The Personalization Tool utilizes the user’s predicted emotions as the output of the Training and Inference domain, together with contextual information, e.g., a user and activity levels, to provide personalized suggestions on the recommended activity level for the user in educational XR applications.
The Personalization Tool exploits the Publisher/Subscriber messaging protocol implemented using Redis to provide asynchronous, real-time communication between the Personalization Tool, Inference domain and an XR educational software implemented using Unity.
A web-based Dashboard is also provided as a graphic interface for better visualizing the personalization tool functionality and how it communicates with the other domain’s components, i.e., multimodal fusion layer and Unity application.
Personalization Tool supports the three main Operational Systems (OS): Linux, MacOS, and Windows. The two pre-requisites are:
- Docker installed
- Python 3.10 installed
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Navigate to the root directory of the downloaded project, and from the root repository, run the command to build the docker images:
docker compose build
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Navigate to the directory Personalization_Tool
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Prepare the virtual environment (Create and activate virtual environment with venv).
python -m venv ./venvsource ./venv/bin/activate -
Install the requirements within the virtual environment
pip install -r requirements.txt
- Navigate to the directory
Personalization_Dashboard - Prepare the virtual environment (Create and activate virtual environment with venv).
python -m venv ./venv
source ./venv/bin/activate
- Install the requirements within the virtual environment
pip install -r requirements.txt
Personalization Tool can be used as a standalone application or with Enablers-CLI, a command-line interface to simplify the use of Enablers. The easiest way to access the Personalization Tool’s functionalities is by using Enablers-CLI.
However, if changing or expanding the Personalization tool’s functionalities is required, it is possible to access each component using docker commands, as exemplified below. Thus, the instructions described below are focused on a development environment.
A “configuration.json” file is required to provide the components with the necessary specifications for running. A default version of “configuration.json” is provided and can be changed by the user.
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Run Redis docker
docker compose up redis -d-
To stop Redis docker
docker compose down
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-
Run
python personalization_tool/suggest_activity_level.py -
Run (in another terminal tab)
python personalization_tool/simulate_input_output.py
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Enter docker image with bash entrypoint
REDIS_HOST=redis docker compose run --rm personalization-tool /bin/bash -
Run simulate input/output script in background
python personalization_tool/simulate_input_output.py > out.txt &
- Run suggest activity level script
python personalization_tool/suggest_activity_level.py
docker compose up -d
Go to http://127.0.0.1:8000/ to access Personalization Dashboard.
Copyright © 2024, Maastricht University
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Pre-trained and fine-tuned models created using the RAVDESS dataset are shared under the CC BY-NC-SA 4.0 license to comply with the RAVDESS license, as the models are derivative works from this dataset.
Please refer to LICENSE.md document for more details.
A list of released versions and the main changes can be found on Changelog.
To check your current version, go to the file setup.cfg.