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Abstract Sign languages are human communication systems that are equivalent to spoken language in their capacity for information transfer, but which use a dynamic visual signal for communication. Thus, linguistic metrics of complexity, which are typically developed for linear, symbolic linguistic representation (such as written forms of spoken languages) do not translate easily into sign language analysis. A comparison of physical signal metrics, on the other hand, is complicated by the higher dimensionality (spatial and temporal) of the sign language signal as compared to a speech signal (solely temporal). Here, we review a variety of approaches to operationalizing sign language complexity based on linguistic and physical data, and identify the approaches that allow for high fidelity modeling of the data in the visual domain, while capturing linguistically-relevant features of the sign language signal. 

Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hard-of-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, 0.93 trigger recognition rate. 

Deaf spaces are unique indoor environments designed to optimize visual communication and Deaf cultural expression. However, much of the technological research geared towards the deaf involve use of video or wearables for American sign language (ASL) translation, with little consideration for Deaf perspective on privacy and usability of the technology. In contrast to video, RF sensors offer the avenue for ambient ASL recognition while also preserving privacy for Deaf signers. Methods: This paper investigates the RF transmit waveform parameters required for effective measurement of ASL signs and their effect on word-level classification accuracy attained with transfer learning and convolutional autoencoders (CAE). A multi-frequency fusion network is proposed to exploit data from all sensors in an RF sensor network and improve the recognition accuracy of fluent ASL signing. Results: For fluent signers, CAEs yield a 20-sign classification accuracy of %76 at 77 GHz and %73 at 24 GHz, while at X-band (10 Ghz) accuracy drops to 67%. For hearing imitation signers, signs are more separable, resulting in a 96% accuracy with CAEs. Further, fluent ASL recognition accuracy is significantly increased with use of the multi-frequency fusion network, which boosts the 20-sign fluent ASL recognition accuracy to 95%, surpassing conventional feature level fusion by 12%. Implications: Signing involves finer spatiotemporal dynamics than typical hand gestures, and thus requires interrogation with a transmit waveform that has a rapid succession of pulses and high bandwidth. Millimeter wave RF frequencies also yield greater accuracy due to the increased Doppler spread of the radar backscatter. Comparative analysis of articulation dynamics also shows that imitation signing is not representative of fluent signing, and not effective in pre-training networks for fluent ASL classification. Deep neural networks employing multi-frequency fusion capture both shared, as well as sensor-specific features and thus offer significant performance gains in comparison to using a single sensor or feature-level fusion. 

Over the years, there has been much research in both wearable and video-based American Sign Language (ASL) recognition systems. However, the restrictive and invasive nature of these sensing modalities remains a significant disadvantage in the context of Deaf-centric smart environments or devices that are responsive to ASL. This paper investigates the efficacy of RF sensors for word-level ASL recognition in support of human-computer interfaces designed for deaf or hard-of-hearing individuals. A principal challenge is the training of deep neural networks given the difficulty in acquiring native ASL signing data. In this paper, adversarial domain adaptation is exploited to bridge the physical/kinematic differences between the copysigning of hearing individuals (repetition of sign motion after viewing a video), and native signing of Deaf individuals who are fluent in sign language. Domain adaptation results are compared with those attained by directly synthesizing ASL signs using generative adversarial networks (GANs). Kinematic improvements to the GAN architecture, such as the insertion of micro-Doppler signature envelopes in a secondary branch of the GAN, are utilized to boost performance. Word-level classification accuracy of 91.3% is achieved for 20 ASL words. 

RF sensing based human activity and hand gesture recognition (HGR) methods have gained enormous popularity with the development of small package, high frequency radar systems and powerful machine learning tools. However, most HGR experiments in the literature have been conducted on individual gestures and in isolation from preceding and subsequent motions. This paper considers the problem of American sign language (ASL) recognition in the context of daily living, which involves sequential classification of a continuous stream of signing mixed with daily activities. In particular, this paper investigates the efficacy of different RF input representations and fusion techniques for ASL and trigger gesture recognition tasks in a daily living scenario, which can be potentially used for sign language sensitive human-computer interfaces (HCI). The proposed approach involves first detecting and segmenting periods of motion, followed by feature level fusion of the range-Doppler map, micro-Doppler spectrogram, and envelope for classification with a bi-directional long short-term memory (BiL-STM) recurrent neural network. Results show 93.3% accuracy in identification of 6 activities and 4 ASL signs, as well as a trigger sign detection rate of 0.93.