Add Malaia et al. 2024 on radar-based sign language corpora

src/index.md

@@ -192,6 +192,10 @@ As a result, estimating pose from videos has become the preferred method in rece
 Compared to video representations, accurate skeletal poses have a lower complexity and provide a semi-anonymized representation of the human body, while observing relatively low information loss. 
 However, they remain a continuous, multidimensional representation that is not adapted to most NLP models.
 
+###### Radar Sensing {-}
+captures 3D articulator motion through electromagnetic returns rather than visual frames, recording only kinematic parameters and thus inherently protecting signer identity.
+@malaia-etal-2024-capturing argue that radar can capture sign language motion at high spatial and temporal resolution, preserving depth information lost in 2D video as a non-intrusive alternative to motion capture.
+
 ###### Written notation systems {-}
 represent signs as discrete visual features. Some systems are written linearly, and others use graphemes in two dimensions.
 While various universal [@writing:sutton1990lessons;@writing:prillwitz1990hamburg] and language-specific notation systems [@writing:stokoe1960sign;@writing:kakumasu1968urubu;@writing:bergman1977tecknad] have been proposed,

src/references.bib

@@ -4711,3 +4711,23 @@ @inproceedings{schulder-etal-2024-signs
  url = {https://aclanthology.org/2024.signlang-1.38},
  year = {2024}
 }
+
+@inproceedings{malaia-etal-2024-capturing,
+    title = "Capturing Motion: Using Radar to Build Better Sign Language Corpora",
+    author = "Malaia, Evie  and
+      Borneman, Joshua  and
+      Gurbuz, Sevgi",
+    editor = "Efthimiou, Eleni  and
+      Fotinea, Stavroula-Evita  and
+      Hanke, Thomas  and
+      Hochgesang, Julie A.  and
+      Mesch, Johanna  and
+      Schulder, Marc",
+    booktitle = "Proceedings of the LREC-COLING 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.signlang-1.23/",
+    pages = "213--218"
+}