Hartog, D.D.; Harlaar, J.; Smit, G. The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait. Sensors2021, 21, 6636.
Hartog, D.D.; Harlaar, J.; Smit, G. The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait. Sensors 2021, 21, 6636.
Hartog, D.D.; Harlaar, J.; Smit, G. The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait. Sensors2021, 21, 6636.
Hartog, D.D.; Harlaar, J.; Smit, G. The Stumblemeter: Design and Validation of a System That Detects and Classifies Stumbles during Gait. Sensors 2021, 21, 6636.
Abstract
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 amount of stumbles in daily life would inform clinicians more accurately and enable the evaluation of treatments that aim to achive a safer walking pattern. An easy to use wearable might fullfill 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 which could potentially be stumbles. In the reduced dataset, time windows were labelled with the aid of video annotation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Support Vector Machine (SVM) algorithms were most succesful, and could detect and classify stumbles with 100% sensitivity, 100% specificity and, 96.7% accuracy, in the independent testing dataset. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. This work shows that stumble detection and classification based on SVMs is accurate and ready to apply in clinical practise.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
