mHeatlth Ai
Patrick Puma, Ethan Graber, Will Kim · Team AI mHealth
Submitted to AI Policy Hackathon at Johns Hopkins University. Sprint projects are early-stage work by participants, not Apart Research publications.
This project proposes a scalable solution leveraging inertial measurement units (IMUs) and machine learning (ML) techniques to provide meaningful metrics on a person's movement performance throughout the day. By developing an activity recognition model and estimating movement quality metrics, we aim to offer continuous asynchronous feedback to patients and valuable insights to therapists. This system could enhance patient adherence, improve rehabilitation outcomes, and extend access to quality physical therapy, particularly in underserved areas. our video didnt have time to edit
Reviews
The team delivers a well-made presentation and is considerate of all the aspects of the product. Would appreciate if this comes with a greater degree of completion.
Cite this project
@misc{puma2024mheatlth,
title = {{mHeatlth Ai}},
author = {Patrick Puma and Ethan Graber and Will Kim},
year = {2024},
month = oct,
note = {Submitted to AI Policy Hackathon at Johns Hopkins University, an Apart Research Sprint},
howpublished = {\url{https://apartresearch.com/sprints/projects/mheatlth-ai}},
url = {https://apartresearch.com/sprints/projects/mheatlth-ai}
}More from AI Policy Hackathon at Johns Hopkins University
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