Sensors, Vol. 21, Pages 6636: The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait

Sensors, Vol. 21, Pages 6636: The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait Sensors doi: 10.3390/s21196636 Authors: Hartog Harlaar Smit Stumbling during gait is commonly encountered in patients who suffer from mild to serious walking problems, e.g., after stroke, in osteoarthritis, or amputees using a lower leg prosthesis. Instead of self-reporting, an objective assessment of the number of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achieve a safer walking pattern. An easy-to-use wearable might fulfill this need. The goal of the present study was to investigate whether a single inertial measurement unit (IMU) placed at the shank and machine learning algorithms could be used to detect and classify stumbling events in a dataset comprising of a wide variety of daily movements. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Subjects also performed multiple Activities of Daily Living. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research