Interactive Control of Lower Limb Exoskeleton Robots: A Review

Zhang, Yue-Peng; Cao, Guang-Zhong; Li, Ling-Long; Diao, Dong-Feng

doi:10.1109/jsen.2024.3352005

Cited by 8 publications

(4 citation statements)

References 190 publications

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“…These would need to be evaluated to determine the applicability of this approach in a wider context and to understand its potential application to fall prevention. Finally, we did not evaluate the integration of multi-source information for motion intention [ 2 , 3 ]. However, FReT can be used for decoding multidimensional systems and predicting multidimensional signal topology [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…Walking trajectory-tracking controllers, characterized by the sensor-aided forecasting of gait kinematic trajectories, represent the next generation in the design of assistive ambulatory devices. This technology aims to achieve natural, stable, and accurate interactive control with respect to human motion intention [ 3 ]. It can be used to reduce injury to users as well as compensate for delays in the response time of more complex control systems [ 1 , 3 , 17 , 18 , 22 , 23 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

“…This technology aims to achieve natural, stable, and accurate interactive control with respect to human motion intention [ 3 ]. It can be used to reduce injury to users as well as compensate for delays in the response time of more complex control systems [ 1 , 3 , 17 , 18 , 22 , 23 , 30 ]. Forecasting gait kinematics can also enable targeted functional electrical stimulation at specific points in the gait cycle, leading to more effective rehabilitation therapy, and may even minimize the risk of falling by detecting deviations in the anticipated gait trajectory [ 1 , 23 , 28 , 29 ].…”

Section: Related Workmentioning

confidence: 99%

“…The advancement in sensor-aided gait kinematic forecasting methods that can incorporate ongoing user-specific sensor signal information on an embedded system could have a significant impact on the design of intelligent lower-limb assistive devices [ 1 ]. Unlike fixed-motion capture systems, embedded systems offer a low-cost approach for the collection of high-quality, multi-source data for motion control purposes [ 2 , 3 ]. Moreover, the portability of wearable embedded systems has numerous practical applications for human–robot interactions to enable more efficient integration of a user’s natural body motion with an assistive device [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Shayne,

Molina,

et al. 2024

Sensors

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Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices.

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Section: Discussionmentioning

confidence: 99%