system, peripheral nervous system, and target organs. After peripheral nerve injury, the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves; changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord. The nerve will undergo axonal regeneration, activation of Schwann cells, inflammatory response, and vascular system regeneration at the injury site. Corresponding damage to target organs can occur, including skeletal muscle atrophy and sensory receptor disruption. We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury. The main current treatments are conducted passively and include physical factor rehabilitation, pharmacological treatments, cell-based therapies, and physical exercise. However, most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway. Therefore, we should further explore multilevel treatment options that produce effective, long-lasting results, perhaps requiring a combination of passive (traditional) and active (novel) treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.

Soft artificial muscles possess inherent compliance and safety features, rendering them highly suitable for applications in wearable robots and unstructured environments. However, accurately modeling the nonlinearity of soft actuators proves to be a challenging task. In this paper, we present an adaptive control method that leverages model learning and model parameter backward adjustment. Our approach focuses on updating the dynamic model of the artificial muscles in two ways: by refining the input-output relation and by addressing prediction and control errors. To achieve this, we utilize tracking performance as a posterior evaluation metric for model parameter adjustment. Through a series of experiments, we demonstrate that our controller is capable of achieving reference tracking with a root mean square error (RMSE) of less than 5% across different stiffness levels. These experimental results validate the effectiveness of our proposed method in capturing the nonlinearity of soft artificial muscles, adapting to varying loads, and achieving precise reference tracking.

Improper positioning of the shoulder joint among children with unilateral shoulder motor impairment during handwriting has been shown to potentially hinder optimal musculoskeletal maturation and whole-body alignment progression. Typical manifestations include shoulder medial rotation, shoulder adduction, shoulder girdle depression and scapula protraction. These postural abnormalities are thought to arise from deficiencies in strength and control of the deltoid musculature on the affected side. Maintenance of improper alignment over extended periods of writing engagement throughout early developmental stages could theoretically propagate dysfunctional patterns with long-term effects on structuring and movement capability of affected anatomical regions. A wearable motion sensor that intended to be affixed above the deltoid muscle to continuously track relative shoulder position in real-time. Upon algorithmic identification of postural configurations outside predetermined reference ranges, the system initiates both tactile and auditory notifications. Such reminding signaling is aimed at eliciting an immediate proprioceptive response from the user to facilitate re-establishment of better alignment during writing tasks. The efficacy of the wearable device was assessed in children with unilateral shoulder motor impairment through comprehensive evaluations incorporating objective and subjective measures. Objective posture, range of motion and muscle strength assessments demonstrated improved postural alignment, increased joint mobility and enhanced strength following device use. Subjectively, children and caregivers reported positive perceptions of improved body symmetry and development. Both instrumented and experimental result suggest preliminary effectiveness of the wearable technology in positively imapcting range of movement and strength of performances over the study period. Initial results suggest the system capably encourages appropriate shoulder positioning during writing tasks, diminishing unwanted postural abnormalities. By facilitating balanced musculoskeletal development, the wearable intervention may contribute to mitigating associated disorders with longer use. Further research should explore the long-term effects on body development in this population.

It is essential to accurately identify gait phases when active exoskeleton devices assist with the lower limbs. This work focuses on IMU-based phase detection for stair ambulation. In order to enhance the detection sensitivity of phase transition, this work utilises the LSTM-CRF hybrid model. Four IMU sensors attached to the thighs and shanks on both legs were utilised to collect data during trials on ten healthy subjects for stair ascent and descent. The network's performance is evaluated by F1-score, recall (true positive rate), and precision, which are 96.3% on average with a standard deviation (std) of 1.9%, 96.6% on average with an std of 1.6%, and 95.9% on average with an std of 2.7%, respectively.

Hand extension is crucial for stroke survivors with spasticity, where their fingers become rigid and their thumb remains curled within the palm. Due to the underactuated nature of the hand, the dominance of flexor muscles over extensors, and the limited surface area available, developing an extension glove with thumb assistance poses a challenge for researchers. This paper introduces a fully wearable soft hand extension glove based on the X-pouch and strap system, addressing the above challenges. The glove enables adequate finger extension, thumb abduction, and extension for high MAS score patients. Modelling and testing revealed extension torques of up to 2.7 Nm at the MCP joint and 0.67 Nm at the PIP and DIP joints. Performance evaluation, including comparison with existing methods, demonstrated the glove’s superior extension capabilities using a model hand with realistic stiffness. Furthermore, the glove’s effectiveness was confirmed through testing on a stroke patient with MAS = 2, validating its on-body functionality.

Diabetic retinopathy (DR) is the leading cause of blindness among people of working age. Fundus lesions are clinical signs of DR, and their recognition and delineation are important for early screening, grading, and monitoring of the disease. We propose in this work a fully automatic deep convolutional neural network method for simultaneous segmentation of four different types of DR-related fundus lesions. To exploit multi-scale image information, we propose a collaborative architecture that comprises a contextual branch and a local branch. An attention mechanism is designed to fuse feature maps from all decoding layers in order to effectively and fully combine informative features from the two branches. Moreover, an auxiliary classification task with a novel supervision scheme is introduced to reduce model overfitting and further improve the accuracy of lesion segmentation. Extensive experiments are conducted using three public fundus datasets, and our method produces a mean AUC value of 0.677, 0.629, and 0.581 on them respectively. The results demonstrate the advantages of the proposed method, outperforming alternative strategies and other state-of-the-art methods in the literature.

To understand the mechanism of complex human sensorimotor control system, it is necessary to investigate the functional relationship between the cortex and peripheral movement. Typical cortico-peripheral interaction measures include corticomuscular coupling and corticokinematic coupling (CKC). Functional CKC reflects movement-related proprioceptive inputs, it could be through kinematics and physiological signals. The process consists of both linear and nonlinear properties of the whole neuromotor control system. Previous studies mainly applied linear techniques to assess the functional coupling between the cortex and periphery, but the nonlinear coupling properties were rarely discussed. In this preliminary study, 128 channels of electroencephalography (EEG) and 3-axis acceleration (ACC) signals were acquired for both dominant and non-dominant hand in four healthy participants during passive finger movement stimulus. Corticokinematic coherence and partial directed coherence analysis was performed to investigate linear coupling and directionality between EEG and ACC during movement, special concerns were focused at stimulus frequency (3Hz) and its harmonics. Further, we utilized phase transfer entropy to characterize top-down and bottom-up nonlinear cortico-peripheral information flow, and for the first time, we found typical nonlinear coupling patterns between the cortex and periphery during passive movement tasks. Such results might facilitate our understanding of human sensorimotor control system and imply potential clinical applications.

Medial prefrontal cortex (MPC) has been associated with a wide range of cognitive functions; however, its specific role in interference control is not fully understood. The current study investigates the role of MPC in interference control by externally stimulating it with an electric current and studying associated behavioral and neurophysiological markers. Participants randomly assigned to experimental and sham groups were administered with a high-definition transcranial direct current stimulation (HD-tDCS) of 2 mA for 15 min. They performed a classic color-word Stroop task before, during, and immediately after the stimulation, while electroencephalography (EEG) was acquired throughout the experiment. A decrease in reaction time (RT) for incongruent and neutral trials of the Stroop task was observed in the experimental group compared to the sham group with a significant reduction in the Stroop Effect after stimulation; however, no significant change was observed in the amplitude and latency of N200, P200, and N450 event related potentials. Furthermore, the resting state complexity of the neural signals in the medial frontal region was decreased in the experimental group with a decrease in theta frequency band during the Stroop task. We conclude that the stimulation of MPC increases its efficiency in resolving the conflict by reducing theta power during the Stroop task, which is also reflected in the reduced complexity in the resting state EEG. (ClinicalTrials.gov Identifier: NCT04318522)

Soft rehabilitation devices have been invented and applied for hand function recovery. In this paper, we propose a new Ring-reinforced 3D printed soft robotic hand, which combines hand rehabilitation and joint stiffness evaluation. The elastomer body of Ring-reinforced Soft-Elastic Composite Actuator (R-SECA) is 3D printed directly for fitting different sizes of fingers and the Iterative learning model predictive control (ILMPC) algorithm is used for controlling. Torque compensating layer inside R-SECA enables finger flexion and extension despite finger spasticity. Plastic rings are used to refrain radial expansion and reinforce the actuator. Bending angle and output tip force at different air pressure inputs are explored with four different R-SECA (120 mm, 112 mm, 96 mm, 72 mm length). Four-stroke survivors are recruited to evaluate the effectiveness of the soft robotic hand, and hand function improvement can be observed from the clinical evaluation data and stiffness evaluation outcomes.

Leukocyte differential test is a widely carried out clinical procedure for screening infectious diseases. Existing hematology analyzers require labor‐intensive work and a panel of expensive reagents. Herein, an artificial‐intelligence‐enabled reagent‐free imaging hematology analyzer (AIRFIHA) modality is reported that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two‐step residual neural network using label‐free images of isolated leukocytes acquired from a custom‐built quantitative phase microscope. By leveraging the rich information contained in quantitative phase images, not only high accuracy is achieved in differentiating B and T lymphocytes, but also CD4 and CD8 T cells are classified, therefore outperforming the classification accuracy of most current hematology analyzers. The performance of AIRFIHA in a randomly selected test set is validated and is cross‐validated across all blood donors. Due to its easy operation, low cost, and accurate discerning capability of complex leukocyte subpopulations, AIRFIHA is clinically translatable and can also be deployed in resource‐limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases.

Neuromuscular electrical stimulation (NMES) has been widely utilized in post-stroke motor restoration. However, its impact on the closed-loop sensorimotor control process remains largely unclear. This is the first study to investigate the directional changes in cortico-muscular interactions after repetitive rehabilitation training by measuring the noninvasive electroencephalogram (EEG) and electromyography (EMG) signals. In this study, 10 subjects with chronic stroke received 20 sessions of NMES-pedaling interventions, and each training session included three 10-min NMES-driven pedaling trials. In addition, pre- and post-intervention assessments of lower limb isometric contraction were conducted before and after the whole NMES-pedaling interventions. The EEG (128 channels) and EMG (3 bilateral lower limb sensors) signals were collected during the isometric contraction tasks for the paretic and non-paretic lower limbs. Both the cortico-muscular coherence (CMC) and generalized partial directed coherence (GPDC) values were analyzed between eight selected EEG channels in the central primary motor cortex and EMG channels. The results revealed significant clinical improvements. Additionally, rehabilitation training facilitated cortico-muscular interaction of the ipsilesional brain and paretic lower limbs (p = 0.004). Moreover, both the descending and ascending cortico-muscular pathways were altered after NMEStraining (p = 0.001, p < 0.001). Therefore, the results implied potential applications of EEG-EMG in understanding neuromuscular changes during the post-stroke motor rehabilitation process.

The interface contact between the active material and its neighboring metal electrodes dominates the sensing response of mainstream high-sensitivity piezoresistive pressure sensors. However, the properties of such interface are often difficult to control and preserve owing to the limited strategies to precisely engineer the surface structure and mechanical property of the active material. Here, a top-down fabrication method to create a grid-like polyurethane fiber-based spacer layer at the interface between a piezoresistive layer and its contact electrodes is proposed. The tuning of the period and thickness of the spacer layer is conveniently achieved by a programmable near-field electrospinning process, and the influence of the spacer structure on the sensing performance is systematically investigated. The sensor with the optimized spacer layer shows a widened sensing range (230 kPa) while maintaining a high sensitivity (1.91 kPa-1 ). Furthermore, the output current fluctuation of the sensors during a 74 000-cycle test is drastically reduced from 14.28% (without a spacer) to 3.63% (with a spacer), demonstrating greatly enhanced long-term reliability. The new near-field electrospinning-based strategy is capable of tuning sensor responses without changing the active material, providing a universal and scalable path to engineer the performances of contact-dominant sensors.

Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure. Therefore, a fully automated and reliable HCC segmentation system is in high demand. In this work, we present a novel hybrid neural network based on multi-task learning and ensemble learning techniques for accurate HCC segmentation of hematoxylin and eosin (H&E)-stained whole slide images (WSIs). First, three task-specific branches are integrated to enlarge the feature space, based on which the network is able to learn more general features and thus reduce the risk of overfitting. Second, an ensemble learning scheme is leveraged to perform feature aggregation, in which selective kernel modules (SKMs) and spatial and channel-wise squeeze-and-excitation modules (scSEMs) are adopted for capturing the features from different spaces and scales. Our proposed method achieves state-of-the-art performance on three publicly available datasets, with segmentation accuracies of 0.797, 0.923, and 0.765 in the PAIP, CRAG, and UHCMC&CWRU datasets, respectively, which demonstrates its effectiveness in addressing the HCC segmentation problem. To the best of our knowledge, this is also the first work on the pixel-wise HCC segmentation of H&E-stained WSIs.

Soft robots are considered intrinsically safe with regard to human–robot interaction. This has motivated the development and investigation of soft medical robots, such as soft robotic gloves for stroke rehabilitation. However, the output force of conventional purely soft actuators is usually limited. This restricts their application in stroke rehabilitation, which requires a large force and bidirectional movement. In addition, accurate control of soft actuators is difficult owing to the nonlinearity of purely soft actuators. In this study, a soft robotic glove is designed based on a soft-elastic composite actuator (SECA) that integrates an elastic torque compensating layer to increase the output force as well as achieving bidirectional movement. Such a hybrid design also significantly reduces the degree of nonlinearity compared with a purely soft actuator. A model-based online learning and adaptive control algorithm is proposed for the wearable soft robotic glove, taking its interaction environment into account, namely, the human hand/finger. The designed hybrid controller enables the soft robotic glove to adapt to different hand conditions for reference tracking. Experimental results show that satisfactory tracking performance can be achieved on both healthy subjects and stroke subjects (with the tracking root mean square error (RMSE) < 0.05 rad). Meanwhile, the controller can output an actuator–finger model for each individual subject (with the learning error RMSE < 0.06 rad), which provides information on the condition of the finger and, thus, has further potential clinical application.

Objectives: Soft robotic hands are proposed for stroke rehabilitation in terms of their high compliance and low inherent stiffness. We investigated the clinical efficacy of a soft robotic hand that could actively flex and extend the fingers in chronic stroke subjects with different levels of spasticity. Methods: Sixteen chronic stroke subjects were recruited into this single-group study. Subjects underwent 20 sessions of 1-hour EMG-driven soft robotic hand training. Training effect was evaluated by the pre-training and post-training assessments with the clinical scores: Action Research Arm Test(ARAT), Fugl-Meyer Assessment for Upper Extremity(FMA-UE), Box-and-Block test(BBT), Modified Ashworth Scale(MAS), and maximum voluntary grip strength. Results: For all the recruited subjects (n = 16), significant improvement of upper limb function was generally observed in ARAT (increased mean=2.44, P = 0.032), FMAUE (increased mean=3.31, P = 0.003), BBT (increasedmean=1.81, P = 0.024), and maximum voluntary grip strength (increased mean=2.14 kg, P < 0.001). No significant change was observed in terms of spasticity with the MAS (decreased mean=0.11, P = 0.423). Further analysis showed subjects with mild or no finger flexor spasticity (MAS<2, n = 9) at pre-training had significant improvement of upper limb function after 20 sessions of training. However, for subjects with moderate and severe finger flexor spasticity (MAS=2,3, n = 7) at pre-training, no significant change in clinical scores was shown and only maximum voluntary grip strength had significant increase. Conclusion: EMG-driven rehabilitation training using the soft robotic hand with flexion and extension could be effective for the functional recovery of upper limb in chronic stroke subjects with mild or no spasticity.

Agonist-antagonist coordination is essential to ensure the accuracy and stability of voluntary movement, which can be presented by time-varying coupling between agonist-antagonist electromyographic (EMG) signals. To discover the stroke-induced neurological change in paretic muscles, the wavelet coherence is firstly compared with coherence by simulated data and is utilized to represent the time-varying coupling of experimental data during elbow-tracking tasks. The simulation in this study demonstrates that the wavelet coherence is superior to coherence in the detection of short-time coupling between simulated signals. In addition, the experiment in this study is designed to explore the coupling between agonist-antagonist activations during the dynamic process. In the experiment, 10 post-stroke patients and 10 age-matched adults serving as controls were recruited and asked to perform elbow sinusoidal trajectory tracking tasks. Both the elbow angle and EMG signals of biceps and triceps were recorded simultaneously. Experimental results showed that wavelet coherence could represent the time-varying coupling between two EMG signals in the time-frequency domain, and its dynamic character was appropriate in the dynamic process to discover the functional coupling. According to the time and frequency analysis, the lower functional coupling in the post-stroke group and the obvious wavelet coherence difference between the two groups in the lower frequency range suggested a possible hypothesis mechanism that the weakening of coupling between agonist-antagonist muscles in the affected sides might in fact be stroke-induced damage in the direct corticospinal pathways.

In the last couple of years, active development of lower limb orthotic devices has mainly targeted individuals with spinal cord injury or stroke. However, due to the smaller population and lack of support from the wider community, there is a group of minorities in society suffering from poliomyelitis who do not benefit from these new technologies. They have unique gait patterns and rehabilitative needs which are very different from those with stroke or spinal cord injuries. To alleviate their difficulties in ambulation and pain, improving the gait performance of individuals with poliomyelitis through newly designed orthoses would be beneficial, our research team has designed a robotic knee orthosis equipped with an automatic knee-locking and actuating mechanism synchronized to the wearer's gait phase. In order to achieve better sensing accuracy, a 3D printed sensing insole with an optimized pressure-sensing module and a specific sensing logic different from existing orthoses has been introduced to identify foot pressure pattern and gait phase during ambulation. This chapter mainly focuses on how to design an exoskeleton robotic system to assist persons with poliomyelitis and to evaluate the effectiveness of the robotic knee orthosis in both clinical performance and gait analysis. A feasibility test has been carried out to investigate the efficacy of robotic knee orthoses on two subjects. Details of how to carry out the clinical training, controlled design of the robotic knee orthosis, and the results of training subjects are discussed.

Introduction: Globally, 300 million adults have clinical obesity. Heightened adiposity and inadequate musculature secondary to obesity alter bipedal stance and gait, diminish musculoskeletal tissue quality, and compromise neuromuscular feedback; these physiological changes alter stability and increase injury risk from falls. Studies in the field focus on obese patients across a broad range of body mass indices (BMI >30 kg/m2) but without isolating the most morbidly obese subset (BMI ≥40 kg/m2). We investigated the impact of obesity in perturbing postural stability in morbidly obese subjects elected for bariatric intervention, harboring a higher-spectrum BMI. Subjects and Methods: Traditional force plate measurements and stabilograms are gold standards employed when measuring center of pressure (COP) and postural sway. To quantify the extent of postural instability in subjects with obesity before bariatric surgery, we assessed 17 obese subjects with an average BMI of 40 kg/m2 in contrast to 13 nonobese subjects with an average BMI of 30 kg/m2. COP and postural sway were measured from static and dynamic tasks. Involuntary movements were measured when patients performed static stances, with eyes either opened or closed. Two additional voluntary movements were measured when subjects performed dynamic, upper torso tasks with eyes opened. Results: Mean body weight was 85% (p < 0.001) greater in obese than nonobese subjects. Following static balance assessments, we observed greater sway displacement in the anteroposterior (AP) direction in obese subjects with eyes open (87%, p < 0.002) and eyes closed (76%, p = 0.04) versus nonobese subjects. Obese subjects also exhibited a higher COP velocity in static tests when subjects’ eyes were open (47%, p = 0.04). Dynamic tests demonstrated no differences between groups in sway displacement in either direction; however, COP velocity in the mediolateral (ML) direction was reduced (31%, p < 0.02) in obese subjects while voluntarily swaying in the AP direction, but increased in the same cohort when swaying in the ML direction (40%, p < 0.04). Discussion and Conclusion: Importantly, these data highlight obesity’s contribution towards increased postural instability. Obese subjects exhibited greater COP displacement at higher AP velocities versus nonobese subjects, suggesting that clinically obese individuals show greater instability than nonobese subjects. Identifying factors contributory to instability could encourage patient-specific physical therapies and presurgical measures to mitigate instability and monitor postsurgical balance improvements.