Ankle Kinematics Estimation Using Artificial Neural Network and Multimodal IMU Data.

Inertial measurement units (IMUs) have become attractive for monitoring joint kinematics due to their portability and versatility. However, their limited accuracy, inability to analyze data in real-time, and complex data fusion algorithms requiring precise sensor-to-segment calibrations hinder their clinical and daily use. This paper introduces KEEN (KinEmatics Estimation Network), an innovative framework that exploits lightweight artificial neural networks (ANNs) to provide real-time predictions of multi-plane ankle kinematics using a minimal number of IMUs, without calibration requirements. Five ANN algorithms were developed and evaluated using 42 inputs derived from four IMUs in both intra-subject and inter-subject tasks. Extensive experimental results yielded exciting findings: even a single IMU located at the heel can provide clinically acceptable estimations of ankle kinematics, implying significant potential for cost and energy savings. Statistical analysis demonstrated the superiority of the developed Long Short-Term Memory (LSTM) network over the other models in intra-subject tasks, achieving impressive accuracy (RMSE: 1.88$\mathrm{^{\circ }}$$\pm$0.02$\mathrm{^{\circ }}$, MAE: 1.41$\mathrm{^{\circ }}$$\pm$0.01$\mathrm{^{\circ }}$, and r2 score: 0.93$\pm$0.01), indicating strong generalization within the same subject. In inter-subject tasks, the convolutional neural network (CNN) and the CNN-LSTM models showed comparable performance but statistically outperformed the other models in terms of estimation accuracy across various inputs. When using a single IMU, the CNN model achieved the lowest error (RMSE: 4.13$\mathrm{^{\circ }}$$\pm$0.55$\mathrm{^{\circ }}$, MAE: 3.33$\mathrm{^{\circ }}$$\pm$0.48$\mathrm{^{\circ }}$, and r2 score: 0.50$\pm$0.21), showcasing its effective generalization to new subjects. Furthermore, deploying the CNN into a microcontroller, with a sinlge IMU at the heel, resulted in promising real-time ankle kinematics estimations (RMSE: 3.34$\mathrm{^{\circ }}$$\pm$0.48$\mathrm{^{\circ }}$, MAE: 2.68$\mathrm{^{\circ }}$$\pm$0.46$\mathrm{^{\circ }}$ and r2 score: 0.63$\pm$0.07). Overall, this research highlights the potential of combining IMUs with ANNs as reliable and practical tools for early prevention and rehabilitation of ankle injuries.