Application of a Validated Innovative Smart Wearable for Performance Analysis by Experienced and Non-Experienced Athletes in Boxing

An athlete’s sporting performance depends to a large extent on the technical execution of the athletic motion in order to achieve maximum effectiveness in physical performance. Performance analysis provides an important means of classifying and quantifying athletic prowess in terms of the significant performance aspects of the sport to provide objective feedback. This study aimed to analyze technical execution in terms of punch trajectory, force, velocity and time, considering the expert-novice paradigm by investigating the technical execution of 31 experienced and non-experienced athletes for the four main punching techniques of the cross, jab, uppercut and hook strike. The kinetic and kinematic data were collected by means of a boxing monitoring system developed and validated for in-field use. The research revealed significant correlation for executed punching trajectory and punch force in intragroup comparison and significant differences in intergroup comparison. No significant differences were detected for punch velocity in either inter- or intra-group paradigms. This study, through use of the sensor system, aligns with the results of existing publications conducted in laboratory conditions, in the assessment of punch force, punch speed and punch time and thus extends the state of research by use of a smart wearable in field method.

Comparing personal and social optimism biases: magnitude, overlap, modifiability, and links with social identification and expertise

Individuals are more optimistic about their own future than a comparable person’s future (personal optimism bias). In addition, they show overoptimism toward people or social groups they identify with compared with those they do not identify with (social optimism bias). However, commonalities and differences between personal and social forms of optimism bias remain to be addressed. Data from an experiment on anticipated performances in soccer (including 160 participants), revealed (a) comparable magnitudes of personal and social optimism biases, and (b) only partial overlap between personal and social optimism biases. We further found the magnitude of the biases to depend on (c) prior experience in the investigated area. Social optimism bias, however, did not correlate with (d) the extent to which the participants identified with a social in-group. In addition, we demonstrate that (e) despite the availability of objective feedback, both personal and social optimism biases are hard to overcome. Our data further suggest (f) the existence of qualitatively different social optimism biases; biases that can possibly be distinguished by their degree of automaticity or the adoption of a more affective vs. utilitarian stance. Consequently, the present research reveals that the phenomenon of social optimism bias needs further refinement to adequately address its specific sub-components.

Quality Indicators for Avalanche Victim Management and Rescue

Decisions in the management and rescue of avalanche victims are complex and must be made in difficult, sometimes dangerous, environments. Our goal was to identify indicators for quality measurement in the management and rescue of avalanche victims. The International Commission for Mountain Emergency Medicine (ICAR MedCom) convened a group of internal and external experts. We used brainstorming and a five-round modified nominal group technique to identify the most relevant quality indicators (QIs) according to the National Quality Forum Measure Evaluation Criteria. Using a consensus process, we identified a set of 23 QIs to measure the quality of the management and rescue of avalanche victims. These QIs may be a valuable tool for continuous quality improvement. They allow objective feedback to rescuers regarding clinical performance and identify areas that should be the foci of further quality improvement efforts in avalanche rescue.

Case Study on a Potential Application of Failure Mode and Effects Analysis in Assessing Compliance Risks

Assessing and reducing compliance risks can now be considered one of the core criteria for business success. While failure mode and effect analysis (FMEA) is widely used in engineering, its application in the financial sector is quite novel, primarily related to compliance risk assessment. This paper presents the results of exploratory research based on the potential application of FMEA in a focus group of compliance experts at one of the largest Central and Eastern European commercial banks. This study aims to establish a process for assessing compliance risks that builds on the strengths of both the qualitative and quantitative assessment methods. Applying FMEA based on a nominal group technique and further statistical analysis provides an opportunity to compare expert assessments and the consensus level of the participants. As a result, the similarity or difference of the assessment patterns can be quantified, providing objective feedback on the evaluation. Finally, this paper proposes lifting the detectability of failures as an evaluation dimension to the same level of importance as the probability and impact of non-compliance and using agreement testing statistical methods.

A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

Many psychological treatments have been shown to be cost-effective and efficacious, as long as they are implemented faithfully. Assessing fidelity and providing feedback is expensive and time-consuming. Machine learning has been used to assess treatment fidelity, but the reliability and generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback.

Online and Distance Learning (ODL) and Hybrid Learning in COVID-19 era: The Effects of Pandemic to Undergraduate Students

Undergraduate students are facing challenges during COVID-19 pandemic where teaching and learning (T&L) are completely migrated to online and distance learning (ODL). Most of the countries worldwide are under lockdown. Regardless of the shock faced, education is one of the main sector where it is important and can be continued to operate completely online, and perhaps combined with face-to-face (F2F) into Hybrid Learning when the pandemic rate is going under control. However, many feedback received from the students especially from various sources including students themselves, parents, and educators on real situation in ODL. Thus, this study is taking an initiative to gather systematic and objective feedback on three main factors; Autonomy, Competence and Relatedness. The undergraduate students of the higher institutions of learning in southern state of Malaysia are the respondents in this study consists of 321 students from multiple faculties representing different fields of studies. As a result, the factors under investigation had effects on students’ motivation, interaction, communication, and online accessibility in continue enrolling to pursue their study using ODL and Hybrid Learning. Learning environment and preferences can be further explored to support the students in a more comprehensive manner in post-COVID-19 pandemic.

Effects of person-organization fit objective feedback and subjective perception on organizational attractiveness in online recruitment

PurposeThis study applies a person-environment fit (PEF) framework to examine the extent to which organizational attractiveness may be influenced by person-organization fit (POF) feedback and person-job fit (PJF) feedback in web-based recruitment. Furthermore, the potential mediating roles of subjective POF and subjective PJF perceptions were examined.Design/methodology/approachSenior undergraduate business administration students participated in a two-stage experiment by completing a paper-and-pencil survey during a campus career fair and then reviewing a recruitment website.FindingsResearch findings showed that online assessment feedback on PJF was positively related to organizational attractiveness. The higher the level PJF, the more organizational attractiveness participants reported. Second, both POF and PJF feedback information can affect organizational attractiveness indirectly through subjective POF and PJF perceptions, respectively. Fresh graduates were more sensitive to PJF feedback in deciding organizational attractiveness.Originality/valueThis study contributes to the recruitment literature in at least three ways. First, online recruitment messages concerning can affect organizational attractiveness. Second, in support of the PEF framework, fresh graduates can distinguish subjective POF perceptions from subjective PJF perceptions. Third, fresh graduates are more sensitive to PJF information and perceptions in deciding organizational attractiveness.

Enhancement of a low-fidelity surgical simulator. Is it possible?

Background Simulation training has become a core component in the training of ENT surgeons. It provides the opportunity for the repetitive practice of a surgical technique. Simulators are broadly categorised into low- and high-fidelity simulators. A method using a home microprocessor to enhance a low-fidelity surgical simulator is introduced. Method The Yorick tonsil tie trainer was enhanced using an Arduino microcontroller attached to the simulated inferior pole of the tonsil. The Arduino was coded to give a visual stimulus when linear motion exceeded parameters. The prototype simulator was tested to gain information on whether the enhancement could identify differences between novice and expert users. Conclusion An enhanced low-fidelity tonsil trainer was produced using a low-cost, simple home microprocessing board. The enhanced simulator gives objective feedback allowing for self-directed learning. Further research is required to evaluate the benefits of these enhancements above non-enhanced simulation training.

Video Feedback and Video Modeling in Teaching Laparoscopic Surgery: A Visionary Concept from Kiel

Learning curves for endoscopic surgery are long and flat. Various techniques and methods are now available for surgical endoscopic training, such as pelvitrainers, virtual trainers, and body donor surgery. Video modeling and video feedback are commonly used in professional training. We report, for the first time, the application of video modeling and video feedback for endoscopic training in gynecology. The purpose is to present an innovative method of training. Attendees (residents and specialists) of minimally invasive surgery courses were asked to perform specific tasks, which were video recorded in a multimodular concept. Feedback was given later by an expert at a joint meeting. The attendees were asked to fill a questionnaire in order to assess video feedback given by the expert. The advantages of video feedback and video modeling for the development of surgical skills were given a high rating (median 84%, interquartile ranges (IQR) 72.5–97.5%, n = 37). The question as to whether the attendees would recommend such training was also answered very positively (median 100%, IQR 89.5–100%, n = 37). We noted a clear difference between subjective perception and objective feedback (58%, IQR 40.5–76%, n = 37). Video feedback and video modeling are easy to implement in surgical training setups, and help trainees at all levels of education.

LAPKaans: Tool-Motion Tracking and Gripping Force-Sensing Modular Smart Laparoscopic Training System

Laparoscopic surgery demands highly skilled surgeons. Traditionally, a surgeon’s knowledge is acquired by operating under a mentor-trainee method. In recent years, laparoscopic simulators have gained ground as tools in skill acquisition. Despite the wide range of laparoscopic simulators available, few provide objective feedback to the trainee. Those systems with quantitative feedback tend to be high-end solutions with limited availability due to cost. A modular smart trainer was developed, combining tool-tracking and force-using employing commercially available sensors. Additionally, a force training system based on polydimethylsiloxane (PDMS) phantoms for sample stiffness differentiation is presented. This prototype was tested with 39 subjects, between novices (13), intermediates (13), and experts (13), evaluating execution differences among groups in training exercises. The estimated cost is USD $200 (components only), not including laparoscopic instruments. The motion system was tested for noise reduction and position validation with a mean error of 0.94 mm. Grasping force approximation showed a correlation of 0.9975. Furthermore, differences in phantoms stiffness effectively reflected user manipulation. Subject groups showed significant differences in execution time, accumulated distance, and mean and maximum applied grasping force. Accurate information was obtained regarding motion and force. The developed force-sensing tool can easily be transferred to a clinical setting. Further work will consist on a validation of the simulator on a wider range of tasks and a larger sample of volunteers.