Motion Recognition Research Articles

Water-driven mechanoluminescent film for multifunctional sensing and display

Mechanoluminescent (ML) materials have garnered significant attention due to their capacity for converting mechanical stress into light emission without the need for external power sources. However, very limited efforts have been cast on the response and utilization of water stimulation using ML materials. Herein, we present a highly efficient self-powered luminescent film that exhibits excellent water-responsive properties. The ML intensity of this film shows the zero-quenching performance when exposed to corrosive solutes (acid or saline solution) and within a broad temperature range of 0−100 °C. The ML intensity of the film remains unaffected by long-term water immersion and the impact of water (>1000 times), providing evidence of the durability of this film in humid environments. The COMSOL simulations aim to uncover the dynamic stress distribution of the ML film under two different modes of water stimulation (vibration and deformation). Furthermore, this ML film is used for water speed sensing, and its water speed sensing range is 0.8 m s−1 −6.02 m s−1. Meanwhile, the film's applications have been demonstrated in illumination and display under hydrodynamic force stimulation, human motion recognition, and underwater sensing and communication. The results show that the film can effectively realize the response and visualization to the hydrodynamic force and broaden the application field of mechanoluminescent materials.

A study on the application of digital technology in improving the quality and effectiveness of dance teaching

Abstract In this paper, the dance teaching digitization process and virtual character dance movement dataset are designed, combined with the Vicon motion capture system, neural network fusion shape technology and dance movement recognition algorithm to realize dance teaching digitization. The traditional K-modes algorithm is improved from three aspects: determining the number of clustering families, the determination of the initial clustering center, and the clustering distance metric to construct a digital dance teaching quality evaluation model. After the application analysis, it was concluded that the difference between the students of the experimental group and the students of the conventional group of dance movements was P<0.05, and the learning situation of the experimental group showed an upward trend, the learning enthusiasm increased by 0.21, the learning efficiency increased by 0.23, and the clustering index were higher than 0.9. Dance teaching based on digital technology can improve the quality and effect of the teaching.

Retracted: Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Retracted: Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Interpretable Multi-Channel Capsule Network for Human Motion Recognition

Recently, capsule networks have emerged as a novel neural network architecture for human motion recognition owing to their enhanced interpretability compared to traditional deep learning networks. However, the characteristic features of human motion are often distributed across distinct spatial dimensions and existing capsule networks struggle to independently extract and combine features across multiple spatial dimensions. In this paper, we propose a new multi-channel capsule network architecture that extracts feature capsules in different spatial dimensions, generates a multi-channel capsule chain with independent routing within each channel, and culminates in the aggregation of information from capsules in different channels to activate categories. The proposed structure endows the network with the capability to independently cluster interpretable features within different channels; aggregates features across channels during classification, thereby enhancing classification accuracy and robustness; and also presents the potential for mining interpretable primitives within individual channels. Experimental comparisons with several existing capsule network structures demonstrate the superior performance of the proposed architecture. Furthermore, in contrast to previous studies that vaguely discussed the interpretability of capsule networks, we include additional visual experiments that illustrate the interpretability of the proposed network structure in practical scenarios.

Inhibitory TMS over Visual Area V5/MT Disrupts Visual Speech Recognition.

During face-to-face communication, the perception and recognition of facial movements can facilitate individuals' understanding of what is said. Facial movements are a form of complex biological motion. Separate neural pathways are thought to processing (1) simple, nonbiological motion with an obligatory waypoint in the motion-sensitive visual middle temporal area (V5/MT); and (2) complex biological motion. Here, we present findings that challenge this dichotomy. Neuronavigated offline transcranial magnetic stimulation (TMS) over V5/MT on 24 participants (17 females and 7 males) led to increased response times in the recognition of simple, nonbiological motion as well as visual speech recognition compared with TMS over the vertex, an active control region. TMS of area V5/MT also reduced practice effects on response times, that are typically observed in both visual speech and motion recognition tasks over time. Our findings provide the first indication that area V5/MT causally influences the recognition of visual speech.SIGNIFICANCE STATEMENT In everyday face-to-face communication, speech comprehension is often facilitated by viewing a speaker's facial movements. Several brain areas contribute to the recognition of visual speech. One area of interest is the motion-sensitive visual medial temporal area (V5/MT), which has been associated with the perception of simple, nonbiological motion such as moving dots, as well as more complex, biological motion such as visual speech. Here, we demonstrate using noninvasive brain stimulation that area V5/MT is causally relevant in recognizing visual speech. This finding provides new insights into the neural mechanisms that support the perception of human communication signals, which will help guide future research in typically developed individuals and populations with communication difficulties.

Tangential Human Motion Recognition With Micro-Doppler Signatures and One-Shot Learning

Tangential Human Motion Recognition With Micro-Doppler Signatures and One-Shot Learning

Multi-characteristic tannic acid-reinforced polyacrylamide/sodium carboxymethyl cellulose ionic hydrogel strain sensor for human-machine interaction

Big data and cloud computing are propelling research in human-computer interface within academia. However, the potential of wearable human-machine interaction (HMI) devices utilizing multiperformance ionic hydrogels remains largely unexplored. Here, we present a motion recognition-based HMI system that enhances movement training. We engineered dual-network PAM/CMC/TA (PCT) hydrogels by reinforcing polyacrylamide (PAM) and sodium carboxymethyl cellulose (CMC) polymers with tannic acid (TA). These hydrogels possess exceptional transparency, adhesion, and remodelling features. By combining an elastic PAM backbone with tunable amounts of CMC and TA, the PCT hydrogels achieve optimal electromechanical performance. As strain sensors, they demonstrate higher sensitivity (GF = 4.03), low detection limit (0.5 %), and good linearity (0.997). Furthermore, we developed a highly accurate (97.85 %) motion recognition system using machine learning and hydrogel-based wearable sensors. This system enables contactless real-time training monitoring and wireless control of trolley operations. Our research underscores the effectiveness of PCT hydrogels for real-time HMI, thus advancing next-generation HMI systems.

Retracted: Dance Movement Recognition Based on Modified GMM-Based Motion Target Detection Algorithm

Retracted: Dance Movement Recognition Based on Modified GMM-Based Motion Target Detection Algorithm

The repatriation of African heritage: shutting the door on the imperialist narrative

This article is written in recognition of the repatriation movement, which is going through a renaissance in relation to the cultural heritage of African peoples. The collecting of African cultural heritage without free, prior and informed consent was a feature of colonialism. This article highlights the vulnerability of past and present African heritage in the light of the ‘imperialist narrative’. The imperialistic narrative accompanied the act of colonialism in Africa and enabled the taking of African heritage to public and private collections in Europe and America where many remain. Much of the heritage was displayed as an African ‘curiosity box’ which helped to support the now discredited idea of a hierarchy of peoples. This articleargues that until there is a steady stream of African heritage returning home to Africa the narrative will continue to impact in situ African heritage including natural resources. Until museums repatriate African heritage unreservedly, the ‘imperialist narrative’ will exclude the corollary narrative of African Renaissance. The article examines potential restitution/repatriation mechanisms for African peoples and states for the return of their cultural heritage, drawing on the UNESCO conventions, the African Union Charter for African Cultural Renaissance, the Sarr and Savoy Report, ‘Restitution of African Cultural Heritage. Toward a New Relational Ethics’, the Report of the Expert Mechanism on the Rights of Indigenous PeoplesRepatriation of ceremonial objects, human remains, and intangible resources under the United Nations Declaration on the Rights of Indigenous Peoples’ and the ECOWAS 2019/2023 Action Plan on the return of cultural properties to their countries of origin.

Movement recognition via channel-activation-wise sEMG attention.

Surface electromyography (sEMG) signals contain rich information recorded from muscle movements and therefore reflect the user's intention. sEMG has seen dominant applications in rehabilitation, clinical diagnosis as well as human engineering, etc. However, current feature extraction methods for sEMG signals have been seriously limited by their stochasticity, transiency, and non-stationarity. Our objective is to combat the difficulties induced by the aforementioned downsides of sEMG and thereby extract representative features for various downstream movement recognition. We propose a novel 3-axis view of sEMG features composed of temporal, spatial, and channel-wise summary. We leverage the state-of-the-art architecture Transformer to enforce efficient parallel search and to get rid of limitations imposed by previous work in gesture classification. The transformer model is designed on top of an attention-based module, which allows for the extraction of global contextual relevance among channels and the use of this relevance for sEMG recognition. We compared the proposed method against existing methods on two Ninapro datasets consisting of data from both healthy people and amputees. Experimental results show the proposed method attains the state-of-the-art (SOTA) accuracy on both datasets. We further show that the proposed method enjoys strong generalization ability: a new SOTA is achieved by pretraining the model on a different dataset followed by fine-tuning it on the target dataset.

Design Electric Bells to Improve Kinesthetic Intelligence for the View Image in the Library and Archive Department of Bengkulu Province

This study aims to design an electric bell and determine the kinesthetic improvement ability of visually impaired people after using the Dewey Decimal Classification (DDC) listicv bell as an information guide. When a visitor wants to know the braille book, title, location, and classification of Dewey (DDC) then it is necessary to press the DDC bell and it will ring. People with visually impaired disabilities were the subjects of this study, and ten people as the object. Data collection using Tests, interviews, and documentation is used to collect data. The method used quantitative experiments. In addition, descriptive analysis showed that the average pre-test score was 25.87 and the average post-test score was 21.05. so Ha is accepted and Ho is rejected. The results of the study stated that the use of the Dewey electric bell (DDC) by the Bengkulu Provincial Library and Archives Service affected the kinesthetic ability of movement, visual and motor recognition of book classifications for the visually impaired.

Research on the identification and integration of folk dance creation elements based on big data technology

Abstract In order to identify the elements of folk dance creation, two methods of continuous folk dance movement recognition are proposed in this paper. By considering the dance movement patterns as a whole and the templates as incomplete patterns, OE-DTW is applied to match them to segment and recognize each movement pattern one by one. To enhance the performance, a global restriction K-Repetition and endpoint detection condition are proposed to match with OE-DTW, and a penalty-based layer matching algorithm is proposed by using the layer structure feature of SegSVD so that the endpoint of the input movement pattern can be determined by the top layer matching of the latter when matching with the template pattern. Meanwhile, the similarity between two patterns based on this algorithm can also be calculated by the local results obtained from the weighted cumulative layer matching. Experiments show that the average recognition rate of OE-DTW is 0.891, SegSVD is 0.86, and CDP is 0.828. Both methods can effectively deal with the continuous action recognition problem, and they have better recognition results compared with CDP.

A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar.

Due to the outstanding penetrating detection performance of low-frequency electromagnetic waves, through-wall radar (TWR) has gained widespread applications in various fields, including public safety, counterterrorism operations, and disaster rescue. TWR is required to accomplish various tasks, such as people detection, people counting, and positioning in practical applications. However, most current research primarily focuses on one or two tasks. In this paper, we propose a multitask network that can simultaneously realize people counting, action recognition, and localization. We take the range-time-Doppler (RTD) spectra obtained from one-dimensional (1D) radar signals as datasets and convert the information related to the number, motion, and location of people into confidence matrices as labels. The convolutional layers and novel attention modules automatically extract deep features from the data and output the number, motion category, and localization results of people. We define the total loss function as the sum of individual task loss functions. Through the loss function, we transform the positioning problem into a multilabel classification problem, where a certain position in the distance confidence matrix represents a certain label. On the test set consisting of 10,032 samples from through-wall scenarios with a 24 cm thick brick wall, the accuracy of people counting can reach 96.94%, and the accuracy of motion recognition is 96.03%, with an average distance error of 0.12 m.

Synergistic promotion between tolerant polymer electrode and super-conductive polyelectrolyte enables anti-damage transient wearable electronics

Manufacturing highly durable and transient materials is urgently needed to achieve optimal performances in wearable electronics. Here, a comprehensive methodology to enhance the durability and transience of wearable electronics by utilizing polypyrrole/vermiculite/polysaccharide (PPy/VMT/AF) electrodes and acid-assisted polyacrylamide (HPAM) electrolytes. The mechanical/thermal/electrochemical properties of integrated electrodes are boosting by introducing negatively charged vermiculite in conductive polymer-polysaccharide, which can be completely degrade after 90 days. Pre-gel drying shrinkage and rehydration were utilized to prepare polyacrylamide hydrogel with frost resistance and ultrahigh conductivity (increased 32.2 times as compared to traditional polyacrylamide). The synergism between PPy/VMT/AF and HPAM enables flexible solid-state supercapacitors with exceptional electrochemical stability even under serious damage such as puncturing, burning and freeze. In addition, the fabricated self-powered pressure sensor realizes ML-assisted human motion recognition with an accuracy of 95.7 %. This work opens up a new perspective on future directions for multifunctional platforms for wearable electronic devices.

Human Motion Capture and Recognition Based on Sparse Inertial Sensor

The field of human motion capture technology represents an emergent and multifaceted domain that encapsulates various disciplines, including but not limited to computer graphics, ergonomics, and communication technology. A distinct network platform within its domain has been established to ensure the reliability and stability of data transmission. Moreover, a sink node has been configured to facilitate sensor data reception through two distinct channels. Notably, the simplicity of the measurement system is directly proportional to the limited number of sensors used. This study focuses on accurately estimating uncertain human 3D movements via a sparse arrangement of wearable inertial sensors, utilizing only six sensors within the system. The methodology is based on a time series sequence throughout the motion process, wherein a series of discontinuous actions constitute the sequential motion. Deep learning methodologies, specifically recurrent neural networks, were employed to refine the regression parameters. Our approach integrated both historical and present sensor data to forecast future sensor data. These data were amalgamated into a superposed input vector, which was fed back into a shallow neural network to estimate human motion. Our experimental results demonstrate the viability of this approach: the six sensors could accurately replicate representative poses. This finding carries significant implications for advancing and applying wearable devices within the realm of motion capture, offering the potential for widespread adoption and implementation.

Retracted: Dance Movement Recognition Based on Multimodal Environmental Monitoring Data.

[This retracts the article DOI: 10.1155/2022/1568930.].

Actuator-level motion and contact episode learning and classification using adaptive resonance theory

Several methods exist to detect and distinguish collisions of robotic systems with their environment, since this information is a critical dependency of many tasks. These methods are prevalently based on thresholds in combination with filters, models, or offline trained machine learning models. To improve the adaptation and thereby enable a more autonomous operation of robots in new environments, this work evaluates the applicability of an incremental learning approach. The method addresses online learning and recognition of motion and contact episodes of robotic systems from proprioceptive sensor data using machine learning. The objective is to learn new category templates representing previously encountered situations of the actuators and improve them based on newly gathered similar data. This is achieved using an artificial neural network based on adaptive resonance theory (ART). The input samples from the robot’s actuator measurements are preprocessed into frequency spectra. This enables the ART neural network to learn incrementally recurring episodic patterns from these preprocessed data. An evaluation based on preliminary experimental data from a grasping motion of a humanoid robot’s arm encountering contacts is presented and suggests that this is a promising approach.

A motion recognition model for upper-limb rehabilitation exercises

A motion recognition model for upper-limb rehabilitation exercises

Design and application of intelligent fitness system based on android system

AbstractThe purpose of this study is to make use of the features of mobile smart phones to create a simple step by step fitness application that can exercise anytime and anywhere. A third‐party open‐source library based on the Android platform was adopted in the entire technology selection, which greatly reduces the writing of a lot of pattern code and enables developers to focus more on the development of fitness system functions. The system has basic functions such as “Fitness data” and “Fitness video courses,” and collects fitness movements through sensors. Based on the extracted eigenvalues and support vector machines (SVM) algorithm, the system determines whether the fitness actions are standard or not, and uses back propagation (BP) neural network to classify and count fitness actions, to achieve recognition of fitness movements. The experimental results show that, the response time, running speed, and load of the system meet daily needs, the system can give the correct instruction probability within 3 s is 74.2%, the correct instruction probability within 3 ˜ 4 s is 17.5%, and the recognition probability after 4 s is only 6.67%, which proves that the intelligent fitness system designed in this study based on the Android system meets basic usage requirements in terms of performance and functionality.

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques.

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition.