Abstract
There are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient’s physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5–180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual’s daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.
Highlights
The World Health Organization (WHO) estimates that globally 100 M people need assistive products such as prosthetic devices, but up to 80–90% of this requirement is not currently being met [1]
The results of this study are more comparable to the results presented by Redfield et al [6] who tested a threshold-based classifier on laboratory data (96.6% vs. 93%), showing the improved performance of machine learning models when applied to free-living data
This study presents a method for accurately measuring daily postures by using a shank worn accelerometer that can be housed within all lower-limb prosthetic devices
Summary
The World Health Organization (WHO) estimates that globally 100 M people need assistive products such as prosthetic devices, but up to 80–90% of this requirement is not currently being met [1]. Ensuring that the existing services provided are optimised is an important step in meeting these requirements One way this can be achieved is by matching the correct prosthetic device to a user’s needs [2,3]. There are currently limited data on how these devices are used and how they support the functional ability of prosthesis users [3] This information is captured by self-reporting from activity diaries or feedback from focus groups, but these subjective measures can be heavily influenced by social bias and patient recall [4,5]. Unobtrusive size and low cost, body-worn sensors have become a commonly used tool for objectively measuring physical behaviour/activities in free-living environments
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