You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: We propose an end-to-end model for human pose estimation in an

Keyphrases: in-bed pose estimation camera-based vision

Dataset description:
PixelHelp: PixelHelp includes 187 multi-step instructions of 4 task categories deined in https://support.google.com/pixelphone and annotated by human. This dataset includes 88 general tasks, such as configuring accounts, 38 Gmail tasks, 31 Chrome tasks, and 30 Photos related tasks.
DDD20: The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames. DDD20 is the longest event camera end-to-end driving dataset to date with 51h of DAVIS event+frame camera and vehicle human control data collected from 4000km of highway and urban driving under a variety of lighting conditions.
EyeCar: EyeCar is a dataset of driving videos of vehicles involved in rear-end collisions paired with eye fixation data captured from human subjects. It contains 21 front-view videos that were captured in various traffic, weather, and day light conditions.
LSP: The Leeds Sports Pose (LSP) dataset is widely used as the benchmark for human pose estimation. The original LSP dataset contains 2,000 images of sportspersons gathered from Flickr, 1000 for training and 1000 for testing.
MPII: The MPII Human Pose Dataset for single person pose estimation is composed of about 25K images of which 15K are training samples, 3K are validation samples and 7K are testing samples (which labels are withheld by the authors). The images are taken from YouTube videos covering 410 different human activities and the poses are manually annotated with up to 16 body joints.

Options:- A: PixelHelp  B: DDD20  C: EyeCar  D: LSP  E: MPII
["LSP", "MPII"]