Newly Published Gait Analysis Study Demonstrates Excellent Repeatability of Digitsole’s Smart Insole AI-Based Solutions?
A novel study published in the (TBD) issue of Sensors, the leading international,?peer-reviewed, open access journal on the science and technology of sensors – entitled Test-Retest Reliability of PODOSmart® Gait Analysis Insoles – concluded that Digitsole’s PODOSmart®?insoles present excellent repeatability in gait analysis parameters.
Repeatability or test-retest reliability indicates the agreement between multiple assessments of the same measurement, under the same conditions. The importance of a repeatability measurement cannot be underestimated as it determines the device’s overall reliability. The demonstration of this excellent repeatability means that the device can be used by healthcare professionals appropriately and provides the reassurance and confidence that the measurements will be consistent and precise. These results provide strong clinical evidence about the reliability of this innovative gait analysis tool.
The first-generation smart insole PODOSmart® system was first introduced in 2019 as a new tool for gait analysis against high-cost laboratory-based equipment. The PODOSmart®?system measures walking profile and gait variables in real-life conditions.
PODOSmart®?insoles consist of wireless sensors, which can be fitted into any shoe and offer the ability to measure spatial, temporal, and kinematic gait parameters. Both the process to gather the data and the algorithms used for the analyses are patented by Digitsole.
About Digitsole
A digital health company started in 2015 in France; Digitsole is a leader in the e-health space, bringing together digital mobility biomarkers and biomechanical data with clinical expertise to improve the wellbeing of people throughout their lives. Digitsole developed their digital health platform to monitor effortlessly mobility measurements and related pathologies. Digitsole’s mission is to empower mobility for a healthier life, and the company knows real-world data can advance wellness and lead to better health predictions.