Researchers at Tel Aviv University (TAU) and the Ichilov’s Tel Aviv Sourasky Medical Center (Tel Aviv) 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 sensitive measure of a wide range of problems and diseases, from cognitive decline and aging to Parkinson’s,” the researchers say. “The conventional measuring devices that exist today are stationary and cumbersome and are only found in specialized clinics and laboratories. The model we developed enables accurate measurement in a patient’s natural environment throughout the day, using a wearable sensor.”

The study was led by Assaf Zadka, a graduate student in the Department of Biomedical Engineering at TAU; Professor Jeffrey Hausdorff from the Department of Physical Therapy at the Faculty of Medical and Health Sciences and the Sagol School of Neuroscience at TAU, as well as from the Department of Neurology, Tel Aviv Sourasky Medical Center (TASMC); and Professor Neta Rabin from the Department of Industrial Engineering at the Fleischman Faculty of Engineering at TAU. An article describing the research was published on May 25, 2024, in the journal Digital Medicine.

“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,” says Professor Hausdorff, an expert in the fields of walking, aging, and neurology. “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 behavior.”

“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 smart watch, and measure parameters associated with walking,” Professor Rabin, an expert in machine learning, adds. “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.”

The researchers used IMU sensor-based gait data, in addition to step length data measured conventionally in a previous study, from 472 subjects with different conditions, such as Parkinson’s, people with mild cognitive impairment, healthy elderly subjects, as well as younger, healthy adults and people with multiple sclerosis. An accurate and diverse database consisting of 83,569 steps were collected in this way. The researchers used this data and machine learning methods to train a number of computer models that translated the IMU data into an estimate of step length.

To test the robustness of the models, the researchers then determined to what extent the various models could accurately analyze new data that was not used in the training process, an ability known as “generalization.”

“We found that the model called XGBoost is the most accurate and is 3.5 times more accurate than the most advanced biomechanical model currently used to estimate step length,” Zadka says. “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.”

The research was supported by TAU’s Center for AI and Data Science. Also participating in the study were Eran Gazit from TASMC and Professor Anat Mirelman from the Faculty of Medical and Health Sciences and the Sagol School of Neuroscience at TAU and TASMC, as well as researchers from Belgium, England, Italy, Holland, and the USA.