AIM: To investigate the effectiveness of head compensatory postures to ensure safe oropharyngeal transit. METHODS: A total of 321 dysphagia patients were enrolled and assessed with videofluoromanometry (VFM). The dysp...AIM: To investigate the effectiveness of head compensatory postures to ensure safe oropharyngeal transit. METHODS: A total of 321 dysphagia patients were enrolled and assessed with videofluoromanometry (VFM). The dysphagia patients were classified as follows: safe transit; penetration without aspiration; aspiration before, during or after swallowing; multiple aspirations and no transit. The patients with aspiration or no transit were tested with VFM to determine whether compensatory postures could correct their swallowing disorder. RESULTS: VFM revealed penetration without aspiration in 71 patients (22.1%); aspiration before swallowing in 17 patients (5.3%); aspiration during swallowing in 32 patients (10%); aspiration after swallowing in 21 patients (6.5%); multiple aspirations in six patients (1.9%); no transit in five patients (1.6%); and safe transit in 169 patients (52.6%). Compensatory postures guaranteed a safe transit in 66/75 (88%) patients with aspiration or no transit. A chin-down posture achieved a safe swallow in 42/75 (56%) patients, a head-turned posture in 19/75 (25.3%) and a hyperextended head posture in 5/75 (6.7%). The compensatory postures were not effective in 9/75 (12%) cases. CONCLUSION: VFM allows the speech-language therapist to choose the most effective compensatory posture without a trial-and-error process and check the effectiveness of the posture.展开更多
We present a novel methodology and strategy to predict pressures and flow rates in the global cardiovascular network in different postures varying from supine to upright. A closed-loop, multiscale mathematical model o...We present a novel methodology and strategy to predict pressures and flow rates in the global cardiovascular network in different postures varying from supine to upright. A closed-loop, multiscale mathematical model of the entire cardiovascular system (CVS) is developed through an integration of one-dimensional (1D) modeling of the large systemic arteries and veins, and zero-dimensional (0D) lumped-parameter modeling of the heart, the cardiac-pulmonary circulation, the cardiac and venous valves, as well as the microcirculation. A versatile junction model is proposed and incorporated into the 1D model to cope with splitting and/or merging flows across a multibranched junction, which is validated to be capable of estimating both subcritical and supercritical flows while ensuring the mass conservation and total pressure continuity. To model gravitational effects on global hemodynamics during postural change, a robust venous valve model is further established for the 1D venous flows and distributed throughout the entire venous network with consideration of its anatomically realistic numbers and locations. The present integrated model is proven to enable reasonable prediction of pressure and flow rate waveforms associated with cardiopulmonary circulation, systemic circulation in arteries and veins, as well as microcirculation within normal physiological ranges, particularly in mean venous pressures, which well match the in vivo measurements. Applications of the cardiovascular model at different postures demonstrate that gravity exerts remarkable influence on arterial and venous pressures, venous returns and cardiac outputs whereas venous pressures below the heart level show a specific correlation between central venous and hydrostatic pressures in right atrium and veins.展开更多
At a different angle, this study analyzed the contour chart of blood flow pressure, extreme pressure and its position to quantify DBFP in thirteen different postures with gravity considered or not (G ≠ 0 or G = 0). T...At a different angle, this study analyzed the contour chart of blood flow pressure, extreme pressure and its position to quantify DBFP in thirteen different postures with gravity considered or not (G ≠ 0 or G = 0). The aim was to determine the suitable body positions, in which the postural model of a single vessel could be simplified to two-dimensional (2D) symmetrical one while only considering such factors as posture and gravity. Computational fluid dynamic simulations were performed. Numerical results demonstrated that the DBFP showed 2D axisymmetry at ±90° and three-dimensional (3D) asymmetry at any other posture with G ≠ 0, and 2D axisymmetrical one at any posture with G = 0. Therefore, modeling a vessel as a 2D model is feasible in space and at ±90° posture on earth. In addition, the maximum pressure occurred between the inlet and the middle of the vessel, and its position variation mainly happened in the range of 0° - 15°. For a single vessel, this study provides the first theoretical evidence for cardiovascular modeling in microgravity and may help guide the researchers in designing defense devices for astronauts or patients clinically.展开更多
A proper landing posture is significant to the reduction of both the im-pact force acting on the human body and the injury at landing.In this paper theimpact force acting on human feet is studied.The subjects were 3 m...A proper landing posture is significant to the reduction of both the im-pact force acting on the human body and the injury at landing.In this paper theimpact force acting on human feet is studied.The subjects were 3 maleparachuters.The experiments were performed by means of high-speed photography and amotor analyzer.The experimental results are as follows:(1)When the subjectjumped from two platforms 1.0m and 1.5m in height,a vertical impact force onthe feet in half-squat posture was larger than in side spin posture.(2)When thesubject jumped from the platform 1.0m high,the feet gained a horizontal impactforce in the half-squat posture,larger than in the side spin posture.When thesubject jumped from the platform 1.5m high,the horizontal impact force pro-duced by both of the above-mentioned postures were just the same,which needsfurther research.(3)In terms of reducing the impact force acting on the feet,theside spin posture is better than the half-squat posture.展开更多
Neck injury is a severe problem in traffic accidents.While most studies are focused on the neck injury in rear and front impacts,few are conducted in side impact.This study focuses on the difference of neck injury und...Neck injury is a severe problem in traffic accidents.While most studies are focused on the neck injury in rear and front impacts,few are conducted in side impact.This study focuses on the difference of neck injury under different postures and the difference of 7 cervical vertebras under the same posture using the method of prescribed structure motion（PSM）.The analytical results show that the maximum changes of mean force and mean moment of 7 cervical vertebras under 8 different postures are 20% and 47% respectively.The variation of each cervical vertebra is different under different neck postures.Up cervical vertebras （C1-C4） and low cervical vertebras （C5-C7） suffer different forces and moments under the same neck posture.Generally speaking,No.6 （neck right leaning 40°） is the posture with lowest neck injury risk.展开更多
Clothing plays a crucial role in determining human thermal comfort.However,most existing models of clothing thermal resistance primarily focus on predicting the overall and local thermal resistance in a standing postu...Clothing plays a crucial role in determining human thermal comfort.However,most existing models of clothing thermal resistance primarily focus on predicting the overall and local thermal resistance in a standing posture.A few models can predict the overall thermal resistance in a sitting posture,but they do not account for local thermal resistance.Therefore,in this study,a mathematical model to predict the overall and local thermal resistance of clothing under various body postures is presented.The geometric models of the human body and clothing were constructed using three-dimensional virtual simulation technology to determine the geometric parameters.The predicted overall and local thermal resistances of 19 ensembles in different postures were compared with experimental data from previous studies.The ensembles validated in this study included single-layer and double-layer clothing for daily wear.The results demonstrated that the predicted values agreed well with the experimental data.The errors of predicted overall and most local thermal resistance were within 15%and 20%,respectively.Furthermore,the model exhibited higher accuracy and resolution compared to previous models.Finally,the effect of posture on clothing thermal resistance was analyzed,providing reliable guidance for the design of clothing thermal performance.This study has significant implications for clothing design and thermal comfort prediction,contributing to improving human thermal comfort.展开更多
Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders(WMSDs),with symptoms often appearing very early in one’s career[1].WMSDs are conditions affecting the muscles,bones,and nervo...Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders(WMSDs),with symptoms often appearing very early in one’s career[1].WMSDs are conditions affecting the muscles,bones,and nervous system due to occupational factors.In 2002,the International Labor Organization included musculoskeletal diseases in the International List of Occupational Diseases.China’s recently updated Classification and Catalog of Occupational Diseases has introduced two new categories of occupational illnesses,including occupational musculoskeletal disorders.WMSDs significantly impact the health and work of dentists,reducing their quality of life and causing economic losses.These disorders are multifactorial in nature,influenced by personal,psychosocial,biomechanical,and environmental factors.Dentists frequently maintain static or awkward postures during procedures,which leads to musculoskeletal strain and discomfort;additionally,long working hours contribute to psychological stress,further increasing the risk of WMSDs[2].展开更多
Although significant advances in the design of soft robotic hands have been made to mimic the structure of the human hands,there are great challenges to control them for coordinated and human-like postures.Based on th...Although significant advances in the design of soft robotic hands have been made to mimic the structure of the human hands,there are great challenges to control them for coordinated and human-like postures.Based on the principle of postural synergies in the human hand,we present a synergistic approach for coordinated control of a soft robotic hand to replicate the human-like grasp postures.To this end,we firstly develop a kinematic model to describe the control variables and the various postures of the soft robotic hand.Based on the postural synergies,we use the developed model and Principal Component Analysis(PCA)method to describe the various postures of the soft robotic hand in a low-dimensional space formed by the synergies of actuator motions.Therefore,the coordinates of these synergies can be used as low-dimensional control inputs for the soft robotic hand with a higher-dimensional postural space.Finally,we establish an experimental platform on a customized soft robotic hand with6 pneumatical actuators to verify the effectiveness of the development.Experimental results demonstrate that with only a 2-dimensional control input,the soft robotic hand can reliably replicate 30 grasp postures in the Feix taxonomy of the human hand.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessmen...This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessment Form has been assessed as severe articulation disorder.The patient has significantly improved his speech function and quality of life after systematic head control training,respiratory function training,articulation motor training,and articulation training.In the course of treatment,emphasis was placed on head postural control training and respiratory function training,and emphasis was placed on the strength and coordination training of articulatory organs,and the results were remarkable.After the patient was discharged from the hospital,the follow-up of basic daily life communication was not limited.展开更多
To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily e...To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily emotion recognition.First,ResNet50,DNN and spatial ransformer models are used to capture facial texture vectors,bodily skeleton vectors and wholebody geometric vectors,and an intraframe correlation attention guidance(S-CAG)mechanism,which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector,is designed to exploit the spatial potential emotional correlation between face and posture.Second,an interframe significant segment enhancement(T-SSE)structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony.Finally,an adaptive weight assignment(M-AWA)strategy is constructed to realise facial-bodily fusion.The experimental results on the BabyRobot Emotion Dataset(BRED)and Context-Aware Emotion Recognition(CAER)dataset indicate that the proposed network reaches accuracies of 81.61%and 89.39%,which are 9.61%and 9.46%higher than those of the baseline network,respectively.Compared with the state-of-the-art methods,the proposed method achieves 7.73%and 20.57%higher accuracy than single-modal methods based on facial expression or bodily posture,respectively,and 2.16%higher accuracy than the dual-modal methods based on facial-bodily fusion.Therefore,the proposed method,which adaptively fuses the complementary information of face and posture,improves the quality of emotion recognition in real-world scenarios.展开更多
Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assess-ment(REBA)pose estimation by leveraging progress in three-dimensional human modeling.This survey provides an extensive o...Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assess-ment(REBA)pose estimation by leveraging progress in three-dimensional human modeling.This survey provides an extensive overview of recent advancements,particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks.By reviewing literature from 2016 to 2024,this study offers a current and comprehensive analysis of techniques,existing challenges,and emerging trends in three-dimensional human pose estimation.In contrast to traditional reviews organized by learning paradigms,this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder(MSD)assessments,focusing on essential advancements,comparative analyses,and ergonomic implications.We extend existing image-based clas-sification schemes by examining state-of-the-art two-dimensional models that enhance monocular three-dimensional prediction accuracy and analyze skeleton representations by evaluating joint connectivity and spatial configuration,offering insights into how structural variability influences model robustness.A core contribution of this work is the identification of a critical research gap:the limited exploration of estimating REBA scores directly from single RGB images using monocular three-dimensional pose estimation.Most existing studies depend on depth sensors or sequential inputs,limiting applicability in real-time and resource-constrained environments.Our review emphasizes this gap and proposes future research directions to develop accurate,lightweight,and generalizable models suitable for practical deployment.This survey is a valuable resource for researchers and practitioners in computer vision,ergonomics,and related disciplines,offering a structured understanding of current methodologies and guidance for future innovation in three-dimensional human pose estimation for REBA-based ergonomic risk assessment.展开更多
This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophagea...This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophageal reflux disease(GERD)monitoring.Unlike conventional approaches limited to four basic postures,CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions,providing enhanced resolution for personalized health assessment.The architecture introduces a unique integration of three complementary components:1D Convolutional Neural Networks(1D-CNN)for efficient local spatial feature extraction,Gated Recurrent Units(GRU)to capture short-termtemporal dependencieswith reduced computational complexity,and Bidirectional Long Short-Term Memory(Bi-LSTM)networks for modeling long-term temporal context in both forward and backward directions.This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data,surpassing the performance of simpler or previously published hybrid models.Experiments were conducted on a benchmark dataset consisting of 18 volunteers(age range:19–24 years,mean 20.56±1.1 years;height 164.78±8.18 cm;weight 55.39±8.30 kg;BMI 20.24±2.04),monitored via a single abdominal accelerometer.A subjectindependent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability.The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%,both reported with standard deviations over multiple runs,outperforming several baseline and state-of-the-art methods.By releasing the dataset publicly and detailing themodel design,this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.展开更多
The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the probl...The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.展开更多
Objective:To observe the therapeutic efficacy of sinew-bone balancing manipulation plus exercise therapy in treating postures of primary school students with upper crossed syndrome(UCS).Methods:Sixty pupils with UCS w...Objective:To observe the therapeutic efficacy of sinew-bone balancing manipulation plus exercise therapy in treating postures of primary school students with upper crossed syndrome(UCS).Methods:Sixty pupils with UCS were divided into an exercise group and a combination group using the random number table method,with 30 cases in each group.The combination group received treatments of sinew-bone balancing manipulation plus exercise therapy,while the exercise group received exercise therapy alone.The two groups received interventions once every other day,for a total of 1 month.The sagittal static posture assessment total score,forward head angle(FHA)and forward shoulder angle(FSA)were compared before and after treatment;the sagittal static assessment total score,FHA and FSA were compared between the exercise group and the combination group.Results:Before treatment,there were no significant differences comparing the sagittal static posture assessment total score,FHA and FSA between the two groups(all P>0.05);after treatment,the sagittal static posture assessment total score,FHA and FSA decreased in the two groups,with intra-group statistical significance(all P<0.01),and were lower in the combination group than in the exercise group,with inter-group statistical significance(all P<0.01).Conclusion:Sinew-bone balancing manipulation plus exercise therapy can notably improve the FHA and FSA and reduce the sagittal static posture total score in pupils with UCS,so as to correct the bad postures and adjust UCS physique.It can produce more significant efficacy compared with exercise therapy alone.展开更多
On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dyna...On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer.Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.展开更多
With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors we...With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.展开更多
This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognit...This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.展开更多
Sensory conflict impacts postural control,yet its effect on cortico-muscular interaction remains underexplored.We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stab...Sensory conflict impacts postural control,yet its effect on cortico-muscular interaction remains underexplored.We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability.We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input,recorded EEG(electroencephalogram)and EMG(electromyogram)data,and constructed a directed connectivity network.The results suggest that,compared to sensory congruence,during sensory conflict:(1)connectivity among the sensorimotor,visual,and posterior parietal cortex generally decreases,(2)cortical control over the muscles is weakened,(3)feedback from muscles to the cortex is strengthened,and(4)the range of body sway increases and its complexity decreases.These results underline the intricate effects of sensory conflict on cortico-muscular networks.During the sensory conflict,the brain adaptively decreases the integration of conflicting information.Without this integrated information,cortical control over muscles may be lessened,whereas the muscle feedback may be enhanced in compensation.展开更多
In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking....In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.展开更多
文摘AIM: To investigate the effectiveness of head compensatory postures to ensure safe oropharyngeal transit. METHODS: A total of 321 dysphagia patients were enrolled and assessed with videofluoromanometry (VFM). The dysphagia patients were classified as follows: safe transit; penetration without aspiration; aspiration before, during or after swallowing; multiple aspirations and no transit. The patients with aspiration or no transit were tested with VFM to determine whether compensatory postures could correct their swallowing disorder. RESULTS: VFM revealed penetration without aspiration in 71 patients (22.1%); aspiration before swallowing in 17 patients (5.3%); aspiration during swallowing in 32 patients (10%); aspiration after swallowing in 21 patients (6.5%); multiple aspirations in six patients (1.9%); no transit in five patients (1.6%); and safe transit in 169 patients (52.6%). Compensatory postures guaranteed a safe transit in 66/75 (88%) patients with aspiration or no transit. A chin-down posture achieved a safe swallow in 42/75 (56%) patients, a head-turned posture in 19/75 (25.3%) and a hyperextended head posture in 5/75 (6.7%). The compensatory postures were not effective in 9/75 (12%) cases. CONCLUSION: VFM allows the speech-language therapist to choose the most effective compensatory posture without a trial-and-error process and check the effectiveness of the posture.
基金supported by a Grant-in-Aid for Scientific Research (Grant 17300141)Japan Society for the Promotion of Science and Research and Development of the Next Generation Integrated Simulation of Living Matter, JST,a part of the Development and Use of the Next Generation Supercomputer Project of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japanthe RIKEN Junior Research Associate Program
文摘We present a novel methodology and strategy to predict pressures and flow rates in the global cardiovascular network in different postures varying from supine to upright. A closed-loop, multiscale mathematical model of the entire cardiovascular system (CVS) is developed through an integration of one-dimensional (1D) modeling of the large systemic arteries and veins, and zero-dimensional (0D) lumped-parameter modeling of the heart, the cardiac-pulmonary circulation, the cardiac and venous valves, as well as the microcirculation. A versatile junction model is proposed and incorporated into the 1D model to cope with splitting and/or merging flows across a multibranched junction, which is validated to be capable of estimating both subcritical and supercritical flows while ensuring the mass conservation and total pressure continuity. To model gravitational effects on global hemodynamics during postural change, a robust venous valve model is further established for the 1D venous flows and distributed throughout the entire venous network with consideration of its anatomically realistic numbers and locations. The present integrated model is proven to enable reasonable prediction of pressure and flow rate waveforms associated with cardiopulmonary circulation, systemic circulation in arteries and veins, as well as microcirculation within normal physiological ranges, particularly in mean venous pressures, which well match the in vivo measurements. Applications of the cardiovascular model at different postures demonstrate that gravity exerts remarkable influence on arterial and venous pressures, venous returns and cardiac outputs whereas venous pressures below the heart level show a specific correlation between central venous and hydrostatic pressures in right atrium and veins.
文摘At a different angle, this study analyzed the contour chart of blood flow pressure, extreme pressure and its position to quantify DBFP in thirteen different postures with gravity considered or not (G ≠ 0 or G = 0). The aim was to determine the suitable body positions, in which the postural model of a single vessel could be simplified to two-dimensional (2D) symmetrical one while only considering such factors as posture and gravity. Computational fluid dynamic simulations were performed. Numerical results demonstrated that the DBFP showed 2D axisymmetry at ±90° and three-dimensional (3D) asymmetry at any other posture with G ≠ 0, and 2D axisymmetrical one at any posture with G = 0. Therefore, modeling a vessel as a 2D model is feasible in space and at ±90° posture on earth. In addition, the maximum pressure occurred between the inlet and the middle of the vessel, and its position variation mainly happened in the range of 0° - 15°. For a single vessel, this study provides the first theoretical evidence for cardiovascular modeling in microgravity and may help guide the researchers in designing defense devices for astronauts or patients clinically.
文摘A proper landing posture is significant to the reduction of both the im-pact force acting on the human body and the injury at landing.In this paper theimpact force acting on human feet is studied.The subjects were 3 maleparachuters.The experiments were performed by means of high-speed photography and amotor analyzer.The experimental results are as follows:(1)When the subjectjumped from two platforms 1.0m and 1.5m in height,a vertical impact force onthe feet in half-squat posture was larger than in side spin posture.(2)When thesubject jumped from the platform 1.0m high,the feet gained a horizontal impactforce in the half-squat posture,larger than in the side spin posture.When thesubject jumped from the platform 1.5m high,the horizontal impact force pro-duced by both of the above-mentioned postures were just the same,which needsfurther research.(3)In terms of reducing the impact force acting on the feet,theside spin posture is better than the half-squat posture.
基金Sponsored by the National High Technology Research and Development Program of China("863"Program) (2006AA110102)
文摘Neck injury is a severe problem in traffic accidents.While most studies are focused on the neck injury in rear and front impacts,few are conducted in side impact.This study focuses on the difference of neck injury under different postures and the difference of 7 cervical vertebras under the same posture using the method of prescribed structure motion（PSM）.The analytical results show that the maximum changes of mean force and mean moment of 7 cervical vertebras under 8 different postures are 20% and 47% respectively.The variation of each cervical vertebra is different under different neck postures.Up cervical vertebras （C1-C4） and low cervical vertebras （C5-C7） suffer different forces and moments under the same neck posture.Generally speaking,No.6 （neck right leaning 40°） is the posture with lowest neck injury risk.
文摘Clothing plays a crucial role in determining human thermal comfort.However,most existing models of clothing thermal resistance primarily focus on predicting the overall and local thermal resistance in a standing posture.A few models can predict the overall thermal resistance in a sitting posture,but they do not account for local thermal resistance.Therefore,in this study,a mathematical model to predict the overall and local thermal resistance of clothing under various body postures is presented.The geometric models of the human body and clothing were constructed using three-dimensional virtual simulation technology to determine the geometric parameters.The predicted overall and local thermal resistances of 19 ensembles in different postures were compared with experimental data from previous studies.The ensembles validated in this study included single-layer and double-layer clothing for daily wear.The results demonstrated that the predicted values agreed well with the experimental data.The errors of predicted overall and most local thermal resistance were within 15%and 20%,respectively.Furthermore,the model exhibited higher accuracy and resolution compared to previous models.Finally,the effect of posture on clothing thermal resistance was analyzed,providing reliable guidance for the design of clothing thermal performance.This study has significant implications for clothing design and thermal comfort prediction,contributing to improving human thermal comfort.
基金supported by the 2021 Shandong Province Higher Education Institutions“Youth Innovation Talent Introduction and Cultivation Plan”(Public Health Safety Risk Assessment and Response Innovation Team)National Traditional Chinese Medicine Comprehensive Reform Demonstration Zone Science and Technology Co construction Project(No.GZYKJSSD-2024-106)Research Project of Shandong Educational Supervision Society(No.SDJYDDXH2023-2159).
文摘Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders(WMSDs),with symptoms often appearing very early in one’s career[1].WMSDs are conditions affecting the muscles,bones,and nervous system due to occupational factors.In 2002,the International Labor Organization included musculoskeletal diseases in the International List of Occupational Diseases.China’s recently updated Classification and Catalog of Occupational Diseases has introduced two new categories of occupational illnesses,including occupational musculoskeletal disorders.WMSDs significantly impact the health and work of dentists,reducing their quality of life and causing economic losses.These disorders are multifactorial in nature,influenced by personal,psychosocial,biomechanical,and environmental factors.Dentists frequently maintain static or awkward postures during procedures,which leads to musculoskeletal strain and discomfort;additionally,long working hours contribute to psychological stress,further increasing the risk of WMSDs[2].
基金supported by the National Natural Science Foundation of China(Grant Nos.52025057,91948302)the Science and Technology Commission of Shanghai Municipality(Grant No.20550712100)。
文摘Although significant advances in the design of soft robotic hands have been made to mimic the structure of the human hands,there are great challenges to control them for coordinated and human-like postures.Based on the principle of postural synergies in the human hand,we present a synergistic approach for coordinated control of a soft robotic hand to replicate the human-like grasp postures.To this end,we firstly develop a kinematic model to describe the control variables and the various postures of the soft robotic hand.Based on the postural synergies,we use the developed model and Principal Component Analysis(PCA)method to describe the various postures of the soft robotic hand in a low-dimensional space formed by the synergies of actuator motions.Therefore,the coordinates of these synergies can be used as low-dimensional control inputs for the soft robotic hand with a higher-dimensional postural space.Finally,we establish an experimental platform on a customized soft robotic hand with6 pneumatical actuators to verify the effectiveness of the development.Experimental results demonstrate that with only a 2-dimensional control input,the soft robotic hand can reliably replicate 30 grasp postures in the Feix taxonomy of the human hand.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
基金Teaching and Research Project of Anhui Urban Management Vocational College(Project No.:2024kfkc001)。
文摘This paper reports a case of cerebral stem infarction with quadriplegia and complete dependence on daily life.The course of the disease lasted more than 7 months.Frenchay's improved articulation Disorder Assessment Form has been assessed as severe articulation disorder.The patient has significantly improved his speech function and quality of life after systematic head control training,respiratory function training,articulation motor training,and articulation training.In the course of treatment,emphasis was placed on head postural control training and respiratory function training,and emphasis was placed on the strength and coordination training of articulatory organs,and the results were remarkable.After the patient was discharged from the hospital,the follow-up of basic daily life communication was not limited.
基金National Natural Science Foundation of China,Grant/Award Number:62176084,Natural Science Foundation of Anhui Province of China,Grant/Award Number:1908085MF195,Natural Science Research Project of the Education Department of Anhui Province of China Grant/Award Numbers:2022AH051038,2023AH050474 and 2023AH050490.
文摘To overcome the deficiencies of single-modal emotion recognition based on facial expression or bodily posture in natural scenes,a spatial guidance and temporal enhancement(SG-TE)network is proposed for facial-bodily emotion recognition.First,ResNet50,DNN and spatial ransformer models are used to capture facial texture vectors,bodily skeleton vectors and wholebody geometric vectors,and an intraframe correlation attention guidance(S-CAG)mechanism,which guides the facial texture vector and the bodily skeleton vector by the whole-body geometric vector,is designed to exploit the spatial potential emotional correlation between face and posture.Second,an interframe significant segment enhancement(T-SSE)structure is embedded into a temporal transformer to enhance high emotional intensity frame information and avoid emotional asynchrony.Finally,an adaptive weight assignment(M-AWA)strategy is constructed to realise facial-bodily fusion.The experimental results on the BabyRobot Emotion Dataset(BRED)and Context-Aware Emotion Recognition(CAER)dataset indicate that the proposed network reaches accuracies of 81.61%and 89.39%,which are 9.61%and 9.46%higher than those of the baseline network,respectively.Compared with the state-of-the-art methods,the proposed method achieves 7.73%and 20.57%higher accuracy than single-modal methods based on facial expression or bodily posture,respectively,and 2.16%higher accuracy than the dual-modal methods based on facial-bodily fusion.Therefore,the proposed method,which adaptively fuses the complementary information of face and posture,improves the quality of emotion recognition in real-world scenarios.
文摘Advancements in deep learning have considerably enhanced techniques for Rapid Entire Body Assess-ment(REBA)pose estimation by leveraging progress in three-dimensional human modeling.This survey provides an extensive overview of recent advancements,particularly emphasizing monocular image-based methodologies and their incorporation into ergonomic risk assessment frameworks.By reviewing literature from 2016 to 2024,this study offers a current and comprehensive analysis of techniques,existing challenges,and emerging trends in three-dimensional human pose estimation.In contrast to traditional reviews organized by learning paradigms,this survey examines how three-dimensional pose estimation is effectively utilized within musculoskeletal disorder(MSD)assessments,focusing on essential advancements,comparative analyses,and ergonomic implications.We extend existing image-based clas-sification schemes by examining state-of-the-art two-dimensional models that enhance monocular three-dimensional prediction accuracy and analyze skeleton representations by evaluating joint connectivity and spatial configuration,offering insights into how structural variability influences model robustness.A core contribution of this work is the identification of a critical research gap:the limited exploration of estimating REBA scores directly from single RGB images using monocular three-dimensional pose estimation.Most existing studies depend on depth sensors or sequential inputs,limiting applicability in real-time and resource-constrained environments.Our review emphasizes this gap and proposes future research directions to develop accurate,lightweight,and generalizable models suitable for practical deployment.This survey is a valuable resource for researchers and practitioners in computer vision,ergonomics,and related disciplines,offering a structured understanding of current methodologies and guidance for future innovation in three-dimensional human pose estimation for REBA-based ergonomic risk assessment.
基金funded by Vietnam National Foundation for Science and Technology Development(NAFOSTED)under grant number:NCUD.02-2024.11.
文摘This study presents CGB-Net,a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer,with direct applicability to gastroesophageal reflux disease(GERD)monitoring.Unlike conventional approaches limited to four basic postures,CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions,providing enhanced resolution for personalized health assessment.The architecture introduces a unique integration of three complementary components:1D Convolutional Neural Networks(1D-CNN)for efficient local spatial feature extraction,Gated Recurrent Units(GRU)to capture short-termtemporal dependencieswith reduced computational complexity,and Bidirectional Long Short-Term Memory(Bi-LSTM)networks for modeling long-term temporal context in both forward and backward directions.This complementary integration allows the model to better represent dynamic and contextual information inherent in the sensor data,surpassing the performance of simpler or previously published hybrid models.Experiments were conducted on a benchmark dataset consisting of 18 volunteers(age range:19–24 years,mean 20.56±1.1 years;height 164.78±8.18 cm;weight 55.39±8.30 kg;BMI 20.24±2.04),monitored via a single abdominal accelerometer.A subjectindependent evaluation protocol with multiple random splits was employed to ensure robustness and generalizability.The proposed model achieves an average Accuracy of 87.60% and F1-score of 83.38%,both reported with standard deviations over multiple runs,outperforming several baseline and state-of-the-art methods.By releasing the dataset publicly and detailing themodel design,this work aims to facilitate reproducibility and advance research in sleep posture classification for clinical applications.
基金Supported by the Special Fund for Basic Scientific Research of Central-Level Public Welfare Scientific Research Institutes(2024-9007)。
文摘The accuracy of center height detection for corrugated beam guardrails is significantly affected by robot posture in the mobile highway guardrail detection systems based on structured light vision.To address the problem,this paper proposes an integrated calibration method for structured light vision sensors.In the proposed system,the sensor is mounted on a crawler-type mobile robot,which scans and measures the center height of guardrails while in motion.However,due to external disturbances such as uneven road surfaces and vehicle vibrations,the posture of the robot may deviate,causing displacement of the sensor platform and resulting in spatial 3D measurement errors.To overcome this issue,the system integrates inertial measurement unit(IMU)data into the sensor calibration process,enabling realtime correction of posture deviations through sensor fusion.This approach achieves a unified calibration of the structured light vision system,effectively compensates for posture-induced errors,and enhances detection accuracy.A prototype was developed and tested in both laboratory and real highway environments.Experimental results demonstrate that the proposed method enables accurate center height detection of guardrails under complex road conditions,significantly reduces posture-related measurement errors,and greatly improves the efficiency and reliability of traditional detection methods.
文摘Objective:To observe the therapeutic efficacy of sinew-bone balancing manipulation plus exercise therapy in treating postures of primary school students with upper crossed syndrome(UCS).Methods:Sixty pupils with UCS were divided into an exercise group and a combination group using the random number table method,with 30 cases in each group.The combination group received treatments of sinew-bone balancing manipulation plus exercise therapy,while the exercise group received exercise therapy alone.The two groups received interventions once every other day,for a total of 1 month.The sagittal static posture assessment total score,forward head angle(FHA)and forward shoulder angle(FSA)were compared before and after treatment;the sagittal static assessment total score,FHA and FSA were compared between the exercise group and the combination group.Results:Before treatment,there were no significant differences comparing the sagittal static posture assessment total score,FHA and FSA between the two groups(all P>0.05);after treatment,the sagittal static posture assessment total score,FHA and FSA decreased in the two groups,with intra-group statistical significance(all P<0.01),and were lower in the combination group than in the exercise group,with inter-group statistical significance(all P<0.01).Conclusion:Sinew-bone balancing manipulation plus exercise therapy can notably improve the FHA and FSA and reduce the sagittal static posture total score in pupils with UCS,so as to correct the bad postures and adjust UCS physique.It can produce more significant efficacy compared with exercise therapy alone.
基金National Natural Science Foundation of China (52375412)Fundamental Research Funds for Central Universities (N2203011)China Scholarship Council Program (202306080057)。
文摘On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer.Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.
文摘With the advancement of technology and the increase in user demands, gesture recognition played a pivotal role in the field of human-computer interaction. Among various sensing devices, Time-of-Flight (ToF) sensors were widely applied due to their low cost. This paper explored the implementation of a human hand posture recognition system using ToF sensors and residual neural networks. Firstly, this paper reviewed the typical applications of human hand recognition. Secondly, this paper designed a hand gesture recognition system using a ToF sensor VL53L5. Subsequently, data preprocessing was conducted, followed by training the constructed residual neural network. Then, the recognition results were analyzed, indicating that gesture recognition based on the residual neural network achieved an accuracy of 98.5% in a 5-class classification scenario. Finally, the paper discussed existing issues and future research directions.
基金funded by the Henan Provincial Science and Technology Research Project(222102210086)the Starry Sky Creative Space Innovation Space Innovation Incubation Project of Zhengzhou University of Light Industry(2023ZCKJ211).
文摘This study proposes a pose estimation-convolutional neural network-bidirectional gated recurrent unit(PSECNN-BiGRU)fusion model for human posture recognition to address low accuracy issues in abnormal posture recognition due to the loss of some feature information and the deterioration of comprehensive performance in model detection in complex home environments.Firstly,the deep convolutional network is integrated with the Mediapipe framework to extract high-precision,multi-dimensional information from the key points of the human skeleton,thereby obtaining a human posture feature set.Thereafter,a double-layer BiGRU algorithm is utilized to extract multi-layer,bidirectional temporal features from the human posture feature set,and a CNN network with an exponential linear unit(ELU)activation function is adopted to perform deep convolution of the feature map to extract the spatial feature of the human posture.Furthermore,a squeeze and excitation networks(SENet)module is introduced to adaptively learn the importance weights of each channel,enhancing the network’s focus on important features.Finally,comparative experiments are performed on available datasets,including the public human activity recognition using smartphone dataset(UCIHAR),the public human activity recognition 70 plus dataset(HAR70PLUS),and the independently developed home abnormal behavior recognition dataset(HABRD)created by the authors’team.The results show that the average accuracy of the proposed PSE-CNN-BiGRU fusion model for human posture recognition is 99.56%,89.42%,and 98.90%,respectively,which are 5.24%,5.83%,and 3.19%higher than the average accuracy of the five models proposed in the comparative literature,including CNN,GRU,and others.The F1-score for abnormal posture recognition reaches 98.84%(heartache),97.18%(fall),99.6%(bellyache),and 98.27%(climbing)on the self-builtHABRDdataset,thus verifying the effectiveness,generalization,and robustness of the proposed model in enhancing human posture recognition.
基金supported by the National Defense Foundation Strengthening Program Technology Field Fund Project of China(2021-JCJQ-JJ-1029)the Science Technology Plan Project of Zhejiang Province(2023C03159)+1 种基金the Science Foundation of National Health and Family Planning Commission-Medical Health Science and Technology Project of Zhejiang Provincial Health(WKJ-ZJ-2334)the key projects of major health science and technology plan of Zhejiang Province(WKJ-ZJ-2129).
文摘Sensory conflict impacts postural control,yet its effect on cortico-muscular interaction remains underexplored.We aimed to investigate sensory conflict's influence on the cortico-muscular network and postural stability.We used a rotating platform and virtual reality to present subjects with congruent and incongruent sensory input,recorded EEG(electroencephalogram)and EMG(electromyogram)data,and constructed a directed connectivity network.The results suggest that,compared to sensory congruence,during sensory conflict:(1)connectivity among the sensorimotor,visual,and posterior parietal cortex generally decreases,(2)cortical control over the muscles is weakened,(3)feedback from muscles to the cortex is strengthened,and(4)the range of body sway increases and its complexity decreases.These results underline the intricate effects of sensory conflict on cortico-muscular networks.During the sensory conflict,the brain adaptively decreases the integration of conflicting information.Without this integrated information,cortical control over muscles may be lessened,whereas the muscle feedback may be enhanced in compensation.
基金supported by the National Natural Science Foundation of China under Grant No.62001199Fujian Province Nature Science Foundation under Grant No.2023J01925.
文摘In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.
