# Shoulder-Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration
## 2 COANN Network Optimization Algorithm Architecture
### COANN Network Optimization Algorithm
The three muscles that were selected for study were muscles with more pronounced force generation in upper-limb movements: biceps, brachialis, and triceps brachii (Figure 3). Figure 2: Surface EMG signal acquisition equipment. ## 4 Acquisition and Processing of Multi-Information Fusion Signals

The multi-modal fusion strategy (sEMG + joint angles) proposed in this study implements complementary capture of muscular activation states and kinematic information through non-invasive acquisition modalities employing surface electrode patches and inertial measurement units (IMUs). Surface electromyography (sEMG) is widely adopted in joint motion prediction research due to its preemptive detection capability, enabling signal acquisition prior to actual muscle contraction. While sEMG signals are susceptible to noise interference yet effectively reflect neuromuscular intent, angular measurements provide stable kinematic data but lack granularity in force exertion details. Their synergistic integration allows noise filtration in sEMG through kinematic constraints, while compensating for missing activation dynamics in angular signals via muscular activation profiles, thereby enhancing data reliability. (Note: Experimental data in this phase have been derived from healthy participants; the model's performance under pathological conditions requires independent clinical validation). ### Surface EMG Signal Acquisition and Processing

The effective frequency range of surface EMG signals is typically 50-200 Hz [18], but they are susceptible to low-frequency motion artifacts (0-20 Hz) and 50 Hz power line noise during acquisition, resulting in signal baseline drift and periodic noise. A fourth-order Butterworth high-pass filter (cutoff frequency 20 Hz, attenuation slope \(-\)24 dB/octave) was used to filter out the 0-20 Hz low-frequency motion artifacts and the filter amplitude-frequency response curve. The IIR digital trap (center frequency 50 Hz, bandwidth 4 Hz, Q value 12.5) is further designed with the transfer function:

\[H(z)=\frac{1-2\cos(\omega_{0})z^{-1}+z^{-2}}{1-2r\cos(\omega_{0})z^{-1}+r^{2}z ^{-2}}, \tag{8}\]

where \(\omega_{0}=2\pi\times 50/2000\), the damping factor \(r=0.95\), and the sampling rate is 2000 Hz. Following high-pass filtering and notch processing, the signal-to-noise ratio (SNR) was enhanced from 15.2 dB to 28.6 dB, representing an 88.2% improvement. This preprocessing pipeline effectively suppressed power line interference and motion artifacts, establishing a robust data foundation for subsequent analytical procedures. After preliminary filtering, the sEMG signal still had amplitude fluctuations (the standard deviation decreased from 0.32 mV to 0.18 mV of the original signal), which required further smoothing. The full-wave rectification technique was used to nonlinearly

Figure 3: Measurement of surface electromyography signals and joint angles during A3 extension exercise in subjects. (Rose is the large arm, sky blue is the small arm, and the white dotted line is the datum line perpendicular to the floor). transform the filtered signal to eliminate the negative signal component and preserve the muscle activation features. The mathematical expression is:

\[x_{rect}(t)=\Big{|}x_{filtered}(t)\Big{|}, \tag{9}\]

A first-order Butterworth low-pass filter (cutoff frequency 5 Hz, attenuation slope \(-6\) dB/octave) was applied to the rectified signal to further smooth the signal, and the standard deviation of its amplitude fluctuation of the output signal was reduced to 0.08 mV. \[sEMG_{rac}(n)=|sEMG(n)|, \tag{10}\]

where is the original EMG signal and is the EMG signal after full-wave rectification. In order to obtain a smoother surface EMG signal, a first-order low-pass Butterworth filter is used for further processing. \[|H(\omega)|^{2}=\frac{1}{1+\left(\frac{\omega}{\omega_{c}}\right)^{2n}}, \tag{11}\]

where n is the number of filter orders and \(\omega_{c}\) is the cutoff frequency; here the cutoff frequency is set to 5 Hz.