Movement Of Persons Research Articles

Durability of motor learning by observing.

Information about another person's movement kinematics obtained through visual observation activates brain regions involved in motor learning. Observation-related changes in these brain areas are associated with adaptive changes to feedforward neural control of muscle activation and behavioral improvements in limb movement control. However, little is known about the stability of these observation-related effects over time. Here, we used force channel trials to probe changes in lateral force production at various time points (1 min, 10 min, 30 min, 60 min, 24 h) after participants either physically performed, or observed another individual performing upper limb reaching movements that were perturbed by novel, robot-generated forces (a velocity-dependent force-field). Observers learned to predictively generate directionally and temporally specific compensatory forces during reaching, consistent with the idea that they acquired an internal representation of the novel dynamics. Participants who physically practiced in the force-field showed adaptation that was detectable at all time points, with some decay detected after 24 h. Observation-related adaptation was less temporally stable in comparison, decaying slightly after 1 h and undetectable at 24 h. Observation induced less adaptation overall than physical practice, which could explain differences in temporal stability. Visually acquired representations of movement dynamics are retained and continue to influence behavior for at least 1 h after observation.NEW & NOTEWORTHY We used force channel probes in an upper limb force-field reaching task in humans to compare the durability of learning-related changes that occurred through visual observation to those after physical movement practice. Visually acquired representations of movement dynamics continued to influence behavior for at least 1 h after observation. Our findings point to a 1-h window during which visual observation of another person could play a role in motor learning.

Awareness of embodiment enhances enjoyment and engages sensorimotor cortices.

Whether in performing arts, sporting, or everyday contexts, when we watch others move, we tend to enjoy bodies moving in synchrony. Our enjoyment of body movements is further enhanced by our own prior experience with performing those movements, or our 'embodied experience'. The relationships between movement synchrony and enjoyment, as well as embodied experience and movement enjoyment, are well known. The interaction between enjoyment of movements, synchrony, and embodiment is less well understood, and may be central for developing new approaches for enriching social interaction. To examine the interplay between movement enjoyment, synchrony, and embodiment, we asked participants to copy another person's movements as accurately as possible, thereby gaining embodied experience of movement sequences. Participants then viewed other dyads performing the same or different sequences synchronously, and we assessed participants' recognition of having performed these sequences, as well as their enjoyment of each movement sequence. We used functional near-infrared spectroscopy to measure cortical activation over frontotemporal sensorimotor regions while participants performed and viewed movements. We found that enjoyment was greatest when participants had mirrored the sequence and recognised it, suggesting that awareness of embodiment may be central to enjoyment of synchronous movements. Exploratory analyses of relationships between cortical activation and enjoyment and recognition implicated the sensorimotor cortices, which subserve action observation and aesthetic processing. These findings hold implications for clinical research and therapies seeking to foster successful social interaction.

Implementasi Pelatihan Tari Nusantara dalam Meningkatkan Nasionalisme dan Kesadaran Budaya

The study attempts to present a qualitative description about how Nusantara dance training may serve to develop nationalism and cultural awareness. That shall be accomplished via a systematic review method and presented in a qualitative descriptive manner. This research uses a systematic review process that includes a number of phases: (a) creating research questions, (b) performing a literature search, (c) performing screening and selection of research articles, (d) carrying out analysis and synthesis of qualitative findings, (e). This research indicates that dance training can be considered a process that gives support and knowledge to dance creators when creating a new movement that incorporates distinctive characteristics with the creator's creativity, and maintaining the distinctive characteristics of every person's movement. The success of the training is dependent to an approach and model that best corresponds to the creator's Key words: Training Implementation, Nusantara Dance, Nationalism, Cultural Awareness

Penerapan Asuhan Keperawatan Berdasarkan Self Transcendence Theory Pamela G.Reed

Nursing care is a process or series of activities in nursing practice that is provided directly to clients/patients in various health service settings. In implementing nursing care based on self-transcendence theory, nurses play the role of facilitators to help individuals experience growth and development through the self-transcendence process. Self-transcendence theory was developed by Pamela G. Reed and emphasizes the spiritual and transpersonal aspects of treatment. Reed explained that nurses act as facilitators to explore positive things and build bio-psycho-social spiritual well-being. Self Transcendence Theory aims to understand a person's movement in transcending self-limitations and achieving higher well-being. This theory focuses on five concepts, namely vulnerability, self-transcendence, well-being, moderating-mediating factors, and point of intervention. This self-transcendence offers the best for living in peace and prosperity. Apart from that, this theory is also used in the context of counseling services to help individuals utilize inner resources that empower patient capacities. Thus, the aim of the Self Transcendence theory is to understand and encourage individuals to achieve higher well-being through holistic self-understanding and development.

Automatic imitation in children: Age-related change and comparison to adults.

Automatic imitation, in which one person's movement is affected by the observation of another person's movements, has been widely reported. However, it remains unclear how automatic imitation changes over a wide age range, particularly during childhood. In this study, we examined the differences in the tendency for automatic imitation between adults and children and the cross-sectional age-related changes in children aged 5-12 years, using a stimulus-response conflict paradigm. In this task, participants perform a choice-reactive finger movement corresponding to a given response stimulus while observing another participant's compatible or incompatible movement stimuli. The tendency for automatic imitation was assessed based on the reaction time, correct rate, and inverse efficiency score. The results showed that the degree of automatic imitation was weak until the children were 7 years old. Interestingly, our results show that the tendency for automatic imitation during childhood changed to an inverted U-shape, indicating nonlinear changes in automatic imitation during childhood. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Converting a DJI Tello Quadcopter into a Face-follower Machine Using the Haar Cascade with PID Controller

Drones have been frequently used for photography in recent years at significantly cheaper rates. However, the most modern drones are exceedingly error-prone and require precise manual control to take high-quality photos or films. We suggest using the AI method of Haar cascades with a PID controller to give drones vision, enabling them to do autonomous tracking and detection. This project aims to improve photography fields. The proposed system tries to detect the face and track the person's movements. This system will help photographers and journalists upgrade their work, even if it is used in surveillance and the military. The algorithm's results show that the DJI Tello tiny drone's camera is capable of detecting and tracking faces. The micro drone was picked since it is lightweight and compact, making its use safe and enabling testing to take place inside. Additionally, the DJI Tello may be easily programmed using Python. The position of the drone is contrasted with the set point in the center of the image to identify errors, allowing control signals for calculating forward/backwards, right/left, and yaw movements. The proposed system results show that the drone can detect and track the face very well, and the PID values are stable.

The Relationship between Nutritional Status and Fundamental Movement Skills of Elementary School Students

The nutritional status and fundamental movement skills are one of things that are quite important for the growth and development of children. The nutritional status of children can affect a person's movement skills and fundamental basic movements can be related to daily activities. This research was conducted to know the relationship between nutritional status and fundamental movements in elementary schools. This type of research is a correlational study with a non-experimental research design. Participants included were grade IV students with an age range of 10-11 years of 100 people. The instruments used were BMI measurements and the TGMD-2 test. Data analysis techniques using formulas in the SPSS with spearman correlation. The results showed that the sig value of BMI and basic movement was 0.799 > 0.05 with a correlation coefficient value of 0.26. From these results, it can be concluded that there is no significant relationship between nutritional status and fundamental movement skills in elementary schools.

Seeing and understanding epistemic actions.

Many actions have instrumental aims, in which we move our bodies to achieve a physical outcome in the environment. However, we also perform actions with epistemic aims, in which we move our bodies to acquire information and learn about the world. A large literature on action recognition investigates how observers represent and understand the former class of actions; but what about the latter class? Can one person tell, just by observing another person's movements, what they are trying to learn? Here, five experiments explore epistemic action understanding. We filmed volunteers playing a "physics game" consisting of two rounds: Players shook an opaque box and attempted to determine i) the number of objects hidden inside, or ii) the shape of the objects inside. Then, independent subjects watched these videos and were asked to determine which videos came from which round: Who was shaking for number and who was shaking for shape? Across several variations, observers successfully determined what an actor was trying to learn, based only on their actions (i.e., how they shook the box)-even when the box's contents were identical across rounds. These results demonstrate that humans can infer epistemic intent from physical behaviors, adding a new dimension to research on action understanding.

Gait Monitoring and Analysis: A Mathematical Approach.

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson's, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson's disease by monitoring their gait due to wearable devices that can accurately detect a person's movements. In our study, about 50 people were involved in the trial (20 with Parkinson's disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the "gait quality" based on the measure of entropy obtained by applying the Fourier transform.

Human Following Robot Based on Motion Sensing Technology

In the current technological age, robots that follow humans are widespread. We can implement the technique of ‘human following’ in robots to follow a person inside a particular area. Human-robot communication is an integral component in this scenario. The human follower robot can assist us in both residential and industrial settings. Robots already exist to perform tasks at ease to assist humans has become a commendable and important development in the world of technology. Using an Arduino board along with an ultrasonic sensor and infrared sensors, the robot will detect motion and follow the person in motion.

Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method.

Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling a person's movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient's nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person's voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson's cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

Static and dynamic human activity recognition with VGG-16 pre-trained CNN model

Human Activity Recognition has been widely studied using the Convolutional Neural Network (CNN) algorithm to classify a person's movements by utilizing data from devices that record movements such as cameras. The benefits generated by this technology are useful for modern devices such as Virtual Reality and Smart Home technology with CCTV cameras. The VGG-16 (Visual Geometric Group with 16 Layers) pre-trained model is one of the models used for transfer learning and has won the Image Net competition. In this study, the authors tested the performance of the VGG-16 model to identify two types of human activity, namely Static and Dynamic. This study uses 1,680 public datasets which are divided into 80% Data Train, 10% Data Validation, and 10% Data Test I. In addition, there are also 100 local datasets as Data Test II. There is no overfitting issue in the training and testing process. The accuracy of the Testing process with public and local images dataset produces a high accuracy of 98.8% and 97% respectively.

Extreme learning machine algorithm‐based model for lung cancer classification from histopathological real‐time images

AbstractIn the present scenario, developing an automatic and credible diagnostic system to analyze lung cancer type, stage, and level from computed tomography (C.T.) images is a very challenging task, even for experienced pathologists, due to the nonuniform illumination and artifacts. The nonuniform illumination and artifacts are the low‐frequency changes in image intensity that arise from the sensor and the person's movement while recording the C.T. scanned images. Although numerous machine learning techniques are used to improve the effectiveness of automatic lung cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real‐time requirement of the diagnostic situations. A new extreme learning machine (ELM) algorithm‐based model (hereafter called XlmNet) is proposed to classify the histopathology scans effectively. XlmNet utilizes The Cancer Imaging Archive (TCIA) dataset. After data collection, the initial stage in XlmNet is preprocessing, including noise removal, histogram equalization, and quality‐improved image. The enhanced Profuse Clustering (EPC) method is implemented for segmenting the affected regions from C.T. scans by image segment using superpixel clustering. The statistical attributes are extracted by using Principal Component Analysis (PCA). ELM classifier helps in classifying the lung nodules. The empirical results of the XlmNet model are related to some advanced classifiers concerning performance metrics. The evaluations of XlmNet on the TCIA dataset reveal that XlmNet outperforms other classification networks with the Accuracy of 0.965, a sensitivity of 0.964, a specificity of 0.865, a precision of 0.962, a Jaccard similarity score (JSS) of 0.95.

Attributive Valence of the German verb GEHEN and the Russian Verb ИДТИ (Cognitive Aspect)

The aim of the work is to identify similarities and differences in the attributive valence of the German verb of movement "gehen" and the Russian verb "идти" in German and Russian. The subject of the study is the sectors of the manifestation of the attributive valence of the above-mentioned verbs in the cognitive aspect. These verbs are considered only in their direct sense, namely from the point of view of transmitting the expression of a person's movement from one point to another. The attributive valence of verbs of movement was chosen due to its insufficient study in comparative linguistics from the position of cognitive linguistics. In the study, the comparative method was used as the main one; methods of semantic and cognitive analysis were also used. The material of the analyzed examples was German and Russian lexicographic sources. The scientific novelty of this study lies in the fact that it reveals for the first time similar and specific cognitive sectors of the manifestation of the attributive valence of the German verb "gehen" and the Russian verb "идти". As a result of the study, the presence of such sectors of attributive valence as "direction of movement", "type of movement" and "pace of movement" was revealed in the verb "gehen". The analyzed examples of the Russian verb "идти" showed the presence of the following sectors: "time", "direction" and "type of movement", "accomplice of movement", "pace of movement" and mixed cases. As a result of comparative analysis, it was found that in German and Russian there are the following similar sectors of the attributive valence of human movement: "direction", "type" and "pace of movement", and the sectors "direction of movement" and "pace of movement" significantly prevail quantitatively in the analyzed examples. The article also draws conclusions about the peculiarity of the perception of the directional movement of a person in the Russian and German linguistic cultures.

Joint Kinematics, Kinetics and Muscle Synergy Patterns During Transitions Between Locomotion Modes.

There is an increasing demand for accurately predicting human movement intentions. To be effective, predictions must be performed as early as possible in the preceding step, though precisely how early has been studied relatively little; how and when a person's movement patterns in a transition step deviate from those in the preceding step must be clearly defined. In this study, we collected motion kinematics, kinetics and electromyography data from 9 able-bodied participants during 7 locomotion modes. Twelve types of steps between the 7 locomotion modes were studied, including 5 continuous steps (taking another step in the same locomotion mode) and 7 transitions steps (taking a step from one locomotion mode into another). For each joint degree of freedom, joint angles, angular velocities, moments, and moment rates were compared between continuous steps and transition steps, and the relative timing during the transition step at which these parameters diverged from those of a continuous step, which we refer to as transition starting times, were identified using multiple analyses of variance. Muscle synergies were also extracted for each step, and we studied in which locomotion modes these synergies were common (task-shared) and in which modes they were specific (task-specific). The transition starting times varied among different transitions and joint degrees of freedom. Most transitions started in the swing phase of the transition step. These findings can be applied to determine the critical timing at which a powered assistive device must adapt its control to enable safe and comfortable support to a user.

Recognizing human functional patterns in IADLs through hand detection and artificial intelligence.

Cognitive impairment is one of the characteristics shown by patients with Alzheimer's dementia (Montenegro et al., 2020; Kelley & Petersen, 2007). This affects the executive functions of most of these patients from the early stage (Guarino et al., 2019) and consequently, have difficulty in carrying out their daily activities (Dubbelman et al., 2020). These activities require precise coordination of the person's movements and interaction with their environment, so instrumental activities of daily living (IADLs) are predictors of such impairment (Barberger-Gateau & Dartigues, 1993), as several research studies have shown(Brovelli et al., 2017). Therefore, assessing the performance of IADLs by analyzing human biomechanical markers such as hand movements is of great interest. This, together with the application of artificial intelligence techniques, in particular deep learning, will allow the development of technological tools for the creation of an automated model to support the early diagnosis of Alzheimer's disease. This work was inspired by the development of Alejandro Acosta et al, where human biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern (Acosta-Franco et al., 2021). To improve the accuracy of the baseline model, we propose a hand detection algorithm based on a probabilistic approach considering skin color as a descriptor. To test the proposed algorithm, we used the dataset of egocentric videos containing the performance of IADL activities, which is organized into four classes, based on the prehensile patterns of the hands: force and precision, and on the kinematics of the instruments: displacement and manipulation. With the improvement made to the base model we obtained an accuracy higher than 74% in recognizing movement patterns of displacement and manipulation of objects, and a good prediction of the depth of the anatomical planes. Our improved model obtained a good percentage of recognition accuracy for the instrument's kinematics, which can help in the development of technological tools for the creation of an automated model to support the diagnosis of early Alzheimer's disease.

Multimodal spatiotemporal skeletal kinematic gait feature fusion for vision-based fall detection

Fall happens when a person's movement coordination is disturbed, forcing them to rest on the ground unintentionally causing serious health risks. The objective of this work is to develop a Multimodal SpatioTemporal Skeletal Kinematic Gait Feature Fusion (MSTSK-GFF) classifier for detecting fall using video data. The walking pattern of an individual is referred to as gait. The event of fall recorded in video shows discrepancies and irregularities in gait patterns. Analysis of these patterns plays a vital role in the identification of fall risk. However, assessment of the gait patterns from video data remains challenging due to its spatial and temporal feature dependencies. The proposed MSTSK-GFF framework presents a multimodal feature fusion process that overcomes these challenges and generates two sets of spatiotemporal kinematic gait features using SpatioTemporal Graph Convolution Network (STGCN) and 1D-CNN network model. These two generated feature sets are combined using concatenative feature fusion process and classification model is constucted for detecting fall. For optimizing the network weights, a bio-inspired spotted hyena optimizer is applied during training process. Finally, performance of the classification model is evaluated and compared to detect fall in videos. The proposed work is experimented with the two vision-based fall datasets namely, UR Fall Detection (URFD) dataset and self-build dataset. The experimental outcome proves the effectiveness of MSTSK-GFF in terms of its classification accuracy of 96.53% and 95.80% with two datasets when compared with existing state-of-the-art techniques.

Prior Experience of Physiotherapy Treatment Among the General Public

Background: Physiotherapy services continue to be an essential component in patient recovery throughout hospital care, from acute to subacute, palliative extended care and outpatient services. Physiotherapists are also concerned as their role in working with a physical or mental condition that limits a person's movement as they deal through active rehabilitation and health promotions. The physiotherapists gain experience through volunteer work with local senior care centre or local clinic. This study aims to find out the Prior experience of Physiotherapy treatment among General public. Methodology A qualitative study conducted with 105 willing participants aged more than 40 years, of both sexes who had a prior experience of physiotherapy treatment at least once in recent 2 years. A self-reported questionnaire was given and the responses recorded. Results It shows that Domain (D1-45, D2 -59, D4-53, D5-59, D6-61, D7-60, D8-57, D9-55) participants reported maximum responses towards agreeing, whereas Domain D3 received maximum responses of 46 participants towards disagreeing. Conclusion The study concluded that the Prior experience of Physiotherapy treatment among General public using a self-administered questionnaire showed more positive responses towards the Physiotherapy treatment and Physiotherapists.

УПРАВЛЕНИЕ УЧЕБНО-ПОЗНАВАТЕЛЬНОЙ ДЕЯТЕЛЬНОСТЬЮ ОБУЧАЮЩИХСЯ ПО ДИСЦИПЛИНЕ «ФИЗИЧЕСКАЯ КУЛЬТУРА И СПОРТ» В УСЛОВИЯХ ИНФОРМАЦИОННО-ОБРАЗОВАТЕЛЬНОЙ СРЕДЫ ВУЗА

Statement of the problem. The article states that the effectiveness of cognitive activity management of participants in the educational process directly depends on the degree of dissemination of the latest methods and techniques of knowledge transfer, through the introduction of technical and technological innovations, the quality of electronic educational resources and the readiness of scientific and pedagogical staff to carry out professional activities in a developed information society. This also proves that the improvement of the educational process in institutions of higher education in physical culture and sports is based on the latest ways of interaction of subjects in the educational process within the information and educational environment of higher education in physical culture and sports. The purpose of the article is to study the mechanisms of interaction between the subjects of educational activity in the information and educational environment of higher education in physical culture and sports in the process of managing students’ cognitive activity. Research methodology includes a systematic approach to the study of the process of managing the cognitive activity of students, theoretical analysis, systematization and generalization of scientific and pedagogical literature and advanced pedagogical experience, and modeling. Research results. It is determined that sports and pedagogical information is distributed in a higher education institution in digital form, and its characteristic features include a relation between physical culture and sports activities, a focus on highlighting aspects of the development of physical culture and sports, concentration on physical abilities of a person in motion. A schematic model of knowledge translation has been developed, the process of managing students’ cognitive activity within the information and educational environment of higher education has been studied and the conditions for effective management of students’ cognitive activity have been determined. Conclusions. The study showed that within a university, the creation and development of the electronic information and educational environment (EIEE) in Physical Culture and Sports is aimed at ensuring continuous training of students in a developed information society, taking into account the specifics of the content of education and the contingent involved in competitive training activities. It is established that, depending on the physical presence of the student in the classroom, the ways of interaction between the subjects of educational activity are being modernized. Free access to knowledge outside the University’s EIEE in Physical Culture and Sports on the territory of the information and educational space opens up opportunities for participants of the educational process to expand and deepen the updated and generated knowledge.

A robust optimized convolutional neural network model for human activity recognition using sensing devices

AbstractHuman activity recognition has become an expansive field of interest in recent years, both in academic and industrial research. Human Activity recognition (HAR) is concerning the prediction of person's movement or action such as walking, standing, sitting, up and downstairs, etc. Convolutional neural network (CNN) is a key component with deep learning. The main objective of this study is to design and implement an activity recognition algorithm in the state of the art of deep learning systems which accomplish superlative performance to detect human activities. Accordingly, an extensive comparison has been developed between different deep learning algorithms such as classical (CNN) models and Recurrent Neural Network (RNN) models with respect to the major human activities. Furthermore, a robust (CNN) deep learning model has been built up and proposed in order to enhance the recognition precision of human activities. This proposed model uses raw data acquired from a set of inertial sensor and exploring numerous human achas been built up and proposedtivities; sitting, standing, jogging, walking, and etc. Experimental results show that the precision of the proposed deep structure has achieved 97.5% with respect to the NAdam optimizer which would be considered as the most effectively recognizer compared to other deep learning architectures.