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AI and Wearable Technologies in Movement Disorder: Data Driven Approach

Translational Neuroscience, Cumming School of Medicine, University of Calgary

This unique and immersive traineeship is designed to equip you with practical skills and knowledge through hands-on, project-based experiences in applying advanced AI tools to address specific questions in basic, translational, or clinical research and healthcare delivery. Bring your own projects or inquiries and learn how to harness AI-driven data analysis, causal inference techniques, and wearable technology applications in the field of movement disorders. Our primary goal is to help you develop high-level familiarity and a deep understanding of AI-based productivity tools in the clinical management and research of movement disorders. Throughout the program, you’ll gain practical skills that can be directly applied to your work. Participants can expect continuous support from the program in publishing their project results beyond the traineeship.

Main Areas of Interest

Dr. Bin Hu is an endowed professor and founding director of Translational Neuroscience Division at the Cumming School of Medicine, University of Calgary. He is also affiliated with the Hotchkiss Brain Institute and the Alberta Children’s Hospital Research Institute. Dr. Hu's research focuses on movement disorders, particularly Parkinson’s Disease (PD), and he is renowned for pioneering innovations in digital health and wearable technologies to enhance treatment and telehealth solutions for neurological disorders.

He is the driving force behind large-scale international projects like the Ambulosono-ACSCON-OpenDH, which aims to develop second-generation wearable systems and digital health platforms. His work also explores non-pharmaceutical interventions, using advanced technological tools to address motor and cognitive decline in neurodegenerative diseases. One notable area of his research is the study of music-induced paradoxical movement in Parkinson's disease, as well as the development of evidence-based digital health interventions

AI and Data Science for Movement Disorders:

This area focuses on applying Large Language Models (LLMs) and data science techniques to ensure high-quality data collection, processing, and analysis. Emphasis will be placed on maintaining data integrity, enhancing precision, and automating workflows in movement disorder research. This would include applications to patient diaries and wearable movement tracking devices.

Causal Inference Analysis:

Learn how to apply causal inference methods and AI tools to identify cause-and-effect relationships from observational clinical data. This area emphasizes understanding the underlying mechanisms of movement disorders and developing non-pharmaceutical interventions for rehabilitation.

Large Language Models (LLMs) and Synthetic Data:

Explore the use of LLMs and synthetic data generation to enhance analysis of high-dimensional data from sources like patient diaries, wearable devices, and rehabilitation training. Techniques will include integrating various data streams to study patient behavior, symptom patterns, and the effects of movement interventions on quality of life.

Wearable Technology and Music-Based Rehabilitation:

Participants will learn about Dr. Hu’s Ambulosono program, which uses wearable technology and music-based reinforced walking exercises to monitor and improve patients' daily activity. The focus will be on how to analyze the data collected from patients’ movements and activities to develop actionable insights for therapy.

Specialties

Artificial Intelligence

Medical Devices

Neurosciences

Techniques You Will Learn

Data Integrity and Precision in Movement Tracking:

Techniques for ensuring data quality in patient tracking devices and how to automate data analysis to identify patterns in movement disorders.

Causal Inference in Clinical Data:

Apply statistical and machine learning methods to infer causal relationships between variables in clinical observational studies. You will learn to draw meaningful conclusions that can lead to targeted interventions.

Using LLMs and Synthetic Data for Analysis:

Techniques to integrate LLMs and synthetic data generation in analyzing large datasets, including movement tracking and patient diaries. This will enhance decision-making processes and personalize treatment approaches.

Wearable Technology in Rehabilitation:

Hands-on training in using wearable devices to track patient movements, along with analyzing the data to optimize rehabilitation programs. Learn how to measure and improve patient adherence to exercise interventions using music-based feedback.

Modules

Learning Objectives

By the end of the traineeship, participants will be able to:

Analyze and Implement Data Science Techniques:

Ensure data quality and integrity in the analysis of movement disorders using AI-driven methods.

Apply Causal Inference Methods:

Use AI tools and statistical techniques to derive meaningful cause-and-effect relationships in clinical observational data.

Utilize LLMs and Synthetic Data:

Integrate large language models and synthetic data to improve the analysis of patient rehabilitation data from wearable devices and diaries.

Develop and Apply Wearable Technologies:

Utilize wearable technologies like movement trackers to monitor daily activities and optimize rehabilitation interventions in patients with movement disorders.

MODULES

Module 1: AI and Data Science in Movement Disorders

Topics Covered:

Introduction to AI and its applications in medicine, including AI modules dedicated to literature review, statistical inference, and voice-based interactions.
Techniques for ensuring data integrity and precision in wearable device data.
Automation of data processing workflows using AI and large language models (LLMs).

Learning Outcomes:

Understand how to maintain high-quality data and the importance of automation in clinical settings.
Implement AI-based tools to optimize data analysis.

Module 2: Causal Inference Analysis in Clinical Data

Topics Covered:

Introduction to causal inference and its significance in movement disorders.
Application of statistical and AI methods to identify cause-and-effect relationships in observational clinical data.

Learning Outcomes:

Ability to apply causal inference techniques to derive meaningful clinical insights.
Understanding how to improve interventions based on data-derived causal relationships.

Module 3: Integrating LLMs and Synthetic Data for Analysis

Topics Covered:

Using large language models (LLMs) for analyzing high-dimensional data from multiple sources (patient diaries, wearable devices, rehabilitation training).
Synthetic data generation to augment real-world datasets.

Learning Outcomes:

Leverage LLMs and synthetic data to improve patient behavior analysis.
Apply advanced data integration techniques to understand rehabilitation impacts and movement patterns.

Module 4: Wearable Technology and Music-Based Rehabilitation

Topics Covered:

Overview of the Ambulosono program: using wearable technology and music-based walking exercises to improve mobility in patients with movement disorders.
Data collection and analysis from wearable devices to monitor patient daily activities and engagement with rehabilitation.

Learning Outcomes:

Use wearable devices for data-driven rehabilitation.
Apply insights from patient data to optimize movement disorder interventions.

SCHEDULE

Week 1:

Date: Thursday, November 7th, 2024
Time: 8:00 AM - 9:00 AM (Calgary time)
Module 1: AI and Data Science in Movement Disorders

Week 2:

Date: Thursday, November 14th, 2024
Time: 8:00 AM - 9:00 AM (Calgary time)
Module 2: Causal Inference Analysis in Clinical Data

Week 3:

Date: Thursday, November 21st, 2024
Time: 8:00 AM - 9:00 AM (Calgary time)
Module 3: Integrating LLMs and Synthetic Data for Analysis

Week 4:

Date: Thursday, November 28th, 2024
Time: 8:00 AM - 9:00 AM (Calgary time)
Module 4: Wearable Technology and Music-Based Rehabilitation

Program Fee


Total Number of Modules	4
Time duration of Each module	60 minutes
Total Program Cost	$350

This program allows Merit Applications. This program allows merit-based applications for virtual and onsite clinical and research programs. If you are successfully awarded under this category, Trialect or the host mentor will cover the tuition fee only. All applications will be evaluated based on merit. Due to the high level of competition, the chances of being selected under the merit category are quite limited.

Who Can Apply

All

Host Details

Host Name: Bin Hu MD. PhD, Founder of Division of Translational Medicine

Affiliation: Translational Neuroscience, Cumming School of Medicine, University of Calgary

Address: 3330 Hospital Dr NW, Calgary, AB T2N 4Z5, Canada

Website URL: https://cumming.ucalgary.ca/

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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Live Interactive One-On-One (Virtual)

Fellowship - Basic/Translational/Clinical Research Program

Canada

Application Review Deadline:

Dec 1st, 2024

Apply Now

Program Logistics and Administrative Fees

This program has the following durations available:

Duration	Fee
2 weeks	$625.00
6 weeks	$1,500.00
12 weeks	$2,750.00

Questions and Answers

Commonly asked questions about this program from the host and other attendees.

Do I need to be a medical professional in movement disorders in participating this trainingship?

No. The AI skills and knowledge taught by the program do not require specific clinical experiences in movement disorders.

Will I learn certain practical skills of AI applications besides the general knowledge?

Yes. A core set of AI applications and skills, known as WisedomWaves developed via Open Digital Heath program in Canada, will be the main teaching content.

What kind of support I can expect from the program beyond trainingship?

Support can include continued access to WisdomWaves, participating further trainingship, private coaching and consultations for project development, and publications.