The Perceptual Consistency and Association of the LMA Effort Elements

Laban Movement Analysis (LMA) and its Effort element provide a conceptual framework through which we can observe, describe, and interpret the intention of movement. Effort attributes provide a link between how people move and how their movement communicates to others. It is crucial to investigate the perceptual characteristics of Effort to validate whether it can serve as an effective framework to support a wide range of applications in animation and robotics that require a system for creating or perceiving expressive variation in motion. To this end, we first constructed an Effort motion database of short video clips of five different motions: walk, sit down, pass, put, wave performed in eight ways corresponding to the extremes of the Effort elements. We then performed a perceptual evaluation to examine the perceptual consistency and perceived associations among Effort elements: Space (Indirect/Direct), Time (Sustained/Sudden), Weight (Light/Strong), and Flow (Free/Bound) that appeared in the motion stimuli. The results of the perceptual consistency evaluation indicate that although the observers do not perceive the LMA Effort element 100% as intended, true response rates of seven Effort elements are higher than false response rates except for light Effort. The perceptual consistency results showed varying tendencies by motion. The perceptual association between LMA Effort elements showed that a single LMA Effort element tends to co-occur with the elements of other factors, showing significant correlation with one or two factors (e.g., indirect and free, light and free).

The effects of visual formats on Facebook health-related posts: evidence from eye movement analysis

PurposeThis study aimed to provide user-centered evidence for health professionals to make optimal use of images for the effective dissemination of health information on Facebook (FB).Design/methodology/approachUsing an eye-tracking experiment and a survey method, this study examined 42 participants' reading patterns as well as recall and recognition outcomes with 36 FB health information posts having various FB post features.FindingsThe findings demonstrated that FB posts with text-embedded images received more attention and resulted in the highest recall and recognition. Meanwhile, compared to text-embedded images, visual only images yielded less effective recall of information, but they caught the viewers' attention; graphics tended to attract more attention than photos. For effective communication, the text features in FB posts should align with the formats of the images.Practical implicationsThe findings of this study provide practical implications for health information disseminators by suggesting that text-embedded images should be used for effective health communication.Originality/valueThis study provided evidence of users' different viewing patterns for FB health information posts and the relationship between FB post types and recall and recognition outcomes.

Neuroinflammation and Behavioral Deficit in Rotenone-Induced Neurotoxicity in Rats and the Possible Effects of Butanolic Extract of Centaurea Africana

Background: Many studies have used rotenone (ROT) to create an experimental animal model of Parkinson's disease (PD) because of its ability to induce similar behavioral and motor deficits. PD is the most common age-related motoric neurodegenerative disorder. Neuroinflammation and apoptosis play an important role in the pathogenesis of this disease. Objective: This study investigated the effect of butanolic (n-BuOH) extract of Centaurea africana (200 mg/kg, 16 days) on a ROT-induced neurotoxicity model in male Wistar albino rats. Methods: Estimation of Tumor Necrosis Factor (TNF-α) and Nitric Oxide (NO) levels along with the myeloperoxidase (MPO) activity in brains was carried out in order to evaluate neuro-inflammation. Oxidative stress, Caspase 3 activity (apoptosis), and behavioral alterations were also evaluated. Results: In behavior assessment, using Ludolph Movement Analysis Scale, all ROT treated animals showed a decreased locomotor activity. The mitochondrial dysfunction induced by ROT was expressed by a decreased activity of complex I of the mitochondrial respiratory chain and increased lipid peroxidation and caspase 3. Co-treatment with the n-BuOH extract significantly restored the activity of complex I (65.41%) compared to treatment with ROT alone. The n-BuOH extract also reduced the neuroinflammation in rat brains by reducing MPO activity (75.12%), NO levels (77.43%), and TNF-α (71.48%) compared to the group treated with ROT. Conclusion: The obtained results indicated that C. africana n-BuOH extract exhibited a protective effect in rats.

Analysis and Research on Human Movement in Sports Scene

As a whole-body sport, skipping rope plays an increasingly important role in daily life. In rope-skipping education, due to the lack of professional teachers, the training efficiency of students is low. The rope-skipping monitoring device is heavy and expensive, and the cost of labor statistics and energy consumption are high. In order to quickly analyze the movement process of students and provide correct guidance, this article implements the movement analysis method of the human body movement process. The problem of limb posture analysis in rope skipping is transformed into a multilabel classification problem, a real-time human motion analysis method based on mobile vision is proposed, and the algorithm model is verified in the rope-skipping scene. The experimental results prove that this paper proposes the improved algorithm, which achieved the expected effect. In the analysis of rope-skipping action, the choice of hyperparameters during the experiment is introduced, and it is verified that the proposed ALSTM-LSTM can solve the problem of multilabel classification in the rope-skipping process. The accuracy rate reaches 95.1%, and it can provide the best in all indicators and good performance. It is of great significance for movement analysis and movement quality evaluation during exercise.

Affective Computing for Late-Life Mood and Cognitive Disorders

Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.

I Want to Control Your Move: Human-Human Interface (HHI) Neuromuscular Electrical Stimulator (NMES)

Neuromuscular electrical stimulation (NMES) has been widely used in rehabilitation hubs to restore or replace the motor function of individuals who have upper neuron damage such as stroke and spinal cord injury. However, the utilization of sensors in NMES is limited and results in the lack of data for upper limb movement analysis. The proposed system implemented NMES integrated with human-to-human interface (HHI) in the rehabilitation process for stroke patients. The therapist (controller) can coach the motion of patients (subject) by injecting his own signal for patients to follow. Ten (10) subjects were tested with five (5) repeating trials. The EMG value was extracted from the finger flexion and extension at the controller side, then injected into the control unit for further stimulation of the subject. In order to evaluate the repeating motion by the subject, an accelerometer was attached to the finger. Performance evaluation of the subject was executed by comparing the flexion angle with the controller side. The result showed that the error of the system was less than 10.29 % for the first trial and gradually reduced to 1 % after 5 trials.

Research on Human Motion Analysis in Moving Scene Based on Timing Detection and Video Description Algorithm

Technical movement analysis requires specialized domain knowledge and processing a large amount of data, and the advantages of AI in processing data can improve the efficiency of data analysis. In this paper, we propose a feature pyramid network-based temporal action detection (FPN-TAD) algorithm, which is used to solve the problem that the action proposal module has a low recall rate for small-scale temporal target action regions in the current video temporal action detection algorithm research. This paper is divided into three parts. The first part is an overview of the algorithm; the second part elaborates the network structure and the working principle of the FPN-TAD algorithm; and the third part gives the experimental results and analysis of the algorithm.

Biomechanical Loads and Their Effects on Player Performance in NCAA D-I Male Basketball Games

Basketball games and training sessions are characterized by quick actions and many scoring attempts, which pose biomechanical loads on the bodies of the players. Inertial Measurement Units (IMUs) capture these biomechanical loads as PlayerLoad and Inertial Movement Analysis (IMA) and teams collect those data to monitor adaptations to training schedules. However, the association of biomechanical loads with game performance is a relatively unexplored area. The aims of the current study were to determine the statistical relations between biomechanical loads in games and training with game performance. Biomechanical training and game load measures and player-level and team-level game stats from one college basketball team of two seasons were included in the dataset. The training loads were obtained on the days before gameday. A three-step analysis pipeline modeled: (i) relations between team-level game stats and the win/loss probabilities of the team, (ii) associations between the player-level training and game loads and their game stats, and (iii) associations between player-level training loads and game loads. The results showed that offensive and defensive game stats increased the odds of winning, but several stats were subject to positional and individual performance variability. Further analyses, therefore, included total points [PTS], two-point field goals, and defensive rebounds (DEF REB) that were less subject to those influences. Increases in game loads were significantly associated with game stats. In addition, training loads significantly affected the game loads in the following game. In particular, increased loads 2 days before the game resulted in increased expected game loads. Those findings suggested that biomechanical loads were good predictors for game performance. Specifically, the game loads were good predictors for game stats, and training loads 2 days before gameday were good predictors for the expected game load. The current analyses accounted for the variation in loads of players and stats that enabled modeling the expected game performance for each individual. Coaches, trainers, and sports scientists can use these findings to further optimize training plans and possibly make in-game decisions for individual player performance.