Turning motion into medicine: How AI, motion capture and wearables can improve your health
The integration of motion data into health monitoring is revolutionizing our understanding of human movement, moving beyond traditional applications in fitness and rehabilitation to encompass broader health insights. Historically, assessing walking and movement relied on rudimentary methods like stopwatches or visual observations. However, advancements in technology—including motion capture systems, wearable sensors, and sophisticated data analysis—are now enabling researchers to quantify movement with unprecedented accuracy. This evolution in biomechanics and data science allows for the extraction of valuable performance insights that can enhance athletic training and assist in patient recovery through personalized feedback.
At the forefront of this innovation are researchers who combine various disciplines to decode human movement. By employing machine learning algorithms, they analyze data collected from continuous monitoring, revealing patterns that can inform health outcomes. For instance, wearable devices like the Apple Watch utilize inertial measurement units to track metrics such as step count and cadence. Yet, raw data can be overwhelming and noisy, necessitating signal processing techniques to filter out irrelevant information. This refined data can then be transformed into actionable health insights, such as estimating fitness capacity from just a few steps of walking. Furthermore, researchers have found that walking speed is a crucial indicator of longevity, making motion data a potentially vital sign for predicting long-term health.
The implications of these advancements extend well into the realm of medicine, where algorithms can aid in rehabilitation and injury prevention. For example, researchers are developing machine learning models that detect heightened injury risks in athletes by analyzing subtle changes in their movement patterns. Similarly, ongoing studies are utilizing these techniques to monitor recovery in stroke patients, allowing for more tailored treatment plans. The future of personalized medicine may very well lie in dynamic monitoring of movement, where every action—be it walking or jumping—provides critical insights into health. Imagine a world where smart shoes alert athletes before an injury occurs or wearable technology detects early signs of medical conditions based on movement patterns. By transforming motion into a vital sign, we are on the cusp of a new era in health monitoring, where real-time feedback becomes a standard aspect of maintaining well-being.
The use of motion data is expanding from fitness and rehabilitation to general health.
Todor Tsvetkov/E+ via Getty Images
People often take walking for granted. We just move, one step after another, without ever thinking about what it takes to make that happen. Yet every single step is an extraordinary act of coordination, driven by precise timing between spinal cord, brain, nerves, muscles and joints.
Historically, people have used stopwatches, cameras or trained eyes to assess walking and its deficits. However, recent technological advances such as
motion capture
, wearable sensors and data science methods can record and quantify characteristics of step-by-step movement.
We are researchers
who study
biomechanics and
human performance
. We and other researchers are increasingly applying this data to improve human movement. These insights not only help athletes of all stripes push their performance boundaries, but they also support movement recovery for patients through personalized feedback. Ultimately, motion could become another vital sign.
From motion data to performance insights
Researchers around the world combine physiology, biomechanics and data science to decode human movement. This interdisciplinary approach sets the stage for a new era where machine learning algorithms find patterns in human movement data collected by continuous monitoring, yielding insights that improve health.
It’s the same technology that powers your fitness tracker. For example, the
inertial measurement unit
in the Apple Watch records motion and derives metrics such as step count, stride length and cadence. Wearable sensors, such as inertial measurement units, record thousands of data points every second. The raw data reveals very little about a person’s movement. In fact, the data is so noisy and unstructured that it’s impossible to extract any meaningful insight.
A study participant walks on a treadmill in our lab while a motion sensor attached to the subject’s ankle captures acceleration signals.
Human Performance and Nutrition Research Institute
That is where
signal processing
comes into play. A signal is simply a sequence of measurements tracked over time. Imagine putting an inertial measurement unit on your ankle. The device constantly tracks the ankle’s movement by measuring signals such as acceleration and rotation. These signals provide an overview of the motion and indicate how the body behaves. However, they often contain unwanted background noise that can blur the real picture.
With mathematical tools, researchers can filter out the noise and isolate the information that truly reflects how the body is performing. It’s like taking a blurry photo and using editing tools to make the picture clear. The process of cleaning and manipulating the signals is known as signal processing.
After processing the signals, researchers use machine learning techniques to transform them into interpretable metrics.
Machine learning
is a subfield of artificial intelligence that works by finding patterns and relationships in data. In the context of human movement, these tools can identify features of motion that correspond to key performance and health metrics.
For example, our team at the Human Performance and Nutrition Research Institute at Oklahoma State University estimated
fitness capacity
without requiring exhaustive physical tests or special equipment. Fitness capacity is how efficiently the body can perform physical activity. By combining biomechanics, signal processing and machine learning, we were able to estimate fitness capacity using data from just a few steps of our subjects’ walking.
Beyond fitness, walking data offers even deeper insights. Walking speed is
a powerful indicator of longevity
, and by tracking it, we could learn about people’s long-term health and life expectancy.
Wearables capture motion signals, and through signal processing and machine learning, the data produces valuable health metrics such as risk of falling.
Human Performance and Nutrition Research Institute
From performance to medicine
The impact of these algorithms extends far beyond tracking performance such as steps and miles walked. They can be applied to support rehabilitation and prevent injuries. Our team is developing a machine learning algorithm to detect when an athlete is at an elevated risk of injury just by analyzing their body movement and detecting subtle changes.
Other scientists have used similar approaches to
monitor motor control impairments
following a stroke by continuously assessing how a patient’s walking patterns evolve, determining whether motor control is improving, or if the patient is compensating in any way that could lead to future injury.
Similar tools can also be used to inform treatment plans based on each patient’s specific needs, moving us closer to true personalized medicine. In Parkinson’s disease, these methods have been used to
diagnose the condition
,
monitor its severity
and detect episodes of walking difficulties to prompt
cues to the patients
to resume walking.
Others have used these techniques to
design and control wearable assistive devices
such as exoskeletons that improve mobility for people with physical disabilities by generating power at precisely timed intervals. In addition, researchers have evaluated movement strategies in military service members and found that those with poor biomechanics
had a higher risk of injury
. Others have used wrist-worn wearables to detect
overuse injuries
in service members. At their core, these innovations all have one goal: to restore and improve human movement.
Motion as a vital sign
We believe that the future of personalized medicine lies in dynamic monitoring. Every step, jump or squat carries information about how the body functions, performs and recovers. With advances in wearable technology, AI and cloud computing, real-time movement monitoring and biofeedback are likely to become a routine part of everyday life.
Imagine an athlete’s shoe that warns them before an injury occurs, clothing for the elderly that detects and prevents a fall before it occurs, or a smartwatch that detects early signs of stroke based on walking patterns. Combining biomechanics, signal processing and data science turns motion into a vital sign, a real-time reflection of your health and well-being.
Matthew Bird has previously received funding from the Department of Defense. The views expressed in this manuscript are those of the author and do not necessarily reflect the views, opinions, or policies of Oklahoma State University.
Azarang Asadi and Collin D. Bowersock do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
