Measuring the number of stumbles during daily life could be an effective way to identify older adults and impaired individuals who are prone to fall. In the absence of compensatory strategies, the lack of ankle dorsiflexion muscles for individuals with a prosthesis is expected to affect MTC, possibly increasing the likelihood of stumbling over an obstacle. Theory predicts that small MTC and larger toe clearance variability increase the probability that the swing foot will contact an unseen obstacle, initiating a stumble. This point in the gait cycle has been referred to as the instant of minimum toe clearance (MTC). During gait, an individual may be particularly susceptible to tripping or stumbling at the instant when the swing foot reaches its peak forward velocity and, simultaneously, the vertical distance between the swing foot and the ground reaches a local minimum. Fall risk is even increased in chronic disorders such as osteoarthritis, stroke, and leg amputees. This work shows that stumble detection and classification based on SVM is accurate and ready to apply in clinical practice.Īmong non-disabled older adults, tripping over an obstacle has consistently been reported as the leading cause of falls, accounting for 33 to 53 percent of all falls. The SVM algorithms were implemented in a user-friendly, freely available, stumble detection app named Stumblemeter. Support Vector Machine (SVM) algorithms were most successful, and could detect and classify stumbles with 100% sensitivity, 100% specificity, and 96.7% accuracy in the independent testing dataset. Trained machine learning algorithms were validated using leave-one-subject-out cross-validation. Subsequently, discriminative features were extracted and fed to train seven different types of machine learning algorithms. In the reduced dataset, time windows were labelled with the aid of video annotation. During data processing, an event-defined window segmentation technique was used to trace high peaks in acceleration that could potentially be stumbles. Subjects also performed multiple Activities of Daily Living. The subjects stumbled a total of 276 times, both using an elevating recovery strategy and a lowering recovery strategy. Ten healthy test subjects were deliberately tripped by an unexpected and unseen obstacle while walking on a treadmill. 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. An easy-to-use wearable might fulfill this need. 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. And these peaks need to be just after the rise.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. To sum up, I want to have one peak for every "red block". In my research I found and tried this : islocalmax(data,'FlatSelection','first'), but it doesn't work well too. ![]() I tried many parameters of findpeaks, like 'MinPeakProminence','Threshold' and 'MinPeakDistance', but the best I obtain is this, with findpeaks(data,'minPeakHeight',4.3,'MinPeakProminence',4) :Īs my peaks in my data are not really flat, I don't always keep the first good ones. Sometimes, the first peak in my "red block" is not at the yellow circles location, like this : I wanted to only keep the first ones (yellow circles). > Here you have my data in red, and the finding peaks in blue with this code : = findpeaks(data,'minPeakHeight',4.3) You will understand my problem with some images : I have data which contains peaks that I want to detect with the function findpeaks from the Signal Processing Toolbox, in Matlab.
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