It remains a formidable challenge to accurately recognize motion intentions of patients thus to control hand exoskeletons according to their volition. Current methods primarily focus on recognition of limited patient’s motion intentions, with the purpose of controlling preconfigured gestures of a hand exoskeleton for grasping objects. These methods exhibit a marked shortfall when encountering scenarios that are unexpected or not designed in advance, such as non-preprogrammed hand movements and object manipulation tasks. To tackle this issue, large language model (LLM) and speech recognition technology are employed in this study to allow the patient to control a hand exoskeleton at will. In particular, two LLMs are tailored to formulate codes of either generating non-preprogrammed gestures or dealing with unencountered objects. Additionally, an incremental learning framework is proposed to enable patients to perform both predefined and non-predefined operation tasks by integrating a natural language parser with the two LLM-based learners. The natural language parser can directly control the hand exoskeleton to perform predefined operations tasks from prestored command set, while the LLM-based learners can incrementally expand the control command set so as to enhance adaptability of the hand exoskeleton to complex activities over daily use. This study is a pioneering work in the field of hand exoskeletons, which will revolutionize the way to control hand exoskeletons. Furthermore, the proposed framework can be easily generalized to any other robots by modifying the prompt of customized LLMs, which provides a new idea to achieve autonomous learning in robotics.Note to Practitioners—The motivation of this article is to tackle the challenge of intention recognition for performing activities of daily living (ADLs) by stroke patients using a multi-degree of freedom hand exoskeleton. Existing methods for intention recognition so far can only be used for several tasks that are predefined in advance, thus none of them allow patients to control the hand exoskeleton completely at will. To surpass this limitation, an LLM-enabled incremental learning framework that integrates a hand exoskeleton controller with Large Language Model (LLM) is proposed and validated in this study. The framework offers patients an intuitive interface via voice interaction and enables patients to perform not only predefined operation tasks by the hand exoskeleton controller but also non-predefined ones that can be learned from the LLM. As a result, the hand exoskeleton controller continues to learn from the LLM, therefore is gradually able to perform all tasks in daily life. This pioneering study paves a new way in building patient-controlled hand exoskeletons with autonomous intelligence that can deal with non-predefined operation tasks in unstructured environments.

Accurate and prompt speed grasp intention recognition is crucial in online human-robot interaction (HRI). However, dynamic grasping relying on complete motion for high recognition accuracy will lead to an unavoidable delay in real-time prediction. To address this issue, we propose a Spatial Gating and Temporal Weighting Early Grasp Prediction (STEGP) method that utilizes incomplete dynamic grasping data from sliding windows to reduce the time delay for reliable robot teleoperation. The proposed method comprises a synergy-based feature extraction module, a spatial gating classification module, and a time-decay weighting fusion prediction module. The spatial-temporal mechanism with gating units effectively classifies sequential movements, achieving performance comparable to that of Transformers but being much easier to implement. Integrating a time-decay weighting frame enables reliable early prediction even with incomplete data. gMLP is chosen for the classification of hand dynamic grasping because of its high accuracy, realizing 93.83% accuracy for 33 grasping categories. The prediction tests demonstrated 85.4% accuracy, with the first 25% grasp completion across 28 subjects. Online robotic teleoperation grasp experiments achieved a 57.4% reduction in time delay and a 93.3% success rate.

In geological exploration, due to various factors, raw seismic data often corrupted by random noise, posing significant challenges to data fidelity, signal-to-noise ratio (SNR), and resolution. Traditional denoising techniques are limited by their inability to fully consider spatiotemporal correlations within seismic data, reliance on fixed filter parameters, and inadequate modeling of complex noise, and they have limited effectiveness and applicability when processing complex seismic signals. Therefore, this study proposes an innovative seismic data denoising model named seismic transformer generative adversarial network (STGAN). This model combines Transformer technology with generative adversarial networks (GANs) to effectively remove noise from seismic data. By incorporating the GAN, the model can effectively learn complex noise features in seismic data and generate cleaner, more realistic data. Meanwhile, the adoption of Transformers makes it possible to captures the temporal dependencies of seismic signals, thus significantly improving the accuracy and efficiency of data processing. The model employs serial and parallel structures in the generator to effectively extract multiscale features and achieve a balance between denoising effect and computational efficiency. Additionally, traditional batch normalization (BN) is replaced with batch renormalization (BRN) to further improve the stability of the model and optimize model performance. Comparative experiments on synthetic and field datasets demonstrate that the STGAN model has better denoising performance than classical methods such as bicubic interpolation, nonlocal means algorithm, and DnCNN, providing a more accurate basis for further interpretation and analysis of seismic data.

Assist-as-needed (AAN) assistance can promote active voluntary participation in rehabilitation and motor function recovery of post-stroke patients. However, different patients have personalized damaged regions and recovery states, causing difficulties to obtain adaptive and customized assistance in robot-assisted rehabilitation. This paper presents a mirror Adaptive Assist-As-Needed (AAAN) scheme, including two modules of Multi-Sensors Fused Estimation (MSFE) and Online Incremental Mirror Adaptation (OIMA), to encourage the subjects to actively participate in rehabilitation. Specifically, the first MSFE module can obtain the needed assistance based on the functional capability of the post-stroke patients via the data fusion of biological and motional signals using Kalman Filter. The second OIMA module fine-tunes the control torques estimated by MSFE to adapt the muscle fatigue and stiffness varieties of the affected limb based on the motion and physiological reference of the mirror healthy limb. The results demonstrate that the AAAN strategy can realize the transparent mode for healthy subjects and promote post-stroke patients to rehabilitate the affected limb with active participation using EMG signals 90.5% similar to those of the mirror healthy limb. The proposed method can be expected to greatly enhance power assistance and rehabilitation outcome of post-stroke patients using exoskeletons by provoking active participation. Note to Practitioners—For robotic rehabilitation, it is crucial to provide suitable assistances that can maximize the participation of post-stroke patients, which can promote the recovery outcome of therapies. The main purpose of this work is to achieve the adaptive assist-as-needed control strategy for upper limb rehabilitation tasks in two steps. Firstly, the elbow joint torques of a post-stroke patient are estimated by data fusion of motion and electromyography (EMG) signals using Kalman Filter, which can make up for the shortcomings of the individual signals, such as poor reliability and low sensitivity. Secondly, the joint motion and EMG signals of the mirror healthy limb are used as the reference to calculate the adaptive needed assistance to rehabilitate the affected limb. The preliminary experiments with healthy and post-stroke subjects demonstrate that this approach can obtain stable motion with the natural physiological states of subjects and enhance active voluntary participation in rehabilitation. In the future study, it will be investigated how to accelerate the adaptation of new patients based on the knowledge of the learned individuals using machine learning methods, such as lifelong learning and incremental learning.

It has been clinically proven that exoskeletons are effective self-training rehabilitation or daily living assistance devices for patients with hand dysfunctions. However, exoskeleton-assisted hand exercises with high degrees-of-freedom are considered as challenging tasks because the digit space, especially the thumb, cannot accommodate enough actuators. In this article, we report a tendon-driven soft hand exoskeleton with a hybrid configuration for thumb actuation. The soft hand exoskeleton system uses the least number of actuators to realize full degrees-of-freedom actuation for all digits. It is tested on a stroke patient with hemiplegia and a healthy subject. The experimental results show that the hand exoskeleton could assist the stroke patient to accomplish various training tasks, such as thumb encircling, grasping, pinching, releasing, and writing. It was found that digit trajectories and joint angle changes of the stroke patient were close to those of the healthy subject. Especially, the range of motion of the stroke patient shows significant improvement with the hand exoskeleton assistance compared to that without the hand exoskeleton assistance. The research in this article paves the way to develop fully actuated soft hand exoskeleton that can be eventually integrated with an electroencephalogram or electromyography for self-training rehabilitation or daily living assistance.

BACKGROUND: Combinatorial drug therapy for complex diseases, such as HSV infection and cancers, has a more significant efficacy than single-drug treatment. However, one key challenge is how to effectively and efficiently determine the optimal concentrations of combinatorial drugs because the number of drug combinations increases exponentially with the types of drugs. RESULTS: In this study, a searching method based on Markov chain is presented to optimize the combinatorial drug concentrations. In this method, the searching process of the optimal drug concentrations is converted into a Markov chain process with state variables representing all possible combinations of discretized drug concentrations. The transition probability matrix is updated by comparing the drug responses of the adjacent states in the network of the Markov chain and the drug concentration optimization is turned to seek the state with maximum value in the stationary distribution vector. Its performance is compared with five stochastic optimization algorithms as benchmark methods by simulation and biological experiments. Both simulation results and experimental data demonstrate that the Markov chain-based approach is more reliable and efficient in seeking global optimum than the benchmark algorithms. Furthermore, the Markov chain-based approach allows parallel implementation of all drug testing experiments, and largely reduces the times in the biological experiments. CONCLUSION: This article provides a versatile method for combinatorial drug screening, which is of great significance for clinical drug combination therapy.

Reinforcement learning (RL) is an efficient learning approach to solving control problems for a robot by interacting with the environment to acquire the optimal control policy. However, there are many challenges for RL to execute continuous control tasks. In this article, without the need to know and learn the dynamic model of a robotic manipulator, a kernel-based dynamic model for RL is proposed. In addition, a new tuple is formed through kernel function sampling to describe a robotic RL control problem. In this algorithm, a reward function is defined according to the features of tracking control in order to speed up the learning process, and then an RL tracking controller with a kernel-based transition dynamic model is proposed. Finally, a critic system is presented to evaluate the policy whether it is good or bad to the RL control tasks. The simulation results illustrate that the proposed method can fulfill the robotic tracking tasks effectively and achieve similar and even better tracking performance with much smaller inputs of force/torque compared with other learning algorithms, demonstrating the effectiveness and efficiency of the proposed RL algorithm.

Bio-syncretic robots made up of living biological systems and electromechanical systems may have the potential excellent performance of natural biological entities. Therefore, the study of the bio-syncretic robots has got lots of attention in recent years. The 3D skeletal muscles have been used widely, due to the considerable contraction force and the controllability. However, the low differentiation quality of the C2C12 in the tissues hinders the broad application in the development of the skeleton muscle actuated bio-syncretic robots. In this work, an approach based on circular mould and rotary electrical stimulation to build high-quality muscle rings, which can be used to actuate various bio-syncretic robots, has been proposed. Firstly, the advantage of the proposed circular mould for the muscle rings culture has been shown by simulation. Then, the muscle rings have been fabricated with different moulds using the experiment-optimized compositions of the biological mixture. After that, the muscle rings in the circular moulds with different electrical stimulations have been cultured, to show the superiority of the proposed rotary electrical stimulation. Moreover, the contractility of the muscle rings have been measured under the different electrical pulses stimulation, for the study of the control property of the muscle rings. This work may be meaningful not only the development of bio-syncretic robots actuated by 3D muscle tissues but also the muscle tissue engineering.

In recent years, reinforcement learning has been developed dramatically and is widely used to solve control problems, e.g., playing games. However, there are still some problems for reinforcement learning to perform robotic control tasks. Fortunately, the kernel trick-based methods provide a chance to deal with those challenges. This work aims at developing a kernel trick-based learning control method to carry out robotic tracking control tasks. A reward system, in this work, is presented in order to speed up the learning processes. And then, a kernel trick-based reinforcement learning tracking controller is presented to perform tracking control tasks on a robotic manipulator system. To evaluate the policy and assist the reward system to accelerate the speed of finding the optimal control policy, a critic system is introduced. Finally, from the comparison with the benchmark, the simulation results illustrate that our algorithm has faster convergence rate and can execute tracking control tasks effectively, the reward function and the critic system proposed in this work is efficient.

Micro-robots have a great application prospect in the biomedical field due to the feature of small size. To solve the issues of energy supply and bio-compatibility of micro-robots, bio-syncretic micro-robots composed of biological materials and electromechanical systems have been studied widely. The skeletal muscle is a potential material to develop bio-actuator for the bio-syncretic robots on account of the great contraction force and the controllability. However, the low differentiation quality of C2C12s and the control of the bio-syncretic robots are the two of the main challenges for the development of the bio-syncretic robots based on the skeleton muscle. In this paper, an approach based on circularly distributed multiple electrodes (CDMEs) was proposed to improve the differentiation of C2C12 myoblast cells and characterize the electro-responsive beating behavior of myotubes for the development of bio-syncretic robots. Three groups of C2C12 blasts were used to fulfill the differentiation experiments without electrical stimulation and with electrical stimulation using parallel electrodes and CDMEs respectively, for evaluating the effect of CDMEs on C2C12 differentiation. It was demonstrated that electrical field through CDMEs can improve the differentiation quality of C2C12 blasts into myotubes in terms of intensity, length, and widths. Then, the effect of electrical stimulation on the beating behaviors of myotubes was also investigated with CDMEs, and it was shown that the beating amplitudes of myotubes were significantly affected by the frequencies, amplitude and direction of electrical stimulation with respect to the myotubes, which is fundamental for the control of the micro-robot based on skeletal muscle cells. The proposed approach is useful for not only the development of the bio-syncretic robots, but also the study of muscle tissue engineering.

Bio-syncretic robots, consisting of living biological materials and traditional electromechanical systems, have attracted lots of attention due to the potentialities of self-sensing, self-actuation and self-repairing with intrinsic safety and high energy conversion efficiency. However, most of current researches focus on the movement of the devices, and have ignored the study on the control of actuation unit “cells”, which is as important as motors for traditional electromechanical robots. In this work, the effects of cell culturing time, seeding concentration and functional drugs (cytochalasin and adrenalin) on contractile frequency and force strength of cardiomyocytes have been studied using scanning ion conductance microscope (SICM) and arrays of micro-pillars made of PDMS. This work will lay the foundation for the further study of quantitatively control of bio-syncretic robots actuated by cardiomyocytes and is also meaningful for the development of cytology, medicine, and clinical science.

Cellular mechanical properties as the main physical performance characteristics have been actively studied in the past years for the study of cytobiology and the development of medicine. In this study, by combining Hertz model, a novel strategy is proposed to simultaneously measure the cellular mechanical properties including cellular mass, elasticity and viscosity, based on the principle of forced vibration stimulated by simple harmonic force, with piezoelectric transducer (PZT) as vibrator and Atomic Force Microscope (AFM) as detector. The corresponding theoretical model was derived and the simulation was realized based on the proposed model. The experiments of indentations and vibrations with myoblasts and myotubes were implemented to calculate the three mechanical parameters of cells according to the proposed strategy. The results validated the proposed approach. This work would be useful for the development of cytology, medicine, previously diagnose, specific therapy and so on.

The nonlinearities such as hysteresis and creep are the major factors inherent in PZT actuation that affect the tip positioning precision and manipulation performance of the AFM system. In this study, an extended PI model is generalized by introducing a creep model to the basic hysteretic operator of the PI model at the inflexion point of the hysteresis loop. Unified compensation for hysteresis and creep can be implemented with the extended PI model. Experiment results demonstrate the validity and effectiveness of the extended PI model and it is implied that the inflexion creep compensation not only improves the tip positioning precision at the inflexion points on the hysteresis loops, but also the localization effectiveness during the whole process of PZT actuation.

Behavior of living organisms is strongly modulated by the day and night cycle giving rise to a cyclic pattern of activities. Such a pattern helps the organisms to coordinate their activities and maintain a balance between what could be performed during the "day" and what could be relegated to the "night." This cyclic pattern, called the "Circadian Rhythm," is a biological phenomenon observed in a large number of organisms. In this paper, our goal is to analyze transcriptome data from Cyanothece for the purpose of discovering genes whose expressions are rhythmic. We cluster these genes into groups that are close in terms of their phases and show that genes from a specific metabolic functional category are tightly clustered, indicating perhaps a "preferred time of the day/night" when the organism performs this function. The proposed analysis is applied to two sets of microarray experiments performed under varying incident light patterns. Subsequently, we propose a model with a network of three phase oscillators together with a central master clock and use it to approximate a set of "circadian-controlled genes" that can be approximated closely.

Behavior of living organisms is strongly modulated by light especially by the day and night cycle giving rise to a cyclic pattern of activities. Such a pattern helps the organism to coordinate their activities and maintain a balance between what could be performed during the ‘day’ and what could be relegated to ‘night’. This cyclic pattern, called the ‘Circadian Rhythm’, is a biological phenomenon observed in a large number of organisms ranging from unicellular bacteria to human beings and is present in data collected at various levels viz. transcriptome, proteome etc. In this paper, our goal is to analyze transcriptome data from Cyanothece, a photosynthetic cyanobacteria, for the purpose of discovering genes whose expressions are rhythmic, especially those for which these rhythms have a 24 hours cycle. Subsequently we propose a model with a network of three phase oscillators for each one of the twenty four hours cycle. Each of the three phase oscillators is chosen to maintain a phase difference of 120 degrees between each other. All the oscillators are connected to an internal clock that is designed to maintain a phase activity close to a master clock derived using KaiC proteins. In Cyanobacteria it is believed that the KaiC proteins provide the internal rhythm. The model parameters, viz. connection strengths between the master clock and peripheral oscillators and the parameters computing the linear combinations of the oscillator phase variables, are optimized to provide a close match to the observed gene expressions even when the frequency of the internal clock and the natural frequencies of the oscillators vary within a certain range. As a final step, the oscillator network model has been used to isolate genes, and hence the associated subprocesses, whose expression cycles are robust with respect to variations in the oscillator frequencies.

Animals routinely rely on their eyes to localize fixed and moving targets. Such a localization process might include prediction of future target location, recalling a sequence of previously visited places or, for the motor control circuit, actuating a successful movement. Typically, target localization is carried out by fusing images from two eyes, in the case of binocular vision, wherein the challenge is to have the images calibrated before fusion. In the field of machine vision, a typical problem of interest is to localize the position and orientation of a network of mobile cameras (sensor network) that are distributed in space and are simultaneously tracking a target. Inspired by the animal visual circuit, we study the problem of binocular image fusion for the purpose of localizing an unknown target in space. Guided by the dynamics of “eye rotation,” we introduce control strategies that could be used to build machines with multiple sensors. In particular, we address the problem of how a group of visual sensors can be optimally controlled in a formation. We also address how images from multiple sensors are encoded using a set of basis functions, choosing a “larger than minimum” number of basis functions so that the resulting code that represents the image is sparse. We address the problem of how a sparsely encoded visual data stream is internally represented by a pattern of neural activity. In addition to the control mechanism, the synaptic interaction between cells is also subjected to “adaptation” that enables the activity waves to respond with greater sensitivity to visual input. We study how the rat hippocampal place cells are used to form a cognitive map of the environment so that the animal's location can be determined from its place cell activity. Finally, we study the problem of “decoding” location of moving targets from the neural activity wave in the cortex.