Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features

Conference paper

Pritom, Y. A., Rahman, M. S., Rahman, H. R., Kowshik, M. A. and Ahad, M. 2022. Lunch-Box Preparation Activity Understanding from Motion Capture Data Using Handcrafted Features. ABC 2021: 3rd International Conference on Activity and Behavior Computing. Online 22 - 23 Oct 2021 Springer Singapore. https://doi.org/10.1007/978-981-19-0361-8_12
AuthorsPritom, Y. A., Rahman, M. S., Rahman, H. R., Kowshik, M. A. and Ahad, M.
TypeConference paper
Abstract

Japanese packed-meal or Bento preparation or packaging automated by a robot is a recent challenge. It is one of the latest cementations in human activity recognition systems. It shows contingency on physical gesture recognition techniques using some sensor-based datasets, especially some motion capture data or skeleton data. To get a firm grip on the recognition, we (Team Boson Kona) implement a method dealing with some handcrafted features gained from the filtered data. We employ several pre-processing steps to handle any inconsistency of the dataset: after cleaning the data, we use filtering and windowing techniques to process the raw data. Then we figure out multiple statistical and geometrical features from joints-based handcrafted features. After considering the most significant features for model training, we try out support vector machine classifier, random forest classifier and extra tree classifier. After applying all these methodologies, we obtain maximum accuracy of 64.6% using the extra tree classifier.

Year2022
ConferenceABC 2021: 3rd International Conference on Activity and Behavior Computing
PublisherSpringer Singapore
Publication dates
Online04 May 2022
Publication process dates
Deposited14 Jul 2023
Journal citationpp. 193-205
ISSN2190-3018
Book titleSensor- and Video-Based Activity and Behavior Computing: Proceedings of 3rd International Conference on Activity and Behavior Computing (ABC 2021)
Book editorAhad, M.
Inoue, S.
Roggen, D.
Fujinami, K.
ISBN9789811903601
9789811903618
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-19-0361-8_12
Web address (URL) of conference proceedingshttps://link.springer.com/book/10.1007/978-981-19-0361-8
Web address (URL)https://link.springer.com/chapter/10.1007/978-981-19-0361-8_12
Copyright holder© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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