Abstract
Introduction
In orthopaedics, clinical outcome assessment (COA) is still mostly performed by questionnaires which suffer from subjectivity, a ceiling effect and pain dominance. Real life activity monitoring (AM) holds the promise to become the new standard in COA with small light weight and easy to use accelerometers. More and more activities can be identified by algorithms based on accelerometry. The identification of stair climbing for instance is important to assess the participation of patients in normal life after an orthopaedic procedure. In this study we validated a custom made algorithm to distinguish normal gait, ascending and descending stairs on a step by step basis.
Methods
A small, lightweight 3D-accelerometer taped to the lateral side of the affected (patients) or non-dominant (healthy subjects) upper leg served as the activity monitor. 13 Subjects (9 patients, 4 healthy) walked a few steps before descending a flight stairs (20 steps with a 180o turn in the middle), walked some steps more, turned around and ascended the same stairs. Templates (up, down and level) were obtained by averaging and stretching the vertical acceleration in the 4 healthy subjects. Classification parameters (low pass (0.4 Hz) horizontal (front-back) acceleration and the Euclidian distance between the vertical acceleration and each template) were obtained for each step. Accuracy is given by the percentage of correctly classified steps.
Results
In total the subjects took 537 (41+/-8 mean+/-std) steps, 525 of which were correctly identified as step. 12 Steps were not detected, and 2 steps were incorrectly identified as step. Per subject the accuracy of the classification algorithm ranged from 57% to 97%. In only 2 subjects the accuracy was less than 75%, giving an overall accuracy of 85%.
Discussion
In literature algorithms able to identify walking the stairs and normal walking have been reported with an accuracy in the range of 80–95%1,2. Our algorithm falls well within this range, and can be even further improved. The low accuracy in two subjects can be explained by the fact that the sensor was placed more to the front of the leg, which influences the low-pass horizontal acceleration. Using a combination of front-back and left-right acceleration could possibly solve this problem. In the future we are confident to identify also other activities and even distinguish different types of stair climbing (i.e. taking a step with each leg versus only taking steps with the unaffected leg and ‘dragging’ the second leg) and obtain more specific activity profiles to be used in clinical outcome assessment.
