Best Practices for Exoskeleton Evaluation Using DeepLabCut-Reference-Cited by-同舟云学术

Best Practices for Exoskeleton Evaluation Using DeepLabCut

Published:2024-01-03 Issue:3 Volume:51 Page:22-24
ISSN:0163-5999
Container-title:ACM SIGMETRICS Performance Evaluation Review
language:en
Short-container-title:SIGMETRICS Perform. Eval. Rev.

Author:

Ahmed Nishat¹,Rahman Amaan¹,Rhode Lucia¹

Affiliation:

1. The Cooper Union for the Advancement of Science and Art

Abstract

Exoskeleton fit evaluation using pose estimation is necessary to ensure exoskeletons promote productivity in industrial settings. However, both marker-based and markerless vision based systems consist of uncertainties in tracking human joints while a person is wearing an exoskeleton because they have been trained on datasets consisting of only the human body [14].

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3639830.3639840

Reference16 articles.

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2. Cristian Canton-Ferrer, Josep R. Casas, and Montse Pard'as. "Marker-based human motion capture in Multiview Sequences - EURASIP Journal on advances in Signal Processing". In: SpringerOpen (Nov. 2010).

3. "Data". In: NIST (July 2021). url: https://www. nist . gov / el / intelligent - systems - division - 73500/exoskeletons-and-exosuits-research-andstandard- test-methods-1.

4. Eldar Insafutdinov et al. DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model. 2016. arXiv: 1605.03170 [cs.CV].

5. Alexander Mathis et al. "A Primer on motion capture with Deep Learning: Principles, pitfalls, and Perspectives". In: Neuron (Oct. 2020).