Developing a method for quantifying hip joint angles and moments during walking using neural networks and wearables

Abstract

Quantifying hip angles/moments during gait is critical for improving hip pathology diagnostic and treatment methods. Recent work has validated approaches combining wearables with artificial neural networks (ANNs) for cheaper, portable hip joint angle/moment computation. This study developed a Wearable-ANN approach for calculating hip joint angles/moments during walking in the sagittal/frontal planes with data from 17 healthy subjects, leveraging one shin-mounted inertial measurement unit (IMU) and a force-measuring insole for data capture. Compared to the benchmark approach, a two hidden layer ANN (n = 5 nodes per layer) achieved an average rRMSE = 15% and R²=0.85 across outputs, subjects and training rounds.

Keywords:

• Hip joint
• wearable
• neural networks
• inertial measurement units
• instrumented insoles

Additional information

Funding

The authors thank the Neukom Institute and the Dartmouth Office of Undergraduate Advising and Research for funding the project.