How Citizen Scientists are Aiding Motor Research and Helping to Personalize Therapy and Training

Tuesday - January 30 2024, 23:01 UTC - 9 months ago

Researchers are using big data from citizen scientists to gain new insights into motor adaptation and learning. This approach, which complements traditional lab studies, allows for the evaluation of subconscious and conscious learning processes and consideration of demographic variables. The resulting database of over 2,000 sessions has already provided new insights, such as a peak in motor adaptation between ages 35 to 45.

Data generated by citizen scientists offer researchers new insight into how people adapt and move differently to correct for movement errors. This innovative approach, using big data, provides new possibilities for motor learning research and may one day lead to personalized physical therapy and training programs.Traditionally, motor learning studies have been conducted in a lab setting using expensive equipment to analyze participants' movements .

However, these studies often involve a small number of participants, raising questions about how generalizable the results are to the larger population. Additionally, the exclusive focus on extreme age groups (very young and very old) has limited the understanding of age-related effects on motor adaptation.To address these limitations, Jonathan Tsay, assistant professor at Carnegie Mellon University's Department of Psychology, developed a simple motor-learning assessment that could be taken online by citizen scientists .

This allowed for a diverse and larger dataset of over 2,000 sessions to be collected.One of the advantages of using big data in motor research is the ability to evaluate the relative contribution of subconscious and conscious learning processes. Through machine learning and other techniques, Tsay and his team were able to determine which variables were important in forecasting motor performance and the process of motor learning .

This led to new insights, such a peak in motor adaptation between ages 35 to 45, which had previously been missed in smaller lab studies.This type of big data analysis also allows for consideration of demographic variables, such as gender, age, and visual impairment, that may impact motor adaptation. By including a wide range of participants, including those with video game experience, the results may be more applicable to the general population .

The short, at-home test developed for citizen scientists takes only eight minutes, significantly less than the traditional 80-minute lab experiment. This allows for efficient tracking of motor learning changes over time, as many participants completed the test multiple times.In conclusion, the use of big data generated by citizen scientists offers a unique and valuable perspective in motor learning research .

By complementing traditional lab studies, this approach has the potential to democratize motor learning research and provide insights that may have been previously unattainable.