newcastleuniversity / dispel Goto Github PK
View Code? Open in Web Editor NEWA python DIgital Signal ProcEssing Library developed to standardize extraction of sensor-derived measures (SDMs) from wearables or smartphones data.
License: MIT License
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Create a Tutorial Notebook to leverage DISPEL with Mobilize-D YAR Data
Overview
This issue aims to develop a comprehensive, easy-to-follow tutorial notebook that effectively demonstrates how to leverage DISPEL with Mobilize-D data. This tutorial aims to provide new users with a hands-on approach to learning the library's functionalities, making it easier to integrate into their projects.
Objectives
- Data Introduction: Briefly introduce Mobilize-D data, its significance, and typical use cases.
- Environment Setup: Guide on setting up the necessary environment, including library installation.
- Basic Operations: Cover basic library functions, showcasing simple data manipulations and operations.
- Advanced Features: Dive into more complex features, providing real-world examples deriving SDMs from Mobilize-D data.
- Best Practices: Offer guidance on best practices for transforming datasets and including technical and behavioural deviations.
- Interactive Examples: Include interactive examples for users to experiment with, enhancing learning engagement.
Desired Outcomes
- A Jupyter notebook that is well-documented, with clear explanations and code comments.
- The tutorial should cater to beginners and intermediate users, gradually building in complexity.
- Users should feel confident using the library with Mobilize-D data for their specific needs after completion.
Add descriptions of test-specific assumptions
Overview
Currently, little information is provided on the basic assumptions for each algorithm in DISPEL.
For instance, gait and turn algorithms assume a lumbar-mounted device. Drawing algorithms assume a handheld device.
Objectives
- Add information about BDH/ADS Konectom battery of tests based this link
- Add information about APDM / Mobilise-D data based on this link
- Add information about Digital Artefacts Finger Tapping this link
Desired Outcomes
- More information about test-specific assumptions and instructions added as module doctstrings.
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