We have developed an interdisciplinary model based on machine learning to accurately estimate step length. The new model can be integrated into a wearable device that is attached with tape to the lower back and enables continuous monitoring of steps in a patient’s everyday life.
Step length is a very sensitive and non-invasive measure for evaluating a wide variety of conditions and diseases, including aging, deterioration as a result of neurological and neurodegenerative diseases, cognitive decline, Alzheimer’s, Parkinson’s, multiple sclerosis, and more. Today it is common to measure step length using devices found in specialized laboratories and clinics, which are based on cameras and measuring devices like force-sensitive gait mats.
While these tests are accurate, they provide only a snapshot view of a person’s walking that likely does not fully reflect real-world, actual functioning. Daily living walking may be influenced by a patient’s level of fatigue, mood, and medications, for example. Continuous, 24/7 monitoring like that enabled by this new model of step length can capture this real-world walking behaviour.
To solve the problem, we sought to harness IMU (inertial measurement unit) systems, which are light and relatively cheap sensors that are currently installed in every phone and smartwatch, and measure parameters associated with walking. "Previous studies have examined IMU-based wearable devices to assess step length, but these experiments were only performed on healthy subjects without walking difficulties, were based on a small sample size that did not allow for generalization, and the devices themselves were not comfortable to wear and sometimes several sensors were needed.
We sought to develop an efficient and convenient solution that would suit people with walking problems, such as the sick and the elderly, and would allow quantifying and collecting data on step length, throughout the day, in an environment familiar to the patient.
We found that the XGBoost model is the most accurate and is 3.5 times more accurate than the most advanced biomechanical model currently used to estimate step length. For a single step, the average error of our model was 6 cm, compared to 21 cm predicted by the conventional model. When we evaluated an average of 10 steps, we arrived at an error of less than 5 cm, a threshold known in the professional literature as 'the minimum difference that has clinical importance,' which allows identifying a significant improvement or decrease in the subject’s condition. In other words, our model is robust and reliable and can be used to analyze sensor data from subjects, some with walking difficulties, who were not included in the original training set.