$\mathrm{^{\circ }}$

$\pm$

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.">

Ankle Kinematics Estimation Using Artificial Neural Network and Multimodal IMU Data (original) (raw)

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