Scientists from Tel Aviv University in Israel have taken step counting to a new level! By utilizing machine learning, they’ve created an algorithm that analyzes data from a tiny, waterproof sensor worn on the lower back. This impressive algorithm translates sensor readings into highly accurate step length estimates. Even better, this new method is nearly four times more precise than the current standard biomechanical model!
Researchers at Tel Aviv University and the Ichilov’s Tel Aviv Sourasky Medical Center (Tel Aviv) led a multidisciplinary international study in which an innovative model based on machine learning was developed to estimate step length accurately. The new model can be integrated into a wearable device that is attached (with “skin tape”) to the lower back and enables continuous monitoring of steps in a patient’s everyday life.
Will you offer us a hand? Every gift, regardless of size, fuels our future.
Your critical contribution enables us to maintain our independence from shareholders or wealthy owners, allowing us to keep up reporting without bias. It means we can continue to make Jewish Business News available to everyone.
You can support us for as little as $1 via PayPal at [email protected].
Thank you.
“Step length is a sensitive measure of a wide range of problems and diseases, from cognitive decline and aging to Parkinson’s,” explained the researchers. “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.”
So, what is step length?
Prof. Hausdorff, an expert in the fields of walking, aging, and neurology and one of the study’s leaders explained, “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 behavior. ”
To develop the algorithm, 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 data base 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.
Prof. Hausdorff went on to say that their research involved collaboration with international experts across various disciplines and that it yielded promising results: a machine learning model that integrates with a comfortable, wearable sensor. This sensor accurately estimates a patient’s step length during daily activities.
The collected data unlocks possibilities for continuous, remote, and long-term patient monitoring. This approach can even be used in clinical trials to assess medication effectiveness. Building on this success, we’re now exploring the potential of creating similar models using data from smartwatch sensors, offering an even more convenient solution for wearers.
