Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

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...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 24; číslo 8; s. 2649
Hlavní autoři: Shayne, Madina, Molina, Leonardo A., Hu, Bin, Chomiak, Taylor
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 21.04.2024
MDPI
Témata:
Adult
Algorithms
Biomechanical Phenomena - physiology
embedded system
Embedded systems
forecast
Forecasting
Gait
Gait - physiology
Humans
Kinematics
Male
Neural networks
Optimization
sensor
Sensors
wearable
Wearable Electronic Devices
embedded system
wearable
forecast
sensor
gait
ISSN:1424-8220, 1424-8220
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Shrnutí: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s24082649