Deep learning approaches for enhanced lower-limb exoskeleton control: A review

M Belal, N Alsheikh, A Aljarah, I Hussain - IEEE Access, 2024 - ieeexplore.ieee.org
Recent advancements in robotics have pushed the development of active exoskeletons and
orthoses for assistive, augmentative, and rehabilitative purposes. Deep Learning …

Deep learning for quantified gait analysis: a systematic literature review

A Khan, O Galarraga, S Garcia-Salicetti… - IEEE Access, 2024 - ieeexplore.ieee.org
Over the past few years, there has been notable advancement in the field of Quantified Gait
Analysis (QGA), thanks to machine learning techniques. QGA and gait prediction are areas …

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

VB Semwal, R Jain, P Maheshwari… - Multimedia Tools and …, 2023 - Springer
Rehabilitation robots are gaining significant popularity for impaired gait rehabilitation.
However, to make the recovering individual feel natural while walking and restore their …

Lower-limb joint torque prediction using LSTM neural networks and transfer learning

L Zhang, D Soselia, R Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Estimation of joint torque during movement provides important information in several
settings, such as effect of athletes' training or of a medical intervention, or analysis of the …

[HTML][HTML] Federated compressed learning edge computing framework with ensuring data privacy for PM2. 5 prediction in smart city sensing applications

KT Putra, HC Chen, Prayitno, MR Ogiela, CL Chou… - Sensors, 2021 - mdpi.com
The sparse data in PM2. 5 air quality monitoring systems is frequently happened on large-
scale smart city sensing applications, which is collected via massive sensors. Moreover, it …

Deep learning models for stable gait prediction applied to exoskeleton reference trajectories for children with cerebral palsy

R Kolaghassi, G Marcelli, K Sirlantzis - IEEE Access, 2023 - ieeexplore.ieee.org
Gait trajectory prediction models have several applications in exoskeleton control; they can
be used as feed-forward input to low-level controllers and to generate reference/target …

Prediction of gait trajectories based on the Long Short Term Memory neural networks

A Zaroug, A Garofolini, DTH Lai, K Mudie, R Begg - Plos one, 2021 - journals.plos.org
The forecasting of lower limb trajectories can improve the operation of assistive devices and
minimise the risk of trip** and balance loss. The aim of this work was to examine four …

[HTML][HTML] Motion trajectories prediction of lower limb exoskeleton based on long short-term memory (LSTM) networks

B Ren, Z Zhang, C Zhang, S Chen - Actuators, 2022 - mdpi.com
A typical man–machine coupling system could provide the wearer a coordinated and
assisted movement by the lower limb exoskeleton. The process of cooperative movement …

[HTML][HTML] Gait trajectory prediction on an embedded microcontroller using deep learning

M Karakish, MA Fouz, A ELsawaf - Sensors, 2022 - mdpi.com
Achieving a normal gait trajectory for an amputee's active prosthesis is challenging due to its
kinematic complexity. Accordingly, lower limb gait trajectory kinematics and gait phase …

Reservoir computing model for human hand locomotion signal classification

T Witchuda, A Wiranata, S Maeda… - IEEE Access, 2023 - ieeexplore.ieee.org
Human-movement recognition is a novel challenge in soft robotics. In recent years, there
have been several attempts to develop soft wearable devices for supporting human-robot …