Movement Information Research Articles

Integrating subject-specific workspace constraint and performance-based control strategy in robot-assisted rehabilitation

IntroductionThe robot-assistive technique has been widely developed in the field of neurorehabilitation for enhancement of neuroplasticity, muscle activity, and training positivity. To improve the reliability and feasibility in this patient–robot interactive context, motion constraint methods and adaptive assistance strategies have been developed to guarantee the movement safety and promote the training effectiveness based on the user’s movement information. Unfortunately, few works focus on customizing quantitative and appropriate workspace for each subject in passive/active training mode, and how to provide the precise assistance by considering movement constraints to improve human active participation should be further delved as well.MethodsThis study proposes an integrated framework for robot-assisted upper-limb training. A human kinematic upper-limb model is built to achieve a quantitative human–robot interactive workspace, and an iterative learning-based repulsive force field is developed to balance the compliant degrees of movement freedom and constraint. On this basis, a radial basis function neural network (RBFNN)-based control structure is further explored to obtain appropriate robotic assistance. The proposed strategy was preliminarily validated for bilateral upper-limb training with an end-effector-based robotic system.ResultsExperiments on healthy subjects are enrolled to validate the safety and feasibility of the proposed framework. The results show that the framework is capable of providing personalized movement workspace to guarantee safe and natural motion, and the RBFNN-based control structure can rapidly converge to the appropriate robotic assistance for individuals to efficiently complete various training tasks.DiscussionThe integrated framework has the potential to improve outcomes in personalized movement constraint and optimized robotic assistance. Future studies are necessary to involve clinical application with a larger sample size of patients.

Freezing of movements and its correspondence with MLG1 neurons response to looming stimuli in the crab Neohelice.

Upon visually detecting a moving predator animals often freeze, i.e. stop moving, to minimize being uncovered and to gather detailed information of the object's movements and properties. In certain conditions the freezing behavior can be enough to avoid a predatory menace but, when the risk is high or increases to a higher level, animals switch strategy and engage in an escape response. The neural bases underlying escape responses to visual stimuli are extensively investigated both in vertebrates and arthropods. However, those involved in freezing behaviors are much less studied. Here, we investigated the freezing behavior displayed by the crab Neohelice granulata when confronted with a variety of looming stimuli simulating objects of distinct sizes approaching on a collision course at different speeds. The experiments were performed in a treadmill-like device. Animals engaged in exploratory walks respond to the looming stimulus with freezing followed by escaping. The analysis of the stimulus optical variables shows that regardless of the looming dynamic, the freezing decision is made when the angular size of the object increases by 1.4°. In vivo intracellular recording responses of Monostratified Lobula Giant Neurons (MLG1) to the same looming stimuli show that the freezing times correlate with the times predicted by a hypothetical spike counter of this neuron.

Improvement of the gait deviation index for spinal cord injury to broaden its applicability: the reduced gait deviation index for spinal cord injury (rSCI-GDI).

The SCI-GDI is an accurate and effective metric to summarize gait kinematics in adults with SCI. It is usually computed with the information registered with a photogrammetry system because it requires accurate information of pelvic and hip movement in the three anatomic planes, which is hard to record with simpler systems. Additionally, due to being developed from the GDI, the SCI-GDI is built upon nine joint movements selected for a pediatric population with cerebral palsy, for which the GDI was originally developed, but those nine movements are not necessarily as meaningful for adults with SCI. Nevertheless, pelvic movement and hip rotation have been proven to have low reliability even when acquired with gold-standard photogrammetry systems. Additionally, the use of photogrammetry is limited in real-life scenarios and when used with rehabilitation technologies, which limits the use of the SCI-GDI to evaluate gait in alternative scenarios to gait laboratories and to evaluate technologies for gait assistance. This research aimed to improve the SCI-GDI to broaden its applicability beyond the use of photogrammetry. An exploration of the mathematical relevance of each joint movement included in the original GDI for the performance of the metric is performed. Considering the results obtained and the clinical relevance of each of the 9 joints used to compute the SCI-GDI in the gait pattern of the SCI population, a more adaptable SCI-GDI is proposed using four joint movements that can be precisely captured with simpler systems than photogrammetry: sagittal planes of hip, knee and ankle and hip abduction/adduction. The reduced SCI-GDI (rSCI-GDI) effectively represents gait variability of adults with SCI as does the SCI-GDI, while providing more generalizable results and equivalent or stronger correlations with clinical tests validated in the population. During the derivation of the improved index, it was demonstrated that pelvic movements, hip rotation, and foot progression angle introduce high variability to the dataset of gait patterns of the adult population with SCI, but they have low relevance to characterize gait kinematics of this population. The rSCI-GDI can be calculated using the 14-feature vectorial basis included in the electronic addendum provided.

Dead-reckoning facilitates determination of activity and habitat use: a case study with European badgers (Meles meles)

BackgroundStudies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited.MethodsCollars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type.ResultsNightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively).Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances.ConclusionsThis study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behavior

With the advancements in technology, telematics data that capture vehicle movement information are becoming available to more insurers. Because these data capture the actual driving behavior, they are expected to improve our understanding of driving risk and facilitate more accurate auto insurance ratemaking. In this article, we analyze an auto insurance dataset with telematics data collected from a major European insurer. Through a detailed discussion of the telematics data structure and related data quality issues, we elaborate on practical challenges in processing and incorporating telematics information in loss modeling and ratemaking. Then, with an exploratory data analysis, we demonstrate the existence of heterogeneity in individual driving behavior, even within the groups of policyholders with and without claims, which supports the study of telematics data. Our regression analysis reiterates the importance of telematics data in claims modeling; in particular, we propose a speed transition matrix that describes discretely recorded speed time series and produces statistically significant predictors for claim counts. We conclude that large speed transitions, together with higher maximum speed attained, nighttime driving, and increased harsh braking, are associated with increased claim counts. Moreover, we empirically illustrate the learning effects in driving behavior: we show that both severe harsh events detected at a high threshold and expected claim counts are not directly proportional with driving time or distance but they increase at a decreasing rate.

A New Accurate Aircraft Trajectory Prediction in Terminal Airspace Based on Spatio-Temporal Attention Mechanism

Trajectory prediction serves as a prerequisite for future trajectory-based operation, significantly reducing the uncertainty of aircraft movement information within airspace by scientifically forecasting the three-dimensional positions of aircraft over a certain period. As convergence points in the aviation network, airport terminal airspace exhibits the most complex traffic conditions in the entire air route network. It has stronger mutual influences and interactions among aircraft compared to the en-route phase. Current research typically uses the trajectory time series information of a single aircraft as input for subsequent predictions. However, it often lacks consideration of the close-range spatial interactions between multiple aircraft in the terminal airspace. This results in a gap in the study of aircraft trajectory prediction that couples spatiotemporal features. This paper aims to predict the four-dimensional trajectories of aircraft in terminal airspace, constructing a Spatio-Temporal Transformer (ST-Transformer) prediction model based on temporal and spatial attention mechanisms. Using radar aircraft trajectory data from the Guangzhou Baiyun Airport terminal airspace, the results indicate that the proposed ST-Transformer model has a smaller prediction error compared to mainstream deep learning prediction models. This demonstrates that the model can better integrate the temporal sequence correlation of trajectory features and the potential spatial interaction information among trajectories for accurate prediction.

A flexible sensor based on ionogel/knitted coil prepared by a simple method for accurate monitoring of human physiological signals

Flexible sensors have received increasing attention due to their various applications in health monitoring, artificial intelligence, and other fields. The simple fabrication of flexible sensing devices with high performances has been a research hotpot. In this work, an ionogel composite sensor combined with elastic knitted coil (EIC) is proposed as a wearable device to monitor human movement and related feature information. This sensor can be fabricated in a simple and easy protocol, which can work as a clothing accessary to track the wellness status parameters in daily life. After washing, the redundant gel originally embedded between the fibers was removed, improving the sensitivity of the sensor, which can provide accurate information about human joint movements and other physiological activities. The EIC sensor is in high sensitivity with superior working durability, which can provide continuous piezoresistive signals in detection mode. Other than the common joint bending movements (wrist, knee, elbow and finger) of human body, this sensor can also accurately output monitoring data on periocular, oral, and pharyngeal movements, providing information about human health status. Besides, a Morse codes transmission system can be constructed based on the piezoresistive outputs of this EIC sensor under different tapping modes, providing a new working platform in other related applications.

The robustness waterproof ionogel based on the phase separation to form soft hard heterostructures and the interaction of cation-π realizes underwater adhesion and sensing

Flexible ionic conductors hold significant promise for advancing the field of soft ion electronics in the future. However, the creation of flexible conductors that possess mechanical robustness, underwater adhesion and underwater motion monitoring remains a challenge. Drawing inspiration from the multiphase hetero structure found in biological connective tissues, such as high modulus fibers and low modulus water-rich matrices, this article proposes a one-pot polymerization in-situ design strategy to create a hydrophobic ionogel with a hetero structure of soft and hard domains. The mechanical robustness, elasticity, and toughness of the ionogel are achieved through a synergistic combination of a strong skeleton in the hard domain and an elastic matrix in the soft domain. Additionally,hydrophobic aromatic rings and quaternary ammonium cation simulated cation-π interactions in mussel foot silk protein. Upon contact with water, the hydrophobic clusters quickly aggregate, facilitating the expulsion of interfacial water and exposing salicylic acid groups and quaternary ammonium cationic groups, leading to outstanding underwater adhesion between the ionogel and various substrates. The resulting hydrophobic ionogel can be utilized in underwater wearable electronic devices for monitoring human movement and physiological information, underwater repair monitoring, and intelligent soft robotics, demonstrating its vast potential in the field of underwater sensors.

Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates.

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (µECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm 2 area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate µECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces. Significance statement Picking up your coffee mug requires coordinating movements of your eyes and hand and processing the outcomes of those movements. Mapping how neural activity relates to different functions helps us understand how the brain performs these computations. Many mapping techniques have limited spatial or temporal resolution, restricting our ability to dissect computations that overlap closely in space and time. We used micro-electrocorticography recordings to map neural activity across multiple cortical areas while monkeys made goal-directed reaches. These measurements revealed high spatial and temporal resolution maps of neural activity related to eye, arm, and reward information processing. These maps reveal overlapping neural computations underlying movement and open opportunities to use eye and reward information to improve therapies to restore motor function.

LDIPRS: A novel longitudinal driving intention prior recognition technique empowered by TENG and deep learning

For autonomous vehicles, real-time and accurate longitudinal driving intention recognition is crucial as it effectively enhances driving safety and improves the driving experience. This study proposes a novel data and model hybrid-driven fine-grained longitudinal driving intention prior recognition system (LDIPRS). Firstly, the system integrates a human-pedal interaction sensor (HPIS) based on triboelectric nanogenerators for fine-grained longitudinal driving maneuver monitoring and the channel attention (CA)-enhanced convolutional neural network (CBRCNet). The HPIS, integrated into the vehicle's acceleration and brake pedals, is capable of monitoring driver foot movement information in the form of electrical signals before the vehicle responds, achieving data level advance. The collected electrical signals are fed into the CBRCNet network, which models and learns the mapping relationship between these signals and fine-grained longitudinal driving intentions, leading to model level advances. The HPIS completes the capture of longitudinal maneuver information 541 ms before the driving simulator starts to respond at the data level. At the model level, CBRCNet can achieve a recognition accuracy of 96.1 % based on partial response data (50 ms after starting response) rather than complete response data of the HPIS. Finally, our proposed LDIPRS realizes the recognition of emergency braking, rapid acceleration, normal braking, and normal acceleration in advance by 732 ms, 1035 ms, 1757 ms, and 2227 ms, respectively. This study introduces self-powered, low-cost, highly sensitive triboelectric sensors into the field of intention recognition, and combines the triboelectric sensors with deep learning algorithms to offer a promising solution to improve the safety of autonomous vehicles and the efficiency of intelligent transportation systems.

Context, transparency and culture in motor resonance phenomena: Causal evidence of the motor cortex

A connection between language and movement information in metaphorical and literal expressions activates the motor system. Despite numerous studies exploring distinctions between idioms and metaphors, a notable research gap remains in the specific effect of idioms with different transparency levels concerning motor resonance. Our primary focus was analysing the functional role of the primary motor cortex (M1) in processing hand motor verbs both in literal expressions and in two idiomatic contexts, i.e., opaque and transparent idioms. Additionally, we explored a potential language and cultural effect by comparing Turkish and Spanish speakers. An overt priming task with self-paced reading was used to judge the relatedness of a primer and a target sentence. We implemented a repetitive transcranial magnetic stimulation (TMS) protocol using continuous theta-burst stimulation (cTBS) compared to sham stimulation over the M1 in both Turkish and Spanish native speakers prior to the experimental task. Our findings reveal that the performance of Turkish and Spanish participants in processing hand motor actions was facilitated after the application of cTBS over the left M1. Moreover, brain stimulation specifically facilitated the processing of only transparent—but not opaque—idioms in both Spanish and Turkish participants. Our study reports distinct motor resonance results between different types of idioms with a parallel cross-cultural effect.

Understanding Places Exploration and Visitation via Human Mobility Mining

Spatial-temporal data is widely available because of advancement in location acquisition technologies (GPS, GSM, Wifi, etc.) over past decades. This spatial-temporal data can easily be used to leverage user's trajectories, history, and habits to develop location-based services (LBS). But to leverage user's history based on location is bit challenging, in this paper we used the user's GPS log data to discover trajectories and analyze different life patterns over it. We proposed that a human spends 80% of his life on known places or safe places. We converted GPS patterns into visitation locations, converted them into daily and periodic trends and analyzed them over time to prove our assumption. We used the GPS data collected by Microsoft Research Asia, this data has 182 users with 73 users with their mode of movement information i.e., Taxi, Walk, Train etc.

Recent Advances in Self-Powered Wearable Flexible Sensors for Human Gaits Analysis.

The rapid progress of flexible electronics has met the growing need for detecting human movement information in exoskeleton auxiliary equipment. This study provides a review of recent advancements in the design and fabrication of flexible electronics used for human motion detection. Firstly, a comprehensive introduction is provided on various self-powered wearable flexible sensors employed in detecting human movement information. Subsequently, the algorithms utilized to provide feedback on human movement are presented, followed by a thorough discussion of their methods and effectiveness. Finally, the review concludes with perspectives on the current challenges and opportunities in implementing self-powered wearable flexible sensors in exoskeleton technology.

The effects of analogy teaching on sport skill acquisition in children

Introduction: Teaching motor skills to novices has been the main task of physical educators, and studies in motor learning have always sought to discover better ways to optimize the skill acquisition process. Movement information is presented transparently through the performer’s demonstration, illustration, feedback, and verbal guidance in motor skills instructing. This study seeks to determine the type of learning, i.e., analogy learning or explicit learning, suitable for promoting the children’s acquisition of skills that are biomechanically and kinematically different from the same skill learned by explicit instructions, and to determine how the physical form of the skill evolves over practice. Material and Methods: Forty-five right-handed healthy beginner male students (mean age 9.93 ±0.55 years; height 1.39 ±4.16 m; body mass 31.65±3.23 kg; novice in basketball) participated in the study. Subjects were randomly allocated to an explicit learning condition (n = 15), an analogy learning condition (n = 15), or an uninstructed control condition (n = 15). Ten free throws (FT) in the standing position were performed with the right hand by each student using modified equipment i.e., a small ball, customized rim with 45-cm circumference and adapted net height of 2 m. Attempts were recorded from 3 m away in a biomechanical laboratory with two-dimensional (2D) video data collection (i.e., using 240 Hz camera resolution). Results: The independent ANOVA yielded a statistically significant effect across the three groups in post-tests: F (8,16) = 283.233, P = 0.001, η2 = 0.793. A statistically significant difference was observed across the three groups: F (8, 16) = 332.057, P = 0.001, η2 = 0.818. Discussion: The analogy group's performance was significantly better in the skill taught. The explicit learners achieved lower scores compared to the analogy learners. Conclusions: The analogy learning training method is recommended for children aged 10–12 willing to learn FT in basketball. The conclusions may be important for coaches concerning the use of different training methods in skill learning.

[formula omitted]: Movement Enhancement with Multi-Relation toward Multi-Scale video representation for Temporal Action Detection

Locating boundary is very important for Temporal Action Detection (TAD) and is a key factor affecting the performance of TAD. However, two factors lead to inaccurate boundary localization: the movement feature submergence and the existence of multi-scale actions. In this work, to address the submergence of movement feature, we design the Movement Enhance Module (MEM), in which the Movement Feature Extractor (MFE) and Multi-Relation Module (MRM) are used to highlight short-term and long-term movement information respectively. To address the characteristic of multi-scale actions, we propose a Scale Feature Pyramid Network (SFPN) to detect multi-scale actions and design a two-stage training strategy that makes each layer focus on a specific scale action. These tow modules are integrated as M3Net, and extensive experiments demonstrate its effectiveness. M3Net outperforms other representative TAD methods on ActivityNet-1.3 and THUMOS-14.

SDN-based reliable emergency message routing schema using Digital Twins for adjusting beacon transmission in VANET

Digital Twin (DT) has revolutionized the contextualized digital environment. This advancement enables real-time monitoring and simulation of events, leading to more effective decision-making. In smart transportation, DT plays a crucial role in enhancing various aspects of road decision-making, including optimizing routing decisions for Emergency Message (EM) forwarding in Vehicular Ad hoc Networks (VANETs). In this study, DT has integrated with cutting-edge technologies such as Software-Defined-Network (SDN) and geographically distributed computing (fog computing) to elevate the position-based routing behavior in two directions: selecting the optimal next-hop and taking control of periodic messages (beacon transmission). The nature of routing decisions can be complex due to constant movement and frequent position changes of vehicles in the VANET system. Merely selecting the next-hop vehicle without considering its stability and predictability can result in suboptimal routing choices. To this end, SDN based on Reliable Emergency Message Routing Schema (SDN-REMR) is introduced. This schema utilizes DT and aims to leverage the behaviors of moving vehicles to enhance the delivery of EM by providing a reliable routing mechanism. To eliminate unstable neighbors from the list of potential next-hops, SDN-REMR forecasts the relative positions of surrounding vehicles using Euclidean distance and position information. Moreover, SDN-REMR uses the movement information collected from vehicles (such as position, speed, and power consumption) to reduce the possibility of a link disruption and select the best next-hop for reliable communication. SDN-REMR enables the Store-Carry-Forward (SCF) approach to ensure packets are successfully forwarded to the destination, even in challenging network conditions. This control strategy aims to strike a balance between maintaining reliable communication and minimizing congestion in the network. The experimental results prove that there are 42.6%, 76.22%, 76.22%, and 22.2% enhancement in terms of network throughput, total delivered packets, number of dropped packets, and routing overhead.

Urban Road Surface Condition Sensing from Crowd-Sourced Trajectories Based on the Detecting and Clustering Framework.

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users' travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications.

Virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition

Currently, artificial intelligence and virtual technology are widely used in motion monitoring. Virtual entertainment robots can provide adaptive and personalized fitness services for different user groups, allowing users to experience the exercise process in games. This article studies the virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition. Firstly, this article explores deep learning methods for motion recognition. By constructing a generative adversarial network to obtain discrimination results, and calculating its anti-loss function. This approach can improve the efficiency of human body movement style conversion through adversarial training. Finally, through the evaluation of movement posture and action recognition, quantitative evaluation results and targeted guidance strategies can be provided to help correct incorrect movements and master correct techniques. Using motion capture systems to identify user movements during exercise not only accurately extracts user fitness movement information, but also makes it easier to use and reduces user costs. The system uses motion evaluation techniques for evaluation to help users correct incorrect fitness habits, prevent exercise losses, and exercise more safely and effectively.

A New Perspective to Interpret How the Vestibular Efferent System Correlates the Complexity of Routine Balance Maintenance with Management of Emergency Fall Prevention Strategies.

Bipedalism is unique among mammals. Until modern times, a fall and resulting leg fracture could be fatal. Balance maintenance after a destabilizing event requires instantaneous decision making. The vestibular system plays an essential role in this process, initiating an emergency response. The afferent otolithic neural response is the first directionally oriented information to reach the cortex, and it can then be used to initiate an appropriate protective response. Some vestibular efferent axons feed directly into type I vestibular hair cells. This allows for rapid vestibular feedback via the striated organelle (STO), which has been largely ignored in most texts. We propose that this structure is essential in emergency fall prevention, and also that the system of sensory detection and resultant motor response works by having efferent movement information simultaneously transmitted to the maculae with the movement commands. This results in the otolithic membrane positioning itself precisely for the planned movement, and any error is due to an unexpected external cause. Error is fed back via the vestibular afferent system. The efferent system causes macular otolithic membrane movement through the STO, which occurs simultaneously with the initiating motor command. As a result, no vestibular afferent activity occurs unless an error must be dealt with.

Simulation of artificial intelligence robots in dance action recognition and interaction process based on machine vision

Artificial intelligence robots based on machine vision have shown great potential in dance action recognition and interaction. In order to perform action recognition, a large amount of dance action video data was collected as the basis for training and testing the model. In motion recognition, various computer vision techniques are used to extract motion features from images or videos to capture key information of dance movements. Machine learning technology was adopted to construct an action classification model. By using the data collected in the early stage and the extracted features, an efficient and accurate classifier has been studied and trained. It can classify the input dance actions into different categories, segment continuous video frames into different action segments, and ensure accurate differentiation of different dance actions during the recognition process, avoiding misjudgment or confusion. By accurately capturing and analyzing the posture information of the human body, we can better understand and simulate dance movements, and enhance the performance and realism of artificial intelligence robots in dance interactions. The experimental results show that the system can accurately recognize various dance movements and achieve good interaction with users.