Load Prediction using an Intraoperative Joint Sensor and Artificial Neural Network

Authors: Al-Nasser, S., Noroozi, S., Haratian, R., Aslani, N. and Harvey, A.

Journal: 19th International Conference on Condition Monitoring and Asset Management, CM 2023

ISBN: 9780903132817

DOI: 10.1784/cm2023.4f2


Predicting load from sensors for intraoperative joint assessment using artificial neural networks (ANNs) is a relatively new and novel technique which can be useful for various other health monitoring applications. In joint replacement surgeries, balancing the load using ANN predicted load measurements can prove useful in cases where a larger sensing area is required, and the geometry of the sensor is more complicated. Once trained ANNs can provide real-time load predictions accurately and precisely, however finding the optimal combination of parameters and hyperparameters to yield the most accurate results requires exploration. In this research, training data was collected by loading the sensor with known weights. Then the dataset was systematically pre-processed and trained using an ANN with different combinations of parameters and hyperparameters to investigate its performance based on different configurations of an ANN. It was found that the Levenberg-Marquardt backpropagation function, with 50 hidden layers and a learning rate of 0.1, yielded the lowest mean square error (MSE) of 0.0034±0.000141, which has also been the best performing training function is other studies. However, the time required to perform this function was significantly longer than for any other combinations (P< 0.001). Additionally, lowering the learning rate for the Levenberg-Marquardt backpropagation function and the BFGS quasi-Newton backpropagation function had no change in the performance. In summation, this study accentuates the importance of a methodical approach in finding the optimal combination of parameters and hyperparameters for ANNs to an achieve adequate performance. It also proves that ANNs can aid in predicting the load from sensors, especially in cases where nonlinearity is observed. Finally, translating the performance of the network to real-time data collection presents new challenges that must be addressed in the future. However, once the ANN is trained and validated, load balancing during joint replacements can be performed with more accuracy and precision.

Source: Scopus