Abstract
Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.
Highlights
Hip osteoarthritis (OA) patients exhibit changes in kinematics and kinetics that affect the contact forces of the hip and knee joints during walking and daily activities
The linear model outperforms the baseline for the hip joint loading
This work presented a machine learning pipeline to estimate the hip and knee joint impulse based on a mobile phone
Summary
Hip osteoarthritis (OA) patients exhibit changes in kinematics and kinetics that affect the contact forces of the hip and knee joints during walking and daily activities. Phone-Based Joint Loading Estimation measured across different exercises can serve as an indication for the number of exercise repetitions that the patient needs to complete when rehabilitating after hip arthroplasty surgery. Despite the importance of joint loading monitoring, it is difficult to systematically and widely measure joint loading in a clinical environment. Acquiring these measurements requires a lab environment consisting of optoelectronic cameras and ground reaction force plates. In order to calculate joint contact forces, one would need to use a musculoskeletal modeling workflow, which requires expert knowledge
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have