1 code implementation • 21 Dec 2021 • Avinash Parnandi, Aakash Kaku, Anita Venkatesan, Natasha Pandit, Audre Wirtanen, Haresh Rajamohan, Kannan Venkataramanan, Dawn Nilsen, Carlos Fernandez-Granda, Heidi Schambra
Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation.
1 code implementation • 3 Nov 2021 • Aakash Kaku, Kangning Liu, Avinash Parnandi, Haresh Rengaraj Rajamohan, Kannan Venkataramanan, Anita Venkatesan, Audre Wirtanen, Natasha Pandit, Heidi Schambra, Carlos Fernandez-Granda
To address this, we propose a novel approach for high-resolution action identification, inspired by speech-recognition techniques, which is based on a sequence-to-sequence model that directly predicts the sequence of actions.
Thus, using a combination of IMU-based motion capture and deep learning, we were able to identify primitives automatically.
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data.
We compared their classification accuracy, computational complexity, and tuning requirements.