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    Efficiency of deep neural networks for joint angle modeling in digital gait assessment
    (Springer Open, 2021) Alcaraz, Javier Conte; Moghaddamnia, Sanam; Peissig, Jürgen
    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.
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    Monitoring the rehabilitation progress using a DCNN and kinematic data for digital healthcare
    (Institute of Electrical and Electronics Engineers, 2021) Alcaraz, Javier Conte; Moghaddamnia, Sanam; Penner, Maxim; Peissig, Jürgen
    Monitoring the progress of patients during the rehabilitation process after an operation is beneficial for adjusting care and medical treatment in order to improve the patient's quality of life. The supervised methods used for this in the literature need data labeling, which is a time and cost-intensive procedure. In this paper, we propose Deep Convolutional Neural Network (DCNN) for monitoring the progress of the rehabilitation, utilizing the kinematic data from a Wearable Sensor System (WSS). The WSS provides three-dimensional linear acceleration and angular velocity from multiple body parts such as the lower back and lower limbs during walking at any speed on level ground. Twelve patients with hip unilateral arthroplasty completed two weeks of gait training after the operation. The classification results of different Inertial Measurement Unit (IMU) placements revealed that the IMU placed at thigh achieved the highest accuracy. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring. This approach provides an objective and evidence-based way of understanding clinically important changes in human movement patterns in response to exercise therapy.