Real-Time Heel Strike Parameter Estimation for FES Triggering

Rahman, Haaris; Kumbla, Ashwij; Megharjun, V N; Talasila, Viswanath

doi:10.1007/978-981-19-2281-7_69

Cited by 2 publications

(4 citation statements)

References 14 publications

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“…Gait kinematics. Given that sensor-aided forecasting of gait kinematic trajectories can improve assistive ambulatory device functionality and user safety 1,[31][32][33][34][35][36][37][38][39] , we next evaluated whether FReT could be used to forecast gait kinematics through the use of wearable motion sensor data. Here, using a single wearable sensor located on the thigh 40 (Fig.…”

Section: Resultsmentioning

confidence: 99%

Time-series forecasting through recurrent topology

Chomiak,

2024

Commun Eng

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Time-series forecasting is a practical goal in many areas of science and engineering. Common approaches for forecasting future events often rely on highly parameterized or black-box models. However, these are associated with a variety of drawbacks including critical model assumptions, uncertainties in their estimated input hyperparameters, and computational cost. All of these can limit model selection and performance. Here, we introduce a learning algorithm that avoids these drawbacks. A variety of data types including chaotic systems, macroeconomic data, wearable sensor recordings, and population dynamics are used to show that Forecasting through Recurrent Topology (FReT) can generate multi-step-ahead forecasts of unseen data. With no free parameters or even a need for computationally costly hyperparameter optimization procedures in high-dimensional parameter space, the simplicity of FReT offers an attractive alternative to complex models where increased model complexity may limit interpretability/explainability and impose unnecessary system-level computational load and power consumption constraints.

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Section: Resultsmentioning

confidence: 99%

Time-series forecasting through recurrent topology

Chomiak,

2024

Commun Eng

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“…Moreover, problems can arise in situations where the motion capture markers used for gait are obscured by arm swinging or hand rails that may be needed for safety in certain clinical populations [ 51 ]. Unlike fixed-motion capture systems, embedded systems have numerous practical and important applications in many areas of rehabilitation science and biomedical engineering [ 1 , 6 , 9 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 28 , 29 ]. For example, as previously noted [ 37 ], the accurate sensor-aided prediction of gait kinematic trajectories can serve as a feedforward mechanism to powered devices instead of predominantly relying on feedback sensors, effectively serving to improve device performance by avoiding alterations in the user’s natural gait trajectories [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

“…While there are intricate complexities related to safety regulatory requirements, user acceptance, as well as device reliability and adaptability that need to be considered, assistive ambulatory devices have the potential to help in such situations and may even reduce the burden on healthcare resources [ 4 ]. Nevertheless, given the importance of mobility assistance for both medical and non-medical end-user applications, there has been increasing interest in designing more effective and intelligent assistive ambulatory technologies [ 1 , 4 , 5 , 6 , 8 , 9 , 11 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ].…”

Section: Related Workmentioning

confidence: 99%

“…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 ]. However, while lower-limb kinematic trajectory prediction can be used to solve numerous problems facing lower-limb robotics, a significant limiting factor for the implementation of accurate gait forecasting in the design of smart devices is the inability of most forecasting models to support continuous model learning [ 1 ].…”

Section: Related Workmentioning

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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Real-Time Heel Strike Parameter Estimation for FES Triggering

Cited by 2 publications

References 14 publications

Time-series forecasting through recurrent topology

Time-series forecasting through recurrent topology

Implementing Gait Kinematic Trajectory Forecasting Models on an Embedded System

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