Leveraging electrocardiogram (ECG) signals for emotion recognition represents a core challenge in affective computing, particularly in achieving robustness across diverse demographic groups (such as older adults with mild cognitive impairment). This challenge is rooted in three key issues: the complex multi-scale nature of ECG signals, high inter-individual physiological variability, and the need for computationally efficient temporal modeling for IoT applications. To address these issues systematically, this study proposes HCMNet, a novel, physiologically-inspired hybrid Convolutional-Mamba network. HCMNet’s architecture is problem-driven: a hierarchical scale-aware convolutional module captures multi-scale features analogous to HRV analysis; an innovative Non-Local Channel Convolutional Attention (NLCCA) mechanism mitigates inter-individual variability by learning to reshape the feature space; and a Mamba2-based Bidirectional State-Space Model (BiSSM) efficiently models temporal dynamics with linear complexity. Additionally, we validated the model on a self-built Wearable ECG emotion dataset comprising healthy elderly individuals and patients with mild cognitive impairment (MCI), as well as on public datasets WESAD and DREAMER. Experimental results demonstrate that our proposed HCMNet, through its synergistic hybrid architecture, effectively extracts robust emotional features. It not only achieves state-of-the-art performance on public benchmarks but also exhibits strong robustness for special populations. Furthermore, our in-depth adaptation analysis reveals that while a “one-model-fits-all” approach is infeasible for unseen subjects, HCMNet excels as a robust transferable base model that can be rapidly personalized, offering a practical paradigm for accurate and adaptable emotion recognition in real-world IoT settings. The source code is available at https://github.com/INSOCE/HCMNet.

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals’ facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions.

Generating diverse and contextually appropriate facial reactions remains a significant challenge due to variability in individual responses, limited explainability, and insufficient modeling of contextual cues. In this study, we propose a multimodal framework that integrates behavioral memory, dynamic attention control, and cognitive style modeling to generate personalized and psychologically grounded facial reactions in dyadic interactions. Our method models the causal link between speaker behavior and listener response by incorporating frame-level behavioral cues, personality traits, and cognitive processing styles. The proposed system consists of three core components: a behavioral memory module that captures temporal context across conversation turns; a Personalized Personality Recognition Style (PPRS) module that infers cognitive tendencies via dual-path learning based on the Big Five personality traits; and a transformer-based generative module equipped with diffusion modeling and context-aware attention gating. This design enables the generation of expressive, individualized responses even during silence or scene transitions. We conduct extensive evaluations on the REACT2025 benchmark using the MARS dataset. Results show that our method outperforms state-of-the-art models in appropriateness (FRCorr ↑0.71), diversity (FRDiv ↑0.1405), and synchrony (FRSyn ↑47.77), ranking 1st in the offline track and 3rd in the online setting. These findings highlight the framework's effectiveness in simulating human-like, emotionally congruent reactions while offering interpretability grounded in personality psychology.

The combination of wearable sensors and competitive sports provides quantitative information for scientific training, effectively assisting athletes in improving their athletic performance. This study presents a technical framework for athletic sports assessment in competitive swimming based on body-area sensor networks. In our approach, wearable inertial sensor nodes are placed on specific body parts of the athletes to capture motion data during different competitive swimming strokes. Multiwearable inertial sensor nodes are worn on specific body parts of athletes for real-time monitoring motion data during training sessions. A motion intensity detection-based error-state-Kalman-filter algorithm is proposed for multisensor data fusion. Additionally, through kinematic statistical analysis, the characteristics of joint motion during training are clearly explained. Furthermore, a deep learning network that fuses sensor time series and human skeleton graphs is proposed for different stroke phase segmentation, enabling quantitative measurement of motion phases, and several baseline classifiers are chosen for comparison to validate the robustness of our phase segmentation method. We also investigate the sensor combination selection issue during the phase segmentation process to determine the optimal sensor configuration. Our approach provides a scientific solution for the integration of wearable sensors and competitive sports, contributing to the high-quality development of the next generation of smart sports.

Gait phase segmentation, pivotal for understanding lower limb motion, finds applications in diverse fields like medicine and sports. While existing method often struggle with accuracy and adaptability in real-world settings, this study presents a novel methodology employing particle filters for precise lower limb motion capture (MoCap) utilizing inertial sensors, which can be used in more everyday environments and in a wider range of applications over a longer period of time. The innovative approach adeptly tracks walking movements, labeling six gait phases via skeleton reconstruction facilitated by the MoCap algorithm. Subsequently, we propose a neural network architecture amalgamating temporal convolutional network (TCN), graph convolutional network (GCN), and long short-term memory (LSTM). This architecture integrates raw data from inertial sensors with joint angles derived from reconstructed motion, achieving accurate segmentation of the six gait phases. Experimental validation compares the MoCap algorithm against an optical motion capture system, and the neural network’s performance against state-of-the-art methods. Results demonstrate our method’s superior accuracy of 96.94%, highlighting its efficacy in addressing gait phase segmentation challenges and propelling advancements in gait analysis.

Conventional deep learning-based trajectory prediction methods always adopt offline training based on trajectory data collected in known scenes. Despite its high prediction accuracy, it is unable to process trajectory data acquired in real-time, making it non-trivial to adapt to unknown scenes. To mitigate the above problem, an online multi-source transfer learning-based pedestrian trajectory predictor, dubbed OMTL-PTP, is proposed to achieve fast adaptation of trajectory prediction. OMTL-PTP resorts to online transfer learning to transfer trajectory knowledge from multiple source domains to the target domain, enabling the model to learn from the new scene and continuously improve its trajectory prediction ability. Concretely, we propose several base learners with external memory modules to preserve source domain trajectory knowledge for online knowledge transfer. A multi-hop attention mechanism is introduced in each learner to handle the future uncertainty of generated trajectories. To fully utilize the knowledge from multiple source domains, OMTL-PTP leverages ensemble learning to transfer knowledge from multiple base learners in the source domains to the online learner and fine-tunes the online learner in the target domain. Specifically, all base learners are combined to update the online learner, improving its ability to process future arriving samples and adapt to unknown scenes quickly. Qualitative and quantitative evaluations on ETH/UCY indicate the effectiveness of OMTL-PTP in online learning, which is beneficial for deploying trajectory prediction methods on intelligent edge devices. The code will be released at https://github.com/zjrcczu/OMTL-PTP after acceptance.Note to Practitioners—This paper is motivated by the challenge of online sustained trajectory prediction for unmanned autonomous agents, but it also applies to other trajectory prediction tasks, such as intelligent monitoring. Existing approaches always collect trajectory data from different scenes for training, making the model generalize to other scenarios. However, they may suffer from performance degradation since they cannot learn trajectory knowledge from unknown scenes. This paper suggests a new approach by transferring trajectory knowledge from known scenes to unknown scenes and gradually learning from unknown scenes, inspired by online transfer learning. In this paper, we propose a trajectory predictor based on a memory network and introduce the multi-hop attention mechanism to mitigate future uncertainty of trajectory prediction. We then show how the external memory can preserve trajectory knowledge, which facilitates transferring knowledge from source domains to the target domain. Afterward, we train an online trajectory predictor based on online multi-source transfer learning, improving the generalization and adaptability of trajectory prediction models in unknown scenes and facilitating deploying trajectory prediction models in edge devices. This method also applies to other neural network-based regression tasks that require online sustained learning. In future research, we will improve the trajectory prediction performance while maintaining the online learning ability.

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64.43% and 80.40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77.09% and 80.18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance.

The goal of this paper is to alleviate the training cost for few-shot semantic segmentation (FSS) models. Despite that FSS in nature improves model generalization to new concepts using only a handful of test exemplars, it relies on strong supervision from a considerable amount of labeled training data for base classes. However, collecting pixel-level annotations is notoriously expensive and time-consuming, and small-scale training datasets convey low information density that limits test-time generalization. To resolve the issue, we take a pioneering step towards label-efficient training of FSS models from fully unlabeled training data, or additionally a few labeled samples to enhance the performance. This motivates an approach based on a novel unsupervised meta-training paradigm. In particular, the approach first distills pre-trained unsupervised pixel embedding into compact semantic clusters from which a massive number of pseudo meta-tasks is constructed. To mitigate the noise in the pseudo meta-tasks, we further advocate a robust Transformer-based FSS model with a novel prototype-based cross-attention design. Extensive experiments have been conducted on two standard benchmarks, i.e., PASCAL-5i and COCO-20i, and the results show that our method produces impressive performance without any annotations, and is comparable to fully supervised competitors even using only 20% of the annotations. Our code is available at: https://github.com/SSSKYue/UMTFSS.

In a closed environment lacking global positioning system (GPS) signals, how to achieve accurate navigation and positioning is a very challenging task. Zero velocity update (ZUPT) is a highly effective foot-mounted inertial pedestrian navigation systems in such environment. However, despite its effectiveness, the limitation of accurate detecting the zero-velocity-interval (ZVI) and heading drift are still the significant challenges of the ZUPT method. To address these issues, a deep learning method for adaptive ZVIs detection is established based solely on inertial sensors by comparing with the optical motion capture system. Additionally, an improved ZUPT-aided extend Kalman filter (EKF) divides the measurement updates of the ZVIs is established for multisensor data fusion, and the heading change with heuristic drift reduction (HDR) is also adopt as measurement, thereby yielding to limit the heading drift. Experimental results demonstrate that our method provides a better estimate of the heading angle, as well as more accurate ZVIs detection, leading to more precise dead-reckoning position estimates than other state-of-the-art methods.

Completing missions with autonomous decision-making unmanned aerial vehicles (UAV) is a development direction for future battlefields. UAV make decisions based on battlefield situation information collected by sensors and can quickly and accurately perform complex tasks such as path planning, cooperative reconnaissance, cooperative pursuit and attacks. Obtaining real-time situation information of enemy is the basis for realizing autonomous decision-making of the UAV. However, in practice, due to internal sensor failure or interference of enemy, the acquired situation information is prone to be missing, which affects the training and decision-making of autonomous UAV. In this paper, an adaptive missing situation data restoration method for UAV confrontation is proposed. The UAV confrontation situation data are acquired through JSBSim, an open-source UAV simulation platform. By fusing temporal convolutional network and long short-term memory sequences, we establish a deep regression method for missing data restoration and introduce an adaptive mechanism to reduce the training time of the restoration model in response to dynamic changes in the enemy’s strategy during UAV confrontation. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models under different degrees of data missing conditions. The performance of our method is quantified by five metrics. The performance of our proposed method is better than the other benchmark algorithms. The experimental results show that the proposed method can solve the missing data restoration problem and provide reliable situation data while effectively reducing the training time of the restoration model.

Depression is a common mental disorder that requires objective and valid assessment tools. However, purely data-driven methods cannot satisfy the clinical diagnostic criteria for automatic depression detection (ADD), and the instability and heterogeneity of multimodal data have not been fully resolved. Therefore, we propose a novel auxiliary tool for ADD based on multimodal purification fusion (MMPF). Initially, a prior constraint gating (PCG) strategy is used to inject doctors’ constraints into depression data to guide and constrain the learning process. Then, we introduce text and audio encoders to extract unpurified features from preprocessed depression data. Afterward, multimodal purification refinement is proposed to extract unintersected common and specific features from unpurified features, generating purified features. Meanwhile, we leverage a multiperspective contrastive learning (MCL) strategy to enhance unpurified and purified features. Finally, modality interaction (MI) based on the transformer is proposed to conduct multimodal fusion. A dynamic corrective learning (DCL) strategy is introduced to tackle modality imbalances and inconsistent sentiment. MMPF is evaluated on the Distress Analysis Interview Corpus Wizard of Oz and performs promisingly in unimodal and multimodal depression detection, indicating its significant role in ADD.

Humanoid robots often struggle to express the intricate and authentic facial expressions characteristic of humans, potentially hampering user engagement. To address this challenge, we introduce a comprehensive two-stage methodology to empower our autonomous affective robot with the capacity to exhibit rich and natural facial expressions. In the initial stage, we present an innovative action unit (AU) driven facial expression disentangled synthesis method, enabling the generation of nuanced robot facial expression images guided by AUs. By harnessing facial AUs within a framework of weakly supervised learning, we effectively surmount the scarcity of paired training data (comprising source and target facial expression images). To preserve the integrity of AUs while mitigating identity interference, we leverage a latent facial attribute space to disentangle expression-related and expression-unrelated cues, employing solely the former for expression synthesis. In the subsequent phase, we actualize an affective robot endowed with multifaceted degrees of freedom for facial movements, facilitating the embodiment of the synthesized fine-grained facial expressions. We devise a specialized motor command mapping network that serves as a conduit between the generated expression images and the robot's realistic facial responses. By utilizing the physical motor positions as constraints, we refine the prediction of precise motor commands from the robot's generated facial expressions. This refinement process ensures that the robot's facial movements authentically express accurate and natural expressions. Finally, qualitative and quantitative evaluations on the benchmarking Emotionet dataset verify the effectiveness of the proposed generation method. Results on the self-developed affective robot indicate that our method achieves a promising generation of specific facial expressions with given AUs, significantly enhancing the affective human–robot interaction.

The lower extremity exoskeleton, which can sense the neural motion state of the human body and then provide motion assistance, is gradually replacing the traditional wheelchairs and assistive devices, making many patients with disabilities or movement disorders able to regain the walking function. This survey provides a comprehensive review on recent technological advances in lower extremity neurorehabilitation exoskeleton from the perspectives of sensing, gait dynamics, and human–robot collaboration. For each technology category, a detailed comparison among state-of-the-art solutions is provided. The results show that the exoskeleton has been greatly improved in mechanical and learning ability. However, some issues, such as adaptability, safety, and efficiency still restrict the development of exoskeleton technology. To address these problems, the remaining open challenges and future directions to improve intelligence, sensing, gait analysis, trust, efficiency, generalization, and power consumption of exoskeleton are also presented and discussed.

Forecasting pedestrian trajectories in dynamic scenes remains a critical problem in various applications, such as autonomous driving and socially aware robots. Such forecasting is challenging due to human-human and human-object interactions and future uncertainties caused by human randomness. Generative model-based methods handle future uncertainties by sampling a latent variable. However, few studies explored the generation of the latent variable. In this work, we propose the trajectory predictor with pseudo Oracle (TPPO), which is a generative model-based trajectory predictor. The first pseudo oracle is pedestrians’ moving directions, and the second one is the latent variable estimated from ground truth trajectories. A social attention module is used to aggregate neighbors’ interactions based on the correlation between pedestrians’ moving directions and future trajectories. This correlation is inspired by the fact that pedestrians’ future trajectories are often influenced by pedestrians in front. A latent variable predictor is proposed to estimate latent variable distributions from observed and ground-truth trajectories. Moreover, the gap between these two distributions is minimized during training. Therefore, the latent variable predictor can estimate the latent variable from observed trajectories to approximate that estimated from ground-truth trajectories. We compare the performance of TPPO with related methods on several public datasets. Results demonstrate that TPPO outperforms state-of-the-art methods with low average and final displacement errors. The ablation study shows that the prediction performance will not dramatically decrease as sampling times decline during tests.

The ability of a humanoid robot to imitate facial expressions with simultaneous head motions is crucial to natural human–robot interaction. This mirrored behavior from human beings to humanoid robots has high demands of similarity and real-time performance. To fulfill these needs, this article proposes a real-time robotic mirrored behavior of facial expressions and head motions based on lightweight networks. First, a humanoid robot that can change the state of its facial organs and neck through servo displacement is developed to achieve the mirrored behavior of facial expressions and head motions. Second, to overcome the high latency caused by deep learning models running in embedded devices, a lightweight deep learning network is constructed for detecting facial feature points, which can reduce model size and improve running speed without affecting the performance of the model. Finally, a mapping relationship of 68 facial feature points to optimal servo displacements is established to realize the mirrored behavior from human beings to humanoid robots. The experimental results show that the facial feature point recognition method based on the lightweight model performs better than other state-of-the-art methods, and our head motion tracking method can maintain high accuracy compared with the gold standard optical motion capture system NOKOV. Overall, our method ensures the accurate and real-time generation of robot mirrored behavior and has a certain reference value for the efficient and natural interaction between humans and robots.

For dimensional emotion recognition, electroencephalography (EEG) signals and electrooculogram (EOG) signals are often combined to improve the performance of classifiers, as each of them provides complementary features to the other. In this article, we combine the EEG signal on the relevant channels with the EOG signal to boost the recognition accuracy. We first explore the mutual information (MI) of all EEG channels and only select emotion-related channels, i.e., channels with more MI are retained, since the emotion recognition performance can be degraded by the interference between uncorrelated channels, while the computational complexity is significant if all EEG channels are used for recognition. While the optimal lengths of EEG and EOG signals for emotion recognition are still uncertain, we systematically investigate the effects of time-window size on emotion recognition. This strategy not only increases the number of training samples, but also reduces the feature redundancy. At this stage, we not only extract multiple statistical features but also employ the increment entropy to find abrupt changes in EEG signals. The experimental results show that 13 out of 32 EEG channels were selected by the proposed channel selection algorithm, and these selected channels can already produce accurate emotion predictions. We found that using optimal time-windows to split EEG and EOG signals into several thin slices and then combine them can further enhance the emotion recognition performance, where the time-windows of 4, 5, 6, and 10 s allow the combined signals to achieve very high accuracy.

Wearable inertial motion capture, a new type of motion capture technology, mainly estimates the human posture in 3-D space through multisensor data fusion. The available method for sensor fusion is usually aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of a complex magnetic field environment. In this article, an extended Kalman filter (EKF) data fusion method is proposed to fuse the 9-axis sensor data. Meanwhile, the heuristic drift reduction (HDR) method is used to calibrate the accumulated error of a heading angle. In addition, the position in 3-D space is estimated by the foot-mounted zero-velocity-update (ZUPT) technique. Combining 3-D attitude and position, a biomechanical model of the human body is established to track the motion of a real human body. The EKF algorithm and position estimation methods are benchmarked against the golden standard, optical motion capture system, for various indoor experiments. In addition, various outdoor experiments are also conducted to verify the reliability of the proposed method. The results show that the proposed algorithm outperforms the available attitude estimation model in motion tracking and is feasible for 3-D human motion capture.

In order to improve the robustness of speech recognition systems, this study attempts to classify stressed speech caused by the psychological stress under multitasking workloads. Due to the transient nature and ambiguity of stressed speech, the stress characteristics is not represented in all the segments in stressed speech as labeled. In this paper, we propose a multi-feature fusion model based on the attention mechanism to measure the importance of segments for stress classification. Through the attention mechanism, each speech frame is weighted to reflect the different correlations to the actual stressed state, and the multi-channel fusion of features characterizing the stressed speech to classify the speech under stress. The proposed model further adopts SpecAugment in view of the feature spectrum for data augment to resolve small sample sizes problem among stressed speech. During the experiment, we compared the proposed model with traditional methods on CASIA Chinese emotion corpus and Fujitsu stressed speech corpus, and results show that the proposed model has better performance in speaker-independent stress classification. Transfer learning is also performed for speaker-dependent classification for stressed speech, and the performance is improved. The attention mechanism shows the advantage for continuous speech under stress in authentic context comparing with traditional methods.

Lots of evidence has indicated that many kinds of animals can achieve goal-oriented navigation by spatial cognition and dead reckoning. The geomagnetic field (GF) is a ubiquitous cue for navigation by these animals. Inspired by the goal-oriented navigation of animals, a novel long-distance underwater geomagnetic navigation (LDUGN) method is presented in this article, which only utilizes the declination component ( D ) and inclination component ( I ) of GF for underwater navigation without any prior knowledge of the geographical location or geomagnetic map. The D and I measured by high-precision geomagnetic sensors are compared periodically with that of the destination to determine the velocity and direction in the next step. A model predictive control (MPC) algorithm with control and state constraints is proposed to achieve the control and optimization of navigation trajectory. Because the optimal control is recalculated at each sampling instant, the MPC algorithm can overcome interferences of geomagnetic daily fluctuation, geomagnetic storms, ocean current, and geomagnetic local anomaly. The simulation results validate the feasibility and accuracy of the proposed algorithm.

Common spatial pattern (CSP) is widely used in motor imagery classification tasks. Classical CSP depends only on spatial filters. To improve its performance, a novel and efficient spatio-temporal filtering strategy is proposed in this paper to extract discriminative features. Common temporal filters are shared among all the spatial channels, so as to reduce the overfitting risk in the case of a small sample size. An efficient alternating optimization algorithm is also developed to optimize coefficients of spatial and temporal filters. To alleviate adverse effects of noise and artifacts and improve implementation efficiency, an ℓ1-norm-based sparsity regularization term is further introduced. The resulting problem is tackled by the reweighting technique. The effectiveness of the proposed algorithm is validated by the experiments using open datasets of BCI Competition.

Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological classification is meaningful for clinicians. The existing ADHD classification methods suffer from two problems, i.e., insufficient data and noise disturbance. Here, a high-accuracy classification method is proposed, which uses brain Functional Connectivity (FC) as material for ADHD feature analysis. In detail, we introduce a binary hypothesis testing framework as the classification outline to cope with insufficient data of ADHD database. Under binary hypotheses, the FCs of test data are allowed to use for training and thus affect the subspace learning of training data. To overcome noise disturbance, an l2,1-norm LDA model is adopted to robustly learn ADHD features in subspaces. The subspace energies of training data under binary hypotheses are then calculated, and an energy-based comparison is finally performed to identify ADHD individuals. On the platform of ADHD-200 database, the experiments show our method outperforms other state-of-the-art methods with the significant average accuracy of 97.6%.

Including mobile environment, conventional security mechanisms have been adapted to satisfy the needs of users. However, the device environment-IoT-based number of connected devices is quite different to the previous traditional desktop PC- or mobile-based environment. Based on the IoT, different kinds of smart and mobile devices are fully connected automatically via device controller, such as smartphone. Therefore, controller must be secure compared to conventional security mechanism. According to the existing security threats, these are quite different from the previous ones. Thus, the countermeasures applied should be changed. However, the smart device-based authentication techniques that have been proposed to date are not adequate in terms of usability and security. From the viewpoint of usability, the environment is based on mobility, and thus devices are designed and developed to enhance their owners’ efficiency. Thus, in all applications, there is a need to consider usability, even when the application is a security mechanism. Typically, mobility is emphasized over security. However, considering that the major characteristic of a device controller is deeply related to its owner’s private information, a security technique that is robust to all kinds of attacks is mandatory. In this paper, we focus on security. First, in terms of security achievement, we investigate and categorize conventional attacks and emerging issues and then analyze conventional and existing countermeasures, respectively. Finally, as countermeasure concepts, we propose several representative methods.

In this paper, we propose a novel sparse common spatial pattern (CSP) algorithm to optimally select channels of EEG signals. Compared to the traditional CSP, which maximizes the variance of signals in one class and minimizes the variance of signals in the other class, the classification accuracy is guaranteed by a constraint that the ratio of variances of signals in two different classes is lower bounded. Then, a sparse spatial filter is achieved by minimizing the l1-norm of filter coefficients and channels of EEG signals can be further optimized. The original nonconvex optimization problem is relaxed to a semidefinite program (SDP), which can be efficiently solved by well-developed numerical solvers. Experimental results demonstrate that the proposed algorithm can identify and discard about 50% channels with only 1% decrease of classification accuracy.

This paper presents an interactive training platform of motor learning using movement imitation and synchronous speech instruction. This platform enables a child with autism spectrum disorder (ASD) and a robot to imitate each other. A robot can ask a child to copy its action and instruct human how to adjust his/her action to match its action. A robot can also ask a child to coach it, which is able to elicit children's response to increase their communication. The platform is built up by a NAO humanoid robot that demonstrates actions, and a depth camera that captures child's actions. We scaled the skeleton tracking data in order to evaluate the consistence of actions between human and robot. The pilot tests on both children with and without ASD have shown that our framework is flexible and convenient for assisting intervention, and that the synchronous speech instructions to some extend facilitate children with ASD to perform their actions for motor learning.

In this paper, a novel algorithm is developed for the minimax design of IIR digital filters. Using a partial second-order factorization (PSOF), the denominator polynomial of an IIR digital filter is decomposed as a cascade of second-order factors (SOFs) and a single higher-order factor (HOF). This is inspired by the fact that, when some poles are closer to the boundary of the stability domain, the other poles tend to stay inside the stability domain such that the specified frequency response can be best approximated. By means of the PSOF, stability constraints are only imposed on a limited number of SOFs and, thus, a better design could be attained. The proposed algorithm successively updates SOFs and HOF. To further reduce its computational complexity, the iterative weighted least-squares approach is applied to optimize each SOF or HOF. Simulation results demonstrate that the proposed algorithm can attain the balance between computational efficiency and design accuracy.

A novel stability condition is developed in this paper. It is both necessary and sufficient, which ensures that optimal design cannot be excluded from the admissible solutions. Compared to other necessary and sufficient stability conditions, the proposed one can be expressed as a quadratic constraint in terms of denominator coefficients, which facilitates its combination with other widely used IIR filter design strategies. In this paper, we adopt the Steiglitz-McBride scheme to design IIR filters. In each iteration, an approximation version of the proposed stability condition is further expressed as a set of linear inequality constraints, such that the resulting design problem becomes a quadratic program that can be efficiently and reliably solved. Simulations demonstrate the effectiveness of the proposed stability condition.

In this paper, we present a novel algorithm to design sparse FIR digital filters in the minimax sense. To tackle the nonconvexity of the design problem, an efficient iterative procedure is developed to find a potential sparsity pattern. In each iteration, a subproblem in a simpler form is constructed. Instead of directly resolving these nonconvex subproblems, we resort to their respective dual problems. It can be proved that under a weak condition, globally optimal solutions of these subproblems can be attained by solving their dual problems. In this case, the overall iterative procedure can converge to a locally optimal solution of the original design problem. The real minimax design can then be achieved by refining the FIR filter obtained by the iterative procedure. The design procedure described above can be repeated for several times to further improve the sparsity of design results. The output of the previous stage can be used as the initial point of the subsequent design. Simulation results demonstrate the effectiveness of our proposed algorithm.