Development of Virtual Laboratory Through Hand Motion Detector in Order to Improve Psychomotor Skills Student of Vocational High School

Abstract: The students interact directly in virtual laboratory with a simulator or remote equipment, and it is desirable that the experience will be similar to a real lab. There are many ways by which a student could attain this experience -through real experimental activities or through computer human interactions. These computer based multimedia environment and cohesive with hardware. These environments offer students a means to explore, experience, express themselves, and train psychomotoric. In a Digital Electronics virtual environment, the students can posit hypotheses about a engineering concept, conduct as many experiments as they want. In this paper the virtual laboratory design based on macromedia flash (software) and hand movement detection (hardware). Have implemented a virtual lab for the user especially vocational students (SMK), making practices more interesting and interactive through user interaction with computer using a periferal with hand movement detection that provides flexibility in operating. Combination of real and virtual lab which is integrated into the course material, can enrich the learning process, increase student’s interest and curiosity, enhance the ability of psychomotor with hands-on

End-user action-sound mapping design for mid-air music performance

How to design the relationship between a performer’s actions and an instrument’s sound response has been a consistent theme in Digital Musical Instrument (DMI) research. Previously, mapping was seen purely as an activity for DMI creators, but more recent work has exposed mapping design to DMI musicians, with many in the field introducing soware to facilitate end-user mapping, democratising this aspect of the DMI design process. This end-user mapping process provides musicians with a novel avenue for creative expression, and offers a unique opportunity to examine how practising musicians approach mapping design.Most DMIs suffer from a lack of practitioners beyond their initial designer, and there are few that are used by professional musicians over extended periods. The Mi.Mu Gloves are one of the few examples of a DMI that is used by a dedicated group of practising musicians, many of whom use the instrument in their professional practice, with a significant aspect of creative practice with the gloves being end-user mapping design. The research presented in this dissertation investigates end-user mapping practice with the Mi.Mu Gloves, and what influences glove musicians’ design decisions based on the context of their music performance practice, examining the question: How do end-users of a glove-based mid-air DMI design action–sound mapping strategies for musical performance?In the first study, the mapping practice of existing members of the Mi.Mu Glove community is examined. Glove musicians performed a mapping design task, which revealed marked differences in the mapping designs of expert and novice glove musicians, with novices designing mappings that evoked conceptual metaphors of spatial relationships between movement and music, while more experienced musicians focused on designing ergonomic mappings that minimised performer error.The second study examined the initial development period of glove mapping practice. A group of novice glove musicians were tracked in a longitudinal study. The findings supported the previous observation that novices designed mappings using established conceptual metaphors, and revealed that transparency and the audience’s ability to perceive their mappings was important to novice glove musicians. However, creative mapping was hindered by system reliability and the novices’ poorly trained posture recognition.The third study examined the mapping practice of expert glove musicians, who took part in a series of interviews. Findings from this study supported earlier observations that expert glove musicians focus on error minimisation and ergonomic, simple controls, but also revealed that the expert musicians embellished these simple controls with performative ancillary gestures to communicate aesthetic meaning. The expert musicians also suffered from system reliability, and had developed a series of gestural techniques to mitigate accidental triggering.The fourth study examined the effects of system-related error in depth. A laboratory study was used to investigate how system-related errors impacted a musician’s ability to acquire skill with the gloves, finding that a 5% rate of system error had a significant effect on skill acquisition.Learning from these findings, a series of design heuristics are presented, applicable for use in the fields of DMI design, mid-air interaction design and end-user mapping design

Design and Analysis of the Virtual Reality Welding Training

A thesis presented to the faculty of the College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Ritesh Chakradhar on November 19, 2021

Assessment of Hand Gestures Using Wearable Sensors and Fuzzy Logic

Hand dexterity and motor control are critical in our everyday lives because a significant portion of the daily motions we perform are with our hands and require some degree of repetition and skill. Therefore, development of technologies for hand and extremity rehabilitation is a significant area of research that will directly help patients recovering from hand debilities sustained from causes ranging from stroke and Parkinson’s disease to trauma and common injuries. Cyclic activity recognition and assessment is appropriate for hand and extremity rehabilitation because a majority of our essential motions are cyclic in their nature. For a patient on the road to regaining functional independence with daily skills, the improvement in cyclic motions constitutes an important and quantifiable rehabilitation goal. However, challenges exist with hand rehabilitation sensor technologies preventing acquisition of long-term, continuous, accurate and actionable motion data. These challenges include complicated and uncomfortable system assemblies, and a lack of integration with consumer electronics for easy readout. In our research, we have developed a glove based system where the inertial measurement unit (IMU) sensors are used synergistically with the flexible sensors to minimize the number of IMU sensors. The classification capability of our system is improved by utilizing a fuzzy logic data analysis algorithm. We tested a total of 25 different subjects using a glove-based apparatus to gather data on two-dimensional motions with one accelerometer and three-dimensional motions with one accelerometer and two flexible sensors. Our research provides an approach that has the potential to utilize both activity recognition and activity assessment using simple sensor systems to help patients recover and improve their overall quality of life

South African sign language dataset development and translation : a glove-based approach

Includes bibliographical references.There has been a definite breakdown of communication between the hearing and the Deaf communities. This communication gap drastically effects many facets of a Deaf person’s life, including education, job opportunities and quality of life. Researchers have turned to technology in order to remedy this issue using Automatic Sign Language. While there has been successful research around the world, this is not possible in South Africa as there is no South African Sign Language (SASL) database available. This research aims to develop a SASL static gesture database using a data glove as the first step towards developing a comprehensive database that encapsulates the entire language. Unfortunately commercial data gloves are expensive and so as part of this research, a low-cost data glove will be developed for the application of Automatic Sign Language Translation. The database and data glove will be used together with Neural Networks to perform gesture classification. This will be done in order to evaluate the gesture data collected for the database. This research project has been broken down into three main sections; data glove development, database creation and gesture classification. The data glove was developed by critically reviewing the relevant literature, testing the sensors and then evaluating the overall glove for repeatability and reliability. The final data glove prototype was constructed and five participants were used to collect 31 different static gestures in three different scenarios, which range from isolated gesture collection to continuous data collection. This data was cleaned and used to train a neural network for the purpose of classification. Several training algorithms were chosen and compared to see which attained the highest classification accuracy. The data glove performed well and achieved results superior to some research and on par with other researchers’ results. The data glove achieved a repeatable angle range of 3.27 degrees resolution with a standard deviation of 1.418 degrees. This result is far below the specified 15 degrees resolution required for the research. The device remained low-cost and was more than $100 cheaper than other custom research data gloves and hundreds of dollars cheaper than commercial data gloves. A database was created using five participants and 1550 type 1 gestures, 465 type 2 gestures and 93 type 3 gestures were collected. The Resilient Back-Propagation and Levenberg-Marquardt training algorithms were considered as the training algorithms for the neural network. The Levenberg-Marquardt algorithm had a superior classification accuracy achieving 99.61%, 77.42% and 81.72% accuracy on the type 1, type 2 and type 3 data respectively

ADEPT:Exploring the Design, Pedagogy, and Analysis of a Mixed Reality Application for Piano Training

(Abstract to follow