New Israeli Technology Advances Step Length Monitoring for Neurological Disorder Care

In a groundbreaking development, Israeli researchers have pioneered a new method to track step length with remarkable precision, offering a beacon of hope for those with neurological diseases and age-related conditions. This innovative approach, developed at Tel Aviv University (TAU) and the Tel Aviv Sourasky Medical Center - Ichilov Hospital, utilizes a lightweight sensor and an advanced algorithm to provide continuous, accurate measurements of step length.

Step length, the distance between the heel of one foot and the heel of the other during walking, is a crucial indicator of various health conditions. According to the researchers, it is a sensitive and non-invasive marker for aging, cognitive decline, and numerous neurological diseases such as Parkinson’s, Alzheimer’s, and multiple sclerosis.

“Our model enables continuous monitoring of this key aspect of a patient’s condition in daily life,” the researchers stated, emphasizing the practical benefits of their new approach.

Recently published in the peer-reviewed journal Digital Medicine, the study highlights that this novel model is nearly four times more accurate than existing biomechanical methods. Professor Jeffrey Hausdorff of TAU explained, “Current methods require stationary and cumbersome devices found only in specialized clinics. Our model allows for accurate measurement in a patient’s natural environment throughout the day using a wearable sensor.”

The team employed inertial measurement unit (IMU) systems, similar to those in smartphones and smartwatches, to develop their model. These sensors measure various walking parameters and are both lightweight and affordable. While previous studies have used IMU-based devices, they were limited to healthy subjects and small sample sizes.

Prof. Neta Rabin, an expert in machine learning at TAU, shared, “We aimed to create an efficient and convenient solution suitable for people with walking difficulties, such as the sick and elderly. Our goal was to develop an algorithm that could accurately translate IMU data into step length measurements, integrated into a wearable device.”

The algorithm's development included analyzing gait data from IMU sensors and conventional step length data from a diverse group of 472 subjects, encompassing individuals with Parkinson’s, mild cognitive impairment, multiple sclerosis, as well as healthy elderly and young adults. This comprehensive dataset, consisting of 83,569 steps, was essential for training the machine learning models.

“We discovered that the XGBoost model was the most accurate, being 3.5 times more precise than the current advanced biomechanical models,” said graduate student Assaf Zadka. “For a single step, the average error of our model was 6 cm, compared to 21 cm with the conventional model. When averaging 10 steps, the error reduced to less than 5 cm, a clinically significant threshold.”

Looking ahead, the team is exploring the integration of similar models into smartwatches, aiming to enhance comfort and usability for patients.

This innovative Israeli breakthrough in step length monitoring underscores the nation's commitment to improving health outcomes through advanced technology and research. As we continue to support and celebrate such achievements, let's share this story to inspire and inform others about the remarkable progress being made.

Stay informed about the latest advancements in health and technology by subscribing to our newsletter.