Efficiency of deep neural networks for joint angle modeling in digital gait assessment

Abstract

Reliability and user compliance of the applied sensor system are two key issues of digital healthcare and biomedical informatics. For gait assessment applications, accurate joint angle measurements are important. Inertial measurement units (IMUs) have been used in a variety of applications and can also provide significant information on gait kinematics. However, the nonlinear mechanism of human locomotion results in moderate estimation accuracy of the gait kinematics and thus joint angles. To develop “digital twins” as a digital counterpart of body lower limb joint angles, three-dimensional gait kinematic data were collected. This work investigates the estimation accuracy of different neural networks in modeling lower body joint angles in the sagittal plane using the kinematic records of a single IMU attached to the foot. The evaluation results based on the root mean square error (RMSE) show that long short-term memory (LSTM) networks deliver superior performance in nonlinear modeling of the lower limb joint angles compared to other machine learning (ML) approaches. Accordingly, deep learning based on the LSTM architecture is a promising approach in modeling of gait kinematics using a single IMU, and thus can reduce the required physical IMUs attached on the subject and improve the practical application of the sensor system.

DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner for Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors in Daily Living

“…But in an uncontrolled environment like stair and slope conditions, their predictive model may not perform well. More recently, Alcaraz et al [26] used a single IMU sensor on the foot to estimate kinematics during overground walking. They have achieved an average RMSE of 1.91°, 2.12°and 2.57°for the hip, knee, and ankle joints.…”

DeepBBWAE-Net: A CNN-RNN Based Deep SuperLearner For Estimating Lower Extremity Sagittal Plane Joint Kinematics Using Shoe-Mounted IMU Sensors In Daily Living

“…But in an uncontrolled environment like stair and slope conditions, their predictive model may not perform well as there is significant variability in those conditions. More recently, [22] used a single sensor on the foot to estimate kinematics during overground walking. They used gait cycle normalization, a Hilbert-Huang transformation, to process the data making it difficult for real-time prediction due to the longer computation time.…”

A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

“…The LSTM cell has a more complex structure and was shown to better handle long-term dependencies within the data [33]. LSTM networks have been proposed to solve problems dealing with wearable inertial sensor data, e.g., in the area of odometry [24,25], pedestrian dead reckoning [26], kinematics [15,31,34,35], attitude estimation [32,36], fall risk assessment [30], activity recognition [37,38], among others.…”

“…Many studies propose the use of inertial sensors as an alternative to gold standard solutions [14]. Inertial sensors are cheaper and portable, offering an interesting alternative for the assessment of gait in clinical settings or in daily life [9,15].…”