Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

Abstract

Introduction: Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy.

Methods: Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy.

Results: Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°).

Conclusions: Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.

Keywords: Joint angle; biomechanics; inertial measurement unit; machine learning; neural networks; range of motion; wearable sensor.

Figure 1.

Infra-red markers 1–8 and IMUs 1 and 2 attached with double-sided adhesive tape to anatomical landmarks. Each marker is attached to a battery pack. Only one IMU was used to calculate joint angle, the location dependent on the exercise performed.

Figure 2.

An example of CODA and IMU data synchronised from the peaks of the “kick” angles.

Figure 3.

Flowchart illustrating the process of IMU joint angle calculation. The input label for the training of the models is the raw IMU values, with the output being the joint angle derived from the reference standard CODA.

Figure 4.

Randomly selected sample of one participant illustrating joint angle comparison between reference standard CODA and IMU. RMSE was calculated across every data point in the exercise set sampled at 100 Hz for each participant.