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. 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.Introduction
Methods
In orthopaedics, clinical outcome assessment (COA) is mostly performed by questionnaires which suffer from subjectivity, a ceiling effect and pain dominance. Real life activity monitoring (AM) can objectively assess function and becomes now feasible as technology has become smaller, lighter, cheaper and easier to use. In this study we validated a custom made algorithm based on accelerometry using different orthopaedic patients with the aim to use AM in orthopaedic COA. A small, lightweight 3D-accelerometer taped to the lateral side of the affected upper leg served as the activity monitor. AM algorithms were programmed in Matlab to classify standing, sitting, and walking. For validation a common protocol was used; subjects were asked to perform several tasks for 5 or 10 seconds in a fixed order. An observer noted the starting time of each task using a stopwatch. Accuracy was calculated for the number of bouts per activity as well as total time per activity. 10 Subjects were chosen with different pathologies (e.g. post total knee/hip arthroplasty, osteoarthritis) since the difference in movement dynamics in each pathology poses a challenge to the algorithm.Introduction
Methods
