# AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals
## 4 Discussion  
This study explored the prediction of peak RTD from a 3-axial ACC signal during knee extensors explosive MVCs using an ANN prediction model following a supervised learning approach. As hypothesized, the ACC signal demonstrated a reliable source of information to indirectly measure the RTD-based. In fact, our AI prediction model was able to successfully predict the peak RTD values. These preliminary observations highlight the potential use of accelerometers as an alternative for measuring RTD, offering several advantages over traditional methods (e.g., testing logistics, cost). Figure 4: Linear fit and limit of agreements measured vs. predicted peak RTD. (**Left panel**): The scatter plot showing the linear relationship between the measured and predicted peak RTD. The solid line represents the best-fit line described by the equation \(\mathrm{y}=1.03\mathrm{x}+76.06\), indicating a strong linear correlation (\(\mathrm{R^{2}=0.81}\)). The ICC (Intraclass Correlation Coefficient) value of 0.94 signifies excellent correlation between measures. (**Right panel**): The Bland–Altman plot assessing the agreement between the measured and predicted peak RTD. The mean difference is indicated by the solid line (Mean bias = 30.75 Nm/s), with the dashed lines representing the limits of agreement at \(\pm\)1.96 standard deviations, suggesting that most differences fall within these bounds. Figure 3: Measured and predicted peak RTD. Boxplots were used to demonstrate the measured and predicted peak RTD data. The circles represent the individual observed values. The mean and standard deviation are included to aid in interpreting the image. The \(p\)-value obtained from a paired \(t\)-test was not significant. Although RTD is recognized as an important variable regarding neuromuscular function [1; 2] and is highly associated with human performance [14; 15; 16; 22], the need for specialized equipment and personnel remains a barrier to widespread adoption for field-based measures [2; 31]. This limitation extends beyond sports and athletic contexts to clinical settings, where deep neuromuscular assessments could benefit populations such as seniors [40; 41]. On the other hand, accelerometers are widely accessible and cost-effective, making them a practical tool for assessing neuromuscular function in various settings [42]. Wearable sensors have undergone significant technological advancements, resulting in reduced size and power requirements, improved wearability, and enhanced data quality and variety [43]. These advancements have facilitated the application of wearable sensors to address critical clinical challenges affecting human health. In fact, ACC signals have been utilized in a wide application regarding predicting physical activity levels and biomechanical variables through deep learning techniques [44; 45; 46]. In this study, we demonstrated that a relatively simple ANN architecture was able to predict the peak RTD from ACC signals with high precision. Deep learning techniques offer several advantages over traditional statistical approaches when developing prediction models for complex datasets [47]. One key advantage is their ability to automatically learn and extract intricate patterns and features from raw data. Another advantage is the scalability and flexibility of deep learning models [37]. They can effectively handle large volumes of data and capture nonlinear relationships between variables, which may be missed by traditional statistical methods. Additionally, deep learning models are robust to noisy data and can adapt to new data patterns, making them suitable for real-world applications where data may be incomplete or noisy [48]. As mentioned earlier, we selected the peak RTD to analyze the present dataset. The rationale behind this choice is directly related to the onset determination in time-based RTD measurements (e.g., 0-50 ms) [2]. Various approaches have been proposed, including both automatic and visual/manual methods [2].