Gait reference trajectory generation at different walking speeds using LSTM and CNN

• Published in: Multimedia tools and applications Vol. 82; no. 21; pp. 33401 - 33419
• Main Authors: Semwal, Vijay Bhaskar, Jain, Rahul, Maheshwari, Pushkar, Khatwani, Saksham
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2023; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Customization; Data Structures and Information Theory; Exoskeletons; Gait; Joints (anatomy); Machine learning; Multimedia Information Systems; Prostheses; Rehabilitation; Rehabilitation robots; Robot dynamics; Robots; Special Purpose and Application-Based Systems; Track 2: Medical Applications of Multimedia; Trajectories; Walking; CNN; LSTM; Personalized gait reference trajectory generation; Rehabilitation robots; Biped robots; GRU; Human gait analysis
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-14733-2

Rehabilitation robots are gaining significant popularity for impaired gait rehabilitation. However, to make the recovering individual feel natural while walking and restore their original gait pattern, adapting the rehabilitation system according to the individual’s need and walking characteristics becomes imperative. In this paper, we have compared four deep learning models for their ability to generate a personalized gait trajectory at different gait speeds. The first three models are primitive and are the basic implementations of long short term memory (LSTM), convolutional neural network (CNN) and gated recurrent unit (GRU). The fourth model is our proposed model, which is a sequential combination of LSTM and CNN. We considered hip, knee and ankle joints data as human gait is represented as the joint angle trajectories of these joints in the sagittal plane. We trained these models on a benchmark public human walking dataset consisting of treadmill walking data of 42 healthy individuals at eight different walking speeds. Anthropometric and demographic data along with gait speeds were given as input to the models. Our proposed LSTM-CNN sequential model is able to generate stable gait trajectories in the speed range of 0.49-1.76 m/s with a high correlation of 0.98 between the actual and the predicted trajectories, and an R 2 Score of 0.94 is obtained. This work can be utilized for providing personalized gait reference trajectories for the rehabilitation of amputees and stroke patients using rehabilitation systems such as exoskeleton robots and prosthetic legs. Also, this work can be utilized for generating stable walking trajectories for bipedal robots.