Seeing Signs: On the appearance of manual movements in gestures

This dissertation presents the results of a series of studies on the appearance of manual movements in gestures. The main goal of this research is to increase our understanding of how humans perceive signs and other gestures. Generated insights from human perception may aid the development of technology for recognizing gestures and sign language automatically with cameras and computers. One example of an application of automatic gesture recognition that has played a role in shaping the research in this dissertation is ELo, an Electronic Learning environment for deaf and hearing impaired children to practice Sign Language of the Netherlands (SLN) signs. The questions addressed in the research focus on a number of aspects including temporal processing of signs, discrimination of gestures from other human behaviour, and how humans handle variation in signs.Industrial DesignIndustrial Design Engineerin

Sign language perception research for improving automatic sign language recognition

Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of data-driven ASLR methods has shortcomings which may not be solvable without the use of knowledge on human sign language processing. Handling variation in the precise execution of signs is an example of such shortcomings: data-driven methods (which include almost all current methods) have difficulty recognizing signs that deviate too much from the examples that were used to train the method. Insight into human sign processing is needed to solve these problems. Perceptual research on sign language can provide such insights. This paper discusses knowledge derived from a set of sign perception experiments, and the application of such knowledge in ASLR. Among the findings are the facts that not all phases and elements of a sign are equally informative, that defining the ‘correct’ form for a sign is not trivial, and that statistical ASLR methods do not necessarily arrive at sign representations that resemble those of human beings. Apparently, current ASLR methods are quite different from human observers: their method of learning gives them different sign definitions, they regard each moment and element of a sign as equally important and they employ a single definition of ‘correct’ for all circumstances. If the object is for an ASLR method to handle natural sign language, then the insights from sign perception research must be integrated into ASLR.Human Information Communication DesignIndustrial Design Engineerin

Teleworking during COVID-19 in the Netherlands: Understanding behaviour, attitudes, and future intentions of train travellers

With the arrival of COVID-19 in the Netherlands in Spring 2020 and the start of the “intelligent lockdown”, daily life changed drastically. The working population was urged to telework as much as possible. However, not everyone had a suitable job for teleworking or liked teleworking. From a mobility perspective, teleworking was considered a suitable means to alleviate travel. Even after the pandemic it can (continue to) reduce pressure on the mobility system during peak hours, thereby improving efficiency and level of service of transport services. Additionally, this could reduce transport externalities, such as emissions and unsafety. The structural impact from teleworking offers opportunities, but also challenges for the planning and operations of public transport. The aim of this study is to better understand teleworking during and after COVID-19 among train travellers, to support operators and authorities in their policy making and design. We study the telework behaviour, attitude towards teleworking, and future intentions through a longitudinal data collection. By applying a latent class cluster analysis, we identified six types of teleworkers, varying in their frequency of teleworking, attitude towards teleworking, intentions to the future, socio-demographics and employer policy. In terms of willingness-to-telework in the future, we distinguish three groups: the high willingness-to-telework group (71%), the low willingness-to-telework group (16%), and the least-impacted self-employed (12%). Those with high willingness are expected to have lasting changes in their travel patterns, where especially public transport is impacted. For this group, policy is required to ensure when (which days) and where (geographical) telework takes place, such that public transport operators can better plan and operate their services. For those with low willingness, it is essential that the government provides tools to companies (especially in education and vital sector) such that they can be better prepared for teleworking (mostly during but also after the pandemic). Employers on the other hand need to better support their employees, such that they stay in contact with colleagues and their concentration and productivity can increase.Transport and Plannin