Each person moves in a unique manner, the way you walk can offer valuable insights into your overall well-being. Gait analysis, the particular way you walk, can be influenced by various factors such as illness, injury, genetics or issues with your legs or feet. Abnormal gaits, characterized by irregular movement like dragging feet or waddling can develop due to some of these factors.
Clinical gait analysis is today still often performed by observation, making it inherently subjective. When observing gait, parameters such as stride length, cadence, joint range of motion, and muscle activation patterns are assessed. This can be done for not just runners, but especially for walking gait analysis. Symmetries between left and right sides as well as evaluation of the foot, ankle, knee and hip for proper alignment, mobility and stability are assessed. Abnormalities such as length of stride, heel strike, toe off, pelvic tilt or any limping are looked for as well. Observational gait analysis is still mainstream in part due to its convenience and low cost. But this process lacks reproducibility and precision. Advances in technology is pushing gait analysis towards a more objective process.
Recent advances in mobile applications and wearable sensors aim to provide more measurable and therefore objective quantified data. Apps now exist to use a phone’s accelerometer and gyroscope to monitor parameters like cadence, walk, ratio, ground contact time and estimated calorie burn. These apps also can detect asymmetries in side-to-side gait.
Inertial Measuremenut Units (IMUS)
Wearable sensor systems can be used to track joint kinematics directly. Measuring devices containing accelerometers, gyroscopes and even magnetometers are attached to the body to record motion. A magnetometer measures magnetic field intensity in order to determine orientation and heading directions. In functional gait analysis, magnetometers are often coupled with accelerometers and gyroscopes in compact wearable sensors called inertial measurement units (IMUs). By fusing data from these three sources, IMUs can track body movement.
IMUs can assess metrics like step length, walking speed, pelvic and trunk rotation, and hip circumduction. Circumduction is when one end of a limb (in this case the hip) moves in a circular motion while the other end remains fixed. Technology today has the capability to analyze these factors, put it into a metric format using age-matched expectations to indicate fall risk, movement asymmetry and functional independence.
IMU data can help detect excessive lateral say or uncontrolled twisting which can precede falls in older falls. It also looks at distance between one’s feet, often decreased in cautious gait. Antalgic gait, or abnormal gait due to the result of pain is the most common type of abnormal gait. Antalgic gait can make one limp. Using such data, a therapist can help to identify deficits through strength and balance training to help restore normal motion and reduce injury risk.
Potential for AI and Gait Analysis
While in its infancy, the potential for artificial intelligence (AI) to be applied to gait analysis is vast. AI technology has been shown to be capable to accurately measure 3D joint kinematics from camera images, eliminating the need to wear IMU sensors. Data collected from the AI process can potentially provide a simpler way to monitor gait over a long-term. By using the computer data along with anecdotal data collected from the client, AI has the potential to help earlier interventions and personalize recommendations to tailor and customize an individual’s rehabilitation or fitness goal.
Integrating these emerging technologies into routine healthcare and wellness screening programs could provide major advantages. Subtle gait changes often precede adverse health events or functional decline. Having ongoing quantifiable data on a patient’s baseline mobility can allow one’s care team to track progression over time. Detecting deteriorations sooner allows for potential earlier intervention which can improve outcomes in many conditions.
All of the above is for information only, and is not to be taken as medical advice. Please see your medical doctor for formal advice.
Ongoing gait monitoring through unobtrusive AI systems may ultimately help and motive patients to remain active and independent between clinical visits. Providing interfaces to track one’s progress toward functional and wellness targets can encourage more engagement.
Barry Schustermann
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