mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation Using mmWave Radars

Creative Commons License

Sengupta A., Cao S.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, vol.34, no.11, pp.8418-8429, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 11
  • Publication Date: 2023
  • Doi Number: 10.1109/tnnls.2022.3151101
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Biotechnology Research Abstracts, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.8418-8429
  • Keywords: Radar, Pose estimation, Optical sensors, Estimation, Antenna arrays, Lighting, Doppler radar, Gated recurrent unit (GRU), millimeter-wave (mmWave) radars, natural language processing (NLP), point cloud (PCL), pose estimation, sequence-to-sequence (Seq2Seq), skeletal key points, skeletal pose, GAIT ANALYSIS, KINECT
  • Recep Tayyip Erdoğan University Affiliated: No


In this article, we presented mmPose-NLP, a novel natural language processing (NLP) inspired sequence-to-sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data. To the best of our knowledge, this is the first method to precisely estimate up to 25 skeletal key points using mmWave radar data alone. Skeletal pose estimation is critical in several applications ranging from autonomous vehicles, traffic monitoring, patient monitoring, and gait analysis, to defense security forensics, and aid both preventative and actionable decision making. The use of mmWave radars for this task, over traditionally employed optical sensors, provides several advantages, primarily its operational robustness to scene lighting and adverse weather conditions, where optical sensor performance degrade significantly. The mmWave radar point-cloud (PCL) data are first voxelized (analogous to tokenization in NLP) and N frames of the voxelized radar data (analogous to a text paragraph in NLP) is subjected to the proposed mmPose-NLP architecture, where the voxel indices of the 25 skeletal key points (analogous to keyword extraction in NLP) are predicted. The voxel indices are converted back to real-world 3-D coordinates using the voxel dictionary used during the tokenization process. Mean absolute error (MAE) metrics were used to measure the accuracy of the proposed system against the ground truth, with the proposed mmPose-NLP offering <3 cm localization errors in the depth, horizontal, and vertical axes. The effect of the number of input frames versus performance/accuracy was also studied for N = {1,2,...,10}. A comprehensive methodology, results, discussions, and limitations are presented in this article. All the source codes and results are made available on GitHub for further research and development in this critical yet emerging domain of skeletal key-point estimation using mmWave radars.