[Lower limb joint contact forces and ground reaction forces analysis based on Azure Kinect motion capture].

Traditional gait analysis systems are typically complex to operate, lack portability, and involve high equipment costs. This study aims to establish a musculoskeletal dynamics calculation process driven by Azure Kinect. Building upon the full-body model of the Anybody musculoskeletal simulation software and incorporating a foot-ground contact model, the study utilized Azure Kinect-driven skeletal data from depth videos of 10 participants. The in-depth videos were prepossessed to extract keypoint of the participants, which were then adopted as inputs for the musculoskeletal model to compute lower limb joint angles, joint contact forces, and ground reaction forces. To validate the Azure Kinect computational model, the calculated results were compared with kinematic and kinetic data obtained using the traditional Vicon system. The forces in the lower limb joints and the ground reaction forces were normalized by dividing them by the body weight. The lower limb joint angle curves showed a strong correlation with Vicon results (mean ρ values: 0.78 ~ 0.92) but with root mean square errors as high as 5.66°. For lower limb joint force prediction, the model exhibited root mean square errors ranging from 0.44 to 0.68, while ground reaction force root mean square errors ranged from 0.01 to 0.09. The established musculoskeletal dynamics model based on Azure Kinect shows good prediction capabilities for lower limb joint forces and vertical ground reaction forces, but some errors remain in predicting lower limb joint angles.

NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding.

In recent years, the paradigm of neural implicit representations has gained substantial attention in the field of Simultaneous Localization and Mapping (SLAM). However, a notable gap exists in the existing approaches when it comes to scene understanding. In this paper, we introduce NIS-SLAM, an efficient neural implicit semantic RGB-D SLAM system, that leverages a pre-trained 2D segmentation network to learn consistent semantic representations. Specifically, for high-fidelity surface reconstruction and spatial consistent scene understanding, we combine high-frequency multi-resolution tetrahedron-based features and low-frequency positional encoding as the implicit scene representations. Besides, to address the inconsistency of 2D segmentation results from multiple views, we propose a fusion strategy that integrates the semantic probabilities from previous non-keyframes into keyframes to achieve consistent semantic learning. Furthermore, we implement a confidence-based pixel sampling and progressive optimization weight function for robust camera tracking. Extensive experimental results on various datasets show the better or more competitive performance of our system when compared to other existing neural dense implicit RGB-D SLAM approaches. Finally, we also show that our approach can be used in augmented reality applications. Project page: https://zju3dv.github.io/nis_slam.

Characterizing upper limb motor dysfunction with temporal and spatial distribution of muscle synergy extracted from high-density surface electromyography.

Objective.To promote the development of objective and comprehensive motion function assessment for patients, based on high-density surface electromyography (HD-sEMG), this study investigates the temporal and spatial variations of neuromuscular activities related to upper limb motor dysfunction.Approach.Patients with unilateral upper limb motor dysfunction and healthy controls were enrolled in the study. HD-sEMG was collected from both arms while they were performing eight hand and wrist movements. Muscle synergies were extracted from the HD-sEMG. Symmetry of bilateral upper limb synergies and synergy differences between motions were proposed as spatial indicators to measure alterations in synergy spatial distribution. Additionally, as a temporal characteristic, the correlation of bilateral upper limb activation coefficient was proposed to describe the coordination control of the central nervous system. All temporal and spatial indicators were compared between patients and healthy subjects.Main results.The patients showed a significant decrease (p< 0.05) in the symmetry of bilateral upper limb synergy spatial distribution and correlation of bilateral upper limb activation coefficient. Patients with motor dysfunction also showed an increase in synergy similarity between motions, indicating altered spatial distribution of muscle synergies.Significance.These findings provide valuable insights into specific patterns associated with motor dysfunction, informing motor function assessment, and guiding targeted interventions and rehabilitation strategies for neurologically disordered patients.

ABR-Attention: An Attention-Based Model for Precisely Localizing Auditory Brainstem Response.

Auditory Brainstem Response (ABR) is an evoked potential in the brainstem's neural centers in response to sound stimuli. Clinically, characteristic waves, especially Wave V latency, extracted from ABR can objectively indicate auditory loss and diagnose diseases. Several methods have been developed for the extraction of characteristic waves. To ensure the effectiveness of the method, most of the methods are time-consuming and rely on the heavy workloads of clinicians. To reduce the workload of clinicians, automated extraction methods have been developed. However, the above methods also have limitations. This study introduces a novel deep learning network for automatic extraction of Wave V latency, named ABR-Attention. ABR-Attention model includes a self-attention module, first and second-derivative attention module, and regressor module. Experiments are conducted on the accuracy with 10-fold cross-validation, the effects on different sound pressure levels (SPLs), the effects of different error scales and the effects of ablation. ABR-Attention shows efficacy in extracting Wave V latency of ABR, with an overall accuracy of 96.76 ± 0.41 % and an error scale of 0.1ms, and provides a new solution for objective localization of ABR characteristic waves.

Assessment of TENS-Evoked Tactile Sensations for Transradial Amputees via EEG Investigation.

Most of current prostheses can offer motor function restoration for limb amputees but usually lack natural and intuitive sensory feedback. Many studies have demonstrated that Transcutaneous Electrical Nerve Stimulation (TENS) is promising in non-invasive sensation evoking for amputees. However, the objective evaluation and mechanism analysis on sensation feedback are still limited. This work utilized multi-channel TENS with diverse stimulus patterns to evoke sensations on four non-disabled subjects and two transradial amputees. Meanwhile, electroencephalogram (EEG) was collected to objectively assess the evoked sensations, where event-related potentials (ERPs), brain electrical activity maps (BEAMs), and functional connectivity (FC) were computed. The results show that various sensations could be successfully evoked for both amputees and non-disabled subjects by customizing stimulus parameters. The ERP confirmed the sensation and revealed the sensory-processing-related components like N100 and P200; the BEAMs confirmed the corresponding regions of somatosensory cortex were activated by stimulation; the FC indicated an increase of interactions between the regions of sensorimotor cortex. This study may shed light on how the brain responds to external stimulation as sensory feedback and serve as a pilot for further bidirectional closed-loop prosthetic control.

Finger-Individuating Exoskeleton System with Non-Contact Leader-Follower Control Strategy.

This paper proposes a novel finger-individuating exoskeleton system with a non-contact leader-follower control strategy that effectively combines motion functionality and individual adaptability. Our solution comprises the following two interactive components: the leader side and the follower side. The leader side processes joint angle information from the healthy hand during motion via a Leap Motion Controller as the system input, providing more flexible and active operations owing to the non-contact manner. Then, as the follower side, the exoskeleton is driven to assist the user's hand for rehabilitation training according to the input. The exoskeleton mechanism is designed as a universal module that can adapt to various digit sizes and weighs only 40 g. Additionally, the current motion of the exoskeleton is fed back to the system in real time, forming a closed loop to ensure control accuracy. Finally, four experiments validate the design effectiveness and motion performance of the proposed exoskeleton system. The experimental results indicate that our prototype can provide an average force of about 16.5 N for the whole hand during flexing, and the success rate reaches 82.03% in grasping tasks. Importantly, the proposed prototype holds promise for improving rehabilitation outcomes, offering diverse options for different stroke stages or application scenarios.

Microneedle Array Electrodes Fabricated With 3D Printing Technology for High-Quality Electrophysiological Acquisition.

Electrophysiological recordings are vital in assessing biological functions, diagnosing diseases, and facilitating biofeedback and rehabilitation. The applications of conventional wet (gel) electrodes often come with some limitations. Microneedle array electrodes (MAEs) present a possible solution for high-quality electrophysiological acquisition, while the prior technologies for MAE fabrication have been either complex, expensive, or incapable of producing microneedles with uniform dimensions. This work employed a projection stereolithography (P µ SL) 3D printing technology to fabricate MAEs with micrometer-level precision. The MAEs were compared with gel and flat electrodes on electrode-skin interface impedance (EII) and performances of EMG and ECG acquisition. The experimental results indicate that the P µ SL 3D printing technology contributed to an easy-to-perform and low-cost fabrication approach for MAEs. The developed MAEs exhibited promising EII and enabled a stable EMG and ECG acquisition in different conditions without inducing skin allergies, inflammation, or injuries. This research lies in the development of a type of customizable MAE with considerable biomedical application potentials for ultra-minimally invasive or non-invasive electrophysiological acquisition.

Smartphone videos-driven musculoskeletal multibody dynamics modelling workflow to estimate the lower limb joint contact forces and ground reaction forces.

The estimation of joint contact forces in musculoskeletal multibody dynamics models typically requires the use of expensive and time-consuming technologies, such as reflective marker-based motion capture (Mocap) system. In this study, we aim to propose a more accessible and cost-effective solution that utilizes the dual smartphone videos (SPV)-driven musculoskeletal multibody dynamics modeling workflow to estimate the lower limb mechanics. Twelve participants were recruited to collect marker trajectory data, force plate data, and motion videos during walking and running. The smartphone videos were initially analyzed using the OpenCap platform to identify key joint points and anatomical markers. The markers were used as inputs for the musculoskeletal multibody dynamics model to calculate the lower limb joint kinematics, joint contact forces, and ground reaction forces, which were then evaluated by the Mocap-based workflow. The root mean square error (RMSE), mean absolute deviation (MAD), and Pearson correlation coefficient (ρ) were adopted to evaluate the results. Excellent or strong Pearson correlations were observed in most lower limb joint angles (ρ = 0.74 ~ 0.94). The averaged MADs and RMSEs for the joint angles were 1.93 ~ 6.56° and 2.14 ~ 7.08°, respectively. Excellent or strong Pearson correlations were observed in most lower limb joint contact forces and ground reaction forces (ρ = 0.78 ~ 0.92). The averaged MADs and RMSEs for the joint lower limb joint contact forces were 0.18 ~ 1.07 bodyweight (BW) and 0.28 ~ 1.32 BW, respectively. Overall, the proposed smartphone video-driven musculoskeletal multibody dynamics simulation workflow demonstrated reliable accuracy in predicting lower limb mechanics and ground reaction forces, which has the potential to expedite gait dynamics analysis in a clinical setting.

Stretchable, Self-Healing, and Bioactive Hydrogel with High-Functionality N,N-bis(acryloyl)cystamine Dynamically Bonded Ag@polydopamine Crosslinkers for Wearable Sensors.

Hydrogels present attractive opportunities as flexible sensors due to their soft nature and tunable physicochemical properties. Despite significant advances, practical application of hydrogel-based sensor is limited by the lack of general routes to fabricate materials with combination of mechanical, conductive, and biological properties. Here, a multi-functional hydrogel sensor is reported by in situ polymerizing of acrylamide (AM) with N,N'-bis(acryloyl)cystamine (BA) dynamic crosslinked silver-modified polydopamine (PDA) nanoparticles, namely PAM/BA-Ag@PDA. Compared with traditional polyacrylamide (PAM) hydrogel, the BA-Ag@PDA nanoparticles provide both high-functionality crosslinks and multiple interactions within PAM networks, thereby endowing the optimized PAM/BA-Ag@PDA hydrogel with significantly enhanced tensile/compressive strength (349.80 kPa at 383.57% tensile strain, 263.08 kPa at 90% compressive strain), lower hysteresis (5.2%), improved conductivity (2.51 S m-1) and excellent near-infrared (NIR) light-triggered self-healing ability. As a strain sensor, the PAM/BA-Ag@PDA hydrogel shows a good sensitivity (gauge factor of 1.86), rapid response time (138 ms), and high stability. Owing to abundant reactive groups in PDA, the PAM/BA-Ag@PDA hydrogel exhibits inherent tissue adhesiveness and antioxidant, along with a synergistic antibacterial effect by PDA and Ag. Toward practical applications, the PAM/BA-Ag@PDA hydrogel can conformally adhere to skin and monitor subtle activities and large-scale movements with excellent reliability, demonstrating its promising applications as wearable sensors for healthcare.

Towards wearable and portable spine motion analysis through dynamic optimization of smartphone videos and IMU data.

BACKGROUND: Monitoring spine kinematics is crucial for applications like disease evaluation and ergonomics analysis. However, the small scale of vertebrae and the number of degrees of freedom present significant challenges for noninvasive and convenient spine kinematics estimation. METHODS: This study developed a dynamic optimization framework for wearable spine motion tracking at the intervertebral joint level by integrating smartphone videos and Inertia Measurement Units (IMUs) with dynamic constraints from a thoracolumbar spine model. Validation involved motion data from 10 healthy males performing static standing, dynamic upright trunk rotations, and gait. This data included rotations of ten IMUs on vertebrae and virtual landmarks from three smartphone videos preprocessed by OpenCap, an application leveraging computer vision for pose estimation. The kinematic measures derived from the optimized solution were compared against simultaneously collected infrared optical marker-based measurements and in vivo literature data. Solutions only based on IMUs or videos were also compared for accuracy evaluation. RESULTS: The proposed optimization approach closely matched the reference data in the intervertebral or segmental rotation range, demonstrating minimal angular differences across all motions and the highest correlation in 3D rotations (maximal Pearson and intraclass correlation coefficients of 0.92 and 0.94, respectively). Time-series changes of joint angles also aligned well with the optical-marker reference. CONCLUSION: Dynamic optimization of the spine simulation that integrates IMUs and computer vision outperforms the single-modality method. SIGNIFICANCE: This markerless 3D spine motion capture method holds potential for spinal health assessment in large cohorts in real-world settings without dedicated laboratories.

Staphylococcal enterotoxin B exposed to pregnant rats inhibits the hedgehog signaling pathway in thymic T lymphocytes of the offspring.

The Hedgehog (Hh) signaling pathway is involved in T cell differentiation and development and plays a major regulatory part in different stages of T cell development. A previous study by us suggested that prenatal exposure to staphylococcal enterotoxin B (SEB) changed the percentages of T cell subpopulation in the offspring thymus. However, it is unclear whether prenatal SEB exposure impacts the Hh signaling pathway in thymic T cells. In the present study, pregnant rats at gestational day 16 were intravenously injected once with 15 µg SEB, and the thymi of both neonatal and adult offspring rats were aseptically acquired to scrutinize the effects of SEB on the Hh signaling pathway. It firstly found that prenatal SEB exposure clearly caused the increased expression of Shh and Dhh ligands of the Hh signaling pathway in thymus tissue of both neonatal and adult offspring rats, but significantly decreased the expression levels of membrane receptors of Ptch1 and Smo, transcription factor Gli1, as well as target genes of CyclinD1, C-myc, and N-myc in Hh signaling pathway of thymic T cells. These data suggest that prenatal SEB exposure inhibits the Hh signaling pathway in thymic T lymphocytes of the neonatal offspring, and this effect can be maintained in adult offspring via the imprinting effect.

Exploring the emerging role of long non-coding RNAs (lncRNAs) in plant biology: Functions, mechanisms of action, and future directions.

Long non-coding RNAs (lncRNAs) are a class of RNA transcripts that surpass 200 nucleotides in length and lack discernible coding potential. LncRNAs that have been functionally characterized have pivotal functions in several plant processes, including the regulation of flowering, and development of lateral roots. It also plays a crucial role in the plant's response to abiotic stressors and exhibits vital activities in environmental adaptation. The progress in NGS (next-generation sequencing) and functional genomics technology has facilitated the discovery of lncRNA in plant species. This review is a brief explanation of lncRNA genomics, its molecular role, and the mechanism of action in plants. The review also addresses the challenges encountered in this field and highlights promising molecular and computational methodologies that can aid in the comparative and functional analysis of lncRNAs.

Robust Epileptic Seizure Detection Based on Biomedical Signals Using an Advanced Multi-View Deep Feature Learning Approach.

Epilepsy is a neurological disorder characterized by abnormal neuronal discharges that manifest in life-threatening seizures. These are often monitored via EEG signals, a key aspect of biomedical signal processing (BSP). Accurate epileptic seizure (ES) detection significantly depends on the precise identification of key EEG features, which requires a deep understanding of the data's intrinsic domain. Therefore, this study presents an Advanced Multi-View Deep Feature Learning (AMV-DFL) framework based on machine learning (ML) technology to enhance the detection of relevant EEG signal features for ES. Our method initially applies a fast Fourier transform (FFT) to EEG data for traditional frequency domain feature (TFD-F) extraction and directly incorporates time domain (TD) features from the raw EEG signals, establishing a comprehensive traditional multi-view feature (TMV-F). Deep features are subsequently extracted autonomously from optimal layers of one-dimensional convolutional neural networks (1D CNN), resulting in multi-view deep features (MV-DF) integrating both time and frequency domains. A multi-view forest (MV-F) is an interpretable rule-based advanced ML classifier used to construct a robust, generalized classification. Tree-based SHAP explainable artificial intelligence (T-XAI) is incorporated for interpreting and explaining the underlying rules. Experimental results confirm our method's superiority, surpassing models using TMV-FL and single-view deep features (SV-DF) by 4% and outperforming other state-of-the-art methods by an average of 3% in classification accuracy. The AMV-DFL approach aids clinicians in identifying EEG features indicative of ES, potentially discovering novel biomarkers, and improving diagnostic capabilities in epilepsy management.

Continuous Kalman Estimation Method for Finger Kinematics Tracking from Surface Electromyography.

Deciphering hand motion intention from surface electromyography (sEMG) encounters challenges posed by the requisites of multiple degrees of freedom (DOFs) and adaptability. Unlike discrete action classification grounded in pattern recognition, the pursuit of continuous kinematics estimation is appreciated for its inherent naturalness and intuitiveness. However, prevailing estimation techniques contend with accuracy limitations and substantial computational demands. Kalman estimation technology, celebrated for its ease of implementation and real-time adaptability, finds extensive application across diverse domains. This study introduces a continuous Kalman estimation method, leveraging a system model with sEMG and joint angles as inputs and outputs. Facilitated by model parameter training methods, the approach deduces multiple DOF finger kinematics simultaneously. The method's efficacy is validated using a publicly accessible database, yielding a correlation coefficient (CC) of 0.73. With over 45,000 windows for training Kalman model parameters, the average computation time remains under 0.01 s. This pilot study amplifies its potential for further exploration and application within the realm of continuous finger motion estimation technology.

Green Fruit Detection with a Small Dataset under a Similar Color Background Based on the Improved YOLOv5-AT.

Green fruit detection is of great significance for estimating orchard yield and the allocation of water and fertilizer. However, due to the similar colors of green fruit and the background of images, the complexity of backgrounds and the difficulty in collecting green fruit datasets, there is currently no accurate and convenient green fruit detection method available for small datasets. The YOLO object detection model, a representative of the single-stage detection framework, has the advantages of a flexible structure, fast inference speed and excellent versatility. In this study, we proposed a model based on the improved YOLOv5 model that combined data augmentation methods to detect green fruit in a small dataset with a background of similar color. In the improved YOLOv5 model (YOLOv5-AT), a Conv-AT block and SA and CA blocks were designed to construct feature information from different perspectives and improve the accuracy by conveying local key information to the deeper layer. The proposed method was applied to green oranges, green tomatoes and green persimmons, and the mAPs were higher than those of other YOLO object detection models, reaching 84.6%, 98.0% and 85.1%, respectively. Furthermore, taking green oranges as an example, a mAP of 82.2% was obtained on the basis of retaining 50% of the original dataset (163 images), which was only 2.4% lower than that obtained when using 100% of the dataset (326 images) for training. Thus, the YOLOv5-AT model combined with data augmentation methods can effectively achieve accurate detection in small green fruit datasets under a similar color background. These research results could provide supportive data for improving the efficiency of agricultural production.

Histone deacetylase Sir2 promotes the systemic Candida albicans infection by facilitating its immune escape via remodeling the cell wall and maintaining the metabolic activity.

Histone deacetylation affects Candida albicans (C. albicans) pathogenicity by modulating virulence factor expression and DNA damage. The histone deacetylase Sir2 is associated with C. albicans plasticity and maintains genome stability to help C. albicans adapt to various environmental niches. However, whether Sir2-mediated chromatin modification affects C. albicans virulence is unclear. The purpose of our study was to investigate the effect of Sir2 on C. albicans pathogenicity and regulation. Here, we report that Sir2 is required for C. albicans pathogenicity, as its deletion affects the survival rate, fungal burden in different organs and the extent of tissue damage in a mouse model of disseminated candidiasis. We evaluated the impact of Sir2 on C. albicans virulence factors and revealed that the Sir2 null mutant had an impaired ability to adhere to host cells and was more easily recognized by the innate immune system. Comprehensive analysis revealed that the disruption of C. albicans adhesion was due to a decrease in cell surface hydrophobicity rather than the differential expression of adhesion genes on the cell wall. In addition, Sir2 affects the distribution and exposure of mannan and ß-glucan on the cell wall, indicating that Sir2 plays a role in preventing the immune system from recognizing C. albicans. Interestingly, our results also indicated that Sir2 helps C. albicans maintain metabolic activity under hypoxic conditions, suggesting that Sir2 contributes to C. albicans colonization at hypoxic sites. In conclusion, our findings provide detailed insights into antifungal targets and a useful foundation for the development of antifungal drugs. IMPORTANCE: Candida albicans (C. albicans) is the most common opportunistic fungal pathogen and can cause various superficial infections and even life-threatening systemic infections. To successfully propagate infection, this organism relies on the ability to express virulence-associated factors and escape host immunity. In this study, we demonstrated that the histone deacetylase Sir2 helps C. albicans adhere to host cells and escape host immunity by mediating cell wall remodeling; as a result, C. albicans successfully colonized and invaded the host in vivo. In addition, we found that Sir2 contributes to carbon utilization under hypoxic conditions, suggesting that Sir2 is important for C. albicans survival and the establishment of infection in hypoxic environments. In summary, we investigated the role of Sir2 in regulating C. albicans pathogenicity in detail; these findings provide a potential target for the development of antifungal drugs.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation.

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Custom orthotic design by integrating 3D scanning and subject-specific FE modelling workflow.

The finite element (FE) foot model can help estimate pathomechanics and improve the customized foot orthoses design. However, the procedure of developing FE models can be time-consuming and costly. This study aimed to develop a subject-specific scaled foot modelling workflow for the foot orthoses design based on the scanned foot surface data. Six participants (twelve feet) were collected for the foot finite element modelling. The subject-specific surface-based finite element model (SFEM) was established by incorporating the scanned foot surface and scaled foot bone geometries. The geometric deviations between the scaled and the scanned foot surfaces were calculated. The SFEM model was adopted to predict barefoot and foot-orthosis interface pressures. The averaged distances between the scaled and scanned foot surfaces were 0.23 ± 0.09 mm. There was no significant difference for the hallux, medial forefoot, middle forefoot, midfoot, medial hindfoot, and lateral hindfoot, except for the lateral forefoot region (p = 0.045). The SFEM model evaluated slightly higher foot-orthoses interface pressure values than measured, with a maximum deviation of 7.1%. These results indicated that the SFEM technique could predict the barefoot and foot-orthoses interface pressure, which has the potential to expedite the process of orthotic design and optimization.

Living Synthelectronics: A New Era for Bioelectronics Powered by Synthetic Biology.

Bioelectronics, which converges biology and electronics, has attracted great attention due to their vital applications in human-machine interfaces. While traditional bioelectronic devices utilize nonliving organic and/or inorganic materials to achieve flexibility and stretchability, a biological mismatch is often encountered because human tissues are characterized not only by softness and stretchability but also by biodynamic and adaptive properties. Recently, a notable paradigm shift has emerged in bioelectronics, where living cells, and even viruses, modified via gene editing within synthetic biology, are used as core components in a new hybrid electronics paradigm. These devices are defined as "living synthelectronics," and they offer enhanced potential for interfacing with human tissues at informational and substance exchange levels. In this Perspective, the recent advances in living synthelectronics are summarized. First, opportunities brought to electronics by synthetic biology are briefly introduced. Then, strategic approaches to designing and making electronic devices using living cells/viruses as the building blocks, sensing components, or power sources are reviewed. Finally, the challenges faced by living synthelectronics are raised. It is believed that this paradigm shift will significantly contribute to the real integration of bioelectronics with human tissues.

A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals.

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.