Deadlift recognition and application based on multiple modalities using recurrent neural network

Shih Wei Sun, Ting Chen Mou, Pao Chi Chang

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

To improve the workout efficiency and to provide the body movement suggestions to users in a ``smart gym'' environment, we propose to use a depth camera for capturing a user's body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Original languageEnglish
Article number002
JournalIS and T International Symposium on Electronic Imaging Science and Technology
Volume2020
Issue number17
DOIs
StatePublished - 26 Jan 2020
Event2020 3D Measurement and Data Processing Conference, 3DMP 2020 - Burlingame, United States
Duration: 26 Jan 202030 Jan 2020

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