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Respositroy for the analysis of the GMAC algorithm for upper-limb use. GMAC stands for "gross movement + acivity counts".

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GMAC: A simple measure to quantify upper limb use from wrist-worn accelerometers

Repository for the analysis of the new GMAC measure for upper-limb use. GMAC stands for "gross movement + acivity counts".

This contains the analysis code employed for the paper "GMAC: A simple measure to quantify upper limb use from wrist-worn accelerometers". The paper is currently under review.

Here is the block diagram of the new GMAC algorithm: GMAC block diagram

Getting the data

The data used in this paper can be obtained from our previously opened dataset from the Upper-Limb Assessment GitHub repository.

After you clone this repository, carry out the following steps to get the data:

  1. Create a folder called data in the root directory of this repository.
  2. Copy the control and patient data folders into data.

Once you do this you are ready to run the analysis code.

Analysis Code Details

The analysis has four notebooks:

01 Pitch Optimization: Notebook for optimizing the parameter for the forearm orientation subblock of the new GMAC algorithm.

02 Accl. Magnitude Optimization: Notebook for optimizing the parameter for estimating the amount of forearm movements subblock of the new GMAC algorithm.

03a Decision Rule Optimization: Notebook for optimizing the parameter of the decision rule block of the new GMAC algorithm.

03b Decision Rule Optimization (Intra-Subject Model): Notebook for optimizing the parameter of the decision rule block of the intra-subject models for the new GMAC algorithm .

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Respositroy for the analysis of the GMAC algorithm for upper-limb use. GMAC stands for "gross movement + acivity counts".

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