Assessment of precautionary measures against COVID-19 in Indonesian workplaces

As the coronavirus disease 19 (COVID-19) pandemic spread across Indonesia, the precautionary measures have been taken by nearly all stakeholders, including industries and employers. The purpose of this study is to assess the measures took against COVID-19 pandemic in the workplaces across the country. This research involved participants from 103 different companies using a questionnaire adapted from Better Work Indonesia (BWI), occupational safety and health association (OSHA) and centers for disease control and prevention (CDC) COVID-19 guidelines. The data analysis conducted with descriptive statistics that resulted with the following key findings. Indonesian companies have taken precautionary measures against COVID-19 at their workplace. Most aspects of COVID-19 preventive measures: policy and procedure, operational aspect, health and safety, and external activities have been observed by the vast majority of the subjects. This study found that among 30 COVID-19 programs, the emergency exercise at work was the only one that had not been conducted by more than half of respondents. As the number of active cases still growing at the time this study conducted, all precautionary measures against COVID-19 shall be carried out with consistency by the company management)

Automated badminton smash recognition using convolutional neural network on the vision based data

Sport performance analysis in sports practice cannot be separable. It is important to help coach analyse and improve the performance of their athletes through training or game session. Due to the advancement of technology nowadays, the notational analysis of the video content using various software packages has become possible. Unluckily, the coach needs to recognize the actions manually before doing further analysis. The purpose of this study is to formulate an automated system for badminton smash recognition on widely available broadcasted videos using pre-trained Convolutional Neural Network (CNN) method. Smash and other badminton actions such as clear, drop, lift and net from the video were used to formulate the CNN models. Therefore, two experiments were conducted in this study. The first experiment is the study on the performance between four different existing pre-trained models which is AlexNet, GoogleNet, Vgg-16 Net and Vgg-19 Net in recognizing five actions. The results show that the pre-trained AlexNet model has the highest performance accuracy and fastest training period among the other models. The second experiment is the study on the performance of two different pre-trained models which is AlexNet and GoogleNet to recognize smash and non-smash action only. The results show that the pre-trained GoogleNet model produces the best performance in recognizing smash action. In conclusion, pre-trained AlexNet model is suitable to be used to automatically recognize the five badminton actions while GoogleNet model is excellent at recognizing smash action from the broadcasted video for further notational analysis

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Towards a Sustainable Life: Smart and Green Design in Buildings and Community

This Special Issue includes contributions about occupants’ sustainable living in buildings and communities, highlighting issues surrounding the sustainable development of our environments and lives by emphasizing smart and green design perspectives. This Special Issue specifically focuses on research and case studies that develop promising methods for the sustainable development of our environment and identify factors critical to the application of a sustainable paradigm for quality of life from a user-oriented perspective. After a rigorous review of the submissions by experts, fourteen articles concerning sustainable living and development are published in this Special Issue, written by authors sharing their expertise and approaches to the concept and application of sustainability in their fields. The fourteen contributions to this special issue can be categorized into four groups, depending on the issues that they address. All the proposed methods, models, and applications in these studies contribute to the current understanding of the adoption of the sustainability paradigm and are likely to inspire further research addressing the challenges of constructing sustainable buildings and communities resulting in a sustainable life for all of society

Training for Optimal Sports Performance and Health

In this book, the emphasis is on various training interventions. Types of exercises that can help improve performance in athletes and health in people facing poor movement diseases.Also, we have presented a variety of strength training interventions in the form of various types of research. On the other hand, we continue to monitor internal and external loads related to non-contact injuries and performance analysis

A Fire Drill Training System Based on VR and Kinect Somatosensory Technologies

The traditional fire drill is difficult, high cost, poor effect etc. in variety of fire scene simulation; a new fire drill platform combined Somatosensory camera Kinect and VR can achieve self-construction and virtual simulation fire drill based on somatosensory interaction; the platform achieves interaction between human and the 3D objects in virtual simulation environment, 3D modeling, scene building with Unity, identified human skeletal by Kinect, and using Euclidean distance matching recognition algorithm for body movements, and finally realizes the evaluation of fire drills.; the platform shows that the system has important practical significance and use value in fire drills, fire safety teaching and other aspects