# MS-TCRNet: Multi-Stage Temporal Convolutional Recurrent Networks for action segmentation using sensor-augmented kinematics

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/ms-tcrnet-multi-stage-temporal-convolutional-recurrent-networks-for-action-2/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Adam Goldbraikh,Omer Shubi,Or Rubin,Carla M. Pugh,Shlomi Laufer |
| Citations | 7 |
| DOI | 10.1016/j.patcog.2024.110778 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://pmc.ncbi.nlm.nih.gov/articles/PMC11526485/pdf/nihms-2010382.pdf |
| OpenAlex ID | https://openalex.org/W4400617645 |
| PMID | 39494221 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Adam Goldbraikh](https://scholariq.org/researchers/adam-goldbraikh/)

## Paper primary topic

- [Human Pose and Action Recognition](https://scholariq.org/topics/human-pose-and-action-recognition/)

## Paper topics

- [Human Pose and Action Recognition](https://scholariq.org/topics/human-pose-and-action-recognition/)
- [Human Motion and Animation](https://scholariq.org/topics/human-motion-and-animation/)
- [Video Analysis and Summarization](https://scholariq.org/topics/video-analysis-and-summarization/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.