Sensors, Vol. 24, Pages 2649: Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

Sensors, Vol. 24, Pages 2649: Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System Sensors doi: 10.3390/s24082649 Authors: Madina Shayne Leonardo A. Molina Bin Hu Taylor Chomiak 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, ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research
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