An Adaptive, Affordable, Open-Source Robotic Hand for Deaf and Deaf-Blind Communication Using Tactile American Sign Language

Abstract

Currently, ∼1.5 million American deaf-blind individuals depend on the availability of interpreting services to communicate in their primary conversational language, tactile American Sign Language (ASL). In an effort to give the deafblind community access to a device that facilitates independent communication using tactile ASL, we developed TATUM (Tactile ASL Translational User Mechanism). TATUM employs 15 degrees of actuation in a hand-wrist system that is capable of signing the 26-letter ASL alphabet. Leveraging Interpres, an independent cloud-based service, all servo sequences that render desired fingerspelled letters and ASL words are stored in a web application programming interface (API). A validation study including both deaf and deaf-blind participants confirmed that the TATUM hand mimics a human hand both in size and feel. The current design of TATUM attained an average recognition rate of 94.7% in visual validation, indicative of the potential to support deaf and deaf-blind individuals in communicating via visual and tactile ASL.

Applications of Additive Manufacturing, or 3D Printing, in the Rehabilitation of Individuals With Deafblindness: A Scoping Study

“…Bulgarelli et al. (2016) and Johnson et al. (2021) present robotic hands for sign language reproduction, based on the PARLOMA project (https://parloma.github.io/), said to enable independent remote communication (Russo et al., 2015).…”

“…Albeit the increasing number of studies on the use of AM for individuals with vision impairments, the current AM literature offers limited insight on its usability in the field of deafblindness. Only five of the included studies (Bulgarelli et al., 2016; Duvernoy et al., 2017, 2019; Johnson et al., 2021; Parker & Sullivan, 2018) specifically focused on AM for the deafblind, while the other four (Brule et al., 2016; Giraud et al., 2017; Giraud & Jouffrais, 2016; Voženílek et al., 2011) focused on its use for students with vision impairments in general. Consequently, the comprehensiveness of the review’s findings is limited due to methodological constraints (small sample sizes, qualitative paradigms such as observational reports, AM-based devices not comprehensively tested, limited or missing involvement of individuals with deafblindness).…”

“…According to the authors, Hapticomm can effectively recognize speech and text and translate it to deafblind tactile fingerspelling language. Bulgarelli et al (2016) and Johnson et al (2021) present robotic hands for sign language reproduction, based on the PARLOMA project (https://parloma.github.io/), said to enable independent remote communication (Russo et al, 2015).…”

“…Relying mostly on the sense of touch to communicate, individuals with deafblindness will learn differently and at a different rate than people with vision impairment alone (McInnes, 1999). To overcome this limitation, AM can help in producing more affordable devices that enable direct and remote communication (Bulgarelli et al, 2016;Chirila et al, 2020;Duvernoy et al, 2019;Johnson et al, 2021) with peers who are inexperienced with tactile sign language (Parker & Sullivan, 2018). Open source projects identified here (Bulgarelli et al, 2016;Duvernoy et al, 2017Duvernoy et al, , 2019 are prime examples of the potential of AM in enabling the affordable production of devices within rehabilitation centers when equivalent technology is lacking or too expensive.…”