Recognition of individual Pepper-produced LIS signs varies widely, with several signs significantly above chance and others not significant¶

Claim · All claims
Evidence · 1 study (2 entries) · design r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims¶

q2 i? One-tailed binomial tests against the 25% chance level showed statistically significant recognition for signs including Dimenticare, Finire/Fatto, Shampoo, and Università (p < 0.0001). → Giulia Boaa 2025 q2 i? Signs such as Insegnare and Libro did not reach significance, which the authors attribute to articulation constraints. → Giulia Boaa 2025 (2)

Evidence¶

Giulia Boaa 2025¶

Giulia Boaa, Marco Boaa, Cristina Gena, Alessandro Mazzei, Massimo Donini, and Alberto Lillo. (2025). Using the Pepper Robot to Support Sign Language Communication. https://github.com/CodeCruncher63/ISCR2025 doi:10.1007/978-981-95-2382-5_8

q2 · i? · design · r2

Binomial-test analysis of the 15 single-choice sign recognition items in the 12-participant questionnaire; several signs reached "sta0s0cally significant recogni0on rates (p < 0.0001)", e.g. Dimenticare at 100% (12 of 12).

"Several signs, including Dimen6care, Finire/FaJo, Shampoo, and Università, showed sta0s0cally significant recogni0on rates (p < 0.0001)."

Giulia Boaa 2025 (2)¶

Giulia Boaa, Marco Boaa, Cristina Gena, Alessandro Mazzei, Massimo Donini, and Alberto Lillo. (2025). Using the Pepper Robot to Support Sign Language Communication. https://github.com/CodeCruncher63/ISCR2025

q2 · i? · design · r2

Same binomial-test analysis: Insegnare reached 33.3% (p = 0.4110) and Libro 50.0% (p = 0.1035), neither significant; the article attributes this to "ar0cula0on constraints".

"Conversely, signs like Insegnare and Libro did not reach significance, likely due to ar0cula0on constraints."

Discussion¶

Related Claims¶

• A MATLAB inverse-kinematics pipeline automated generation of 38 of 52 LIS signs on Pepper and cut authoring time from 45–60 to under 10 minutes per sign — related
• Empirical research on the Pepper robot in formal education is recent, concentrated in primary education, and dominated by qualitative designs with small samples — related
• LIS users recognize Pepper's isolated signs far better than its full signed sentences in an exploratory evaluation — related