▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

Gender differences in health: a Canadian study of the psychosocial, structural and behavioural determinants of health.

Equity of access to health care services: theory and evidence from the UK.

Sign Language Recognition (SLR) has been an active research field for the last two decades. However, most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language. In contrast, we introduce the Sign Language Translation (SLT) problem. Here, the objective is to generate spoken language translations from sign language videos, taking into account the different word orders and grammar. We formalize SLT in the framework of Neural Machine Translation (NMT) for both end-to-end and pretrained settings (using expert knowledge). This allows us to jointly learn the spatial representations, the underlying language model, and the mapping between sign and spoken language. To evaluate the performance of Neural SLT, we collected the first publicly available Continuous SLT dataset, RWTH-PHOENIX-Weather 2014T1. It provides spoken language translations and gloss level annotations for German Sign Language videos of weather broadcasts. Our dataset contains over .95M frames with >67K signs from a sign vocabulary of >1K and >99K words from a German vocabulary of >2.8K. We report quantitative and qualitative results for various SLT setups to underpin future research in this newly established field. The upper bound for translation performance is calculated at 19.26 BLEU-4, while our end-to-end frame-level and gloss-level tokenization networks were able to achieve 9.58 and 18.13 respectively.

/pdf/neural-sign-language-translation-w7h7ogbjwr.pdf

Neural Sign Language Translation

This review examines sex differences in health and survival, with a focus on the Nordic countries. There is a remarkable discrepancy between the health and survival of the sexes: men are physically stronger and have fewer disabilities, but have substantially higher mortality at all ages compared with women: the so-called male-female health-survival paradox. A number of proposed explanations for this paradox are rooted in biological, social, and psychological interpretations. It is likely to be due to multiple causes that include fundamental biological differences between the sexes such as genetic factors, immune system responses, hormones, and disease patterns. Behavioral differences such as risk-taking and reluctance to seek and comply with medical treatment may also play a role. Another consideration is that part of the difference may be due to methodological challenges, such as selective non-participation and under-reporting of health problems, and delayed seeking of treatment by men. The Nordic countries provide a unique opportunity for such studies, as they have good-quality data in their national health registers, which cover the whole population, and a long tradition of high participation rates in surveys.

Men: good health and high mortality. Sex differences in health and aging*

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a precis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

/pdf/sign-language-recognition-4pd8mf7uaq.pdf

Sign Language Recognition

This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.

/pdf/sign-language-recognition-using-sub-units-1fmdu91u0h.pdf

Sign language recognition using sub-units

Objective To assess whether equity is achieved in use of general practitioner, outpatient, and inpatient services by children and young people according to their ethnic group and socioeconomic background. Design Secondary analysis of the British general household survey, 1991-94. Subjects 20 473 children and young people aged between 0 and 19 years. Main outcome measures Consultations with a general practitioner within a two week period, outpatient attendances within a three month period, and inpatient stays during the past year. Results There were no significant class differences in the use of health services by children and young people, and there was little evidence of variation in use of health services according to housing tenure and parental work status. South Asian children and young people used general practitioner services more than any other ethnic group after controlling for socioeconomic background and perceived health status, but the use of hospital outpatient and inpatient services was significantly lower for children and young people from all minority ethnic groups compared with the white population. Conclusions Our results differ from previous studies, which have reported significant class differences in use of health services for other age groups. We found no evidence that children and young people9s use of health services varied according to their socioeconomic status, suggesting that equity has been achieved. A child or young person9s ethnic origin, however, was clearly associated with use of general practitioner and hospital services, which could imply that children and young people from minority ethnic groups receive a poorer quality of health care than other children and young people.

https://www.bmj.com/content/bmj/317/7165/1047.full.pdf

Use of health services by children and young people according to ethnicity and social class: secondary analysis of a national survey

This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.

/pdf/sign-language-recognition-using-sequential-pattern-trees-4npb88fjf1.pdf

Sign Language Recognition using Sequential Pattern Trees

This paper introduces a fully automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Based on head and hand tracking, a novel temporally constrained adaptation of a priori mining is used to extract similar regions of video, with the aid of a proposed contextual negative selection method. These regions are refined in the temporal domain to isolate the occurrences of similar signs in each example. The system is shown to automatically identify and segment signs from standard news broadcasts containing a variety of topics.

/pdf/learning-signs-from-subtitles-a-weakly-supervised-approach-3vqmfp6ctp.pdf

Learning signs from subtitles: A weakly supervised approach to sign language recognition

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