# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 6 Conclusions and Future Work  
In this paper, we have proposed an activity recognition framework based on signal segmentation, decomposition, and feature extraction from sEMG and IMU sensors. This approach is applied to two different types of signals extracted from EMG and IMU sensors. This methodology relies heavily on deep signal conditioning of IMU and EMG signals to pave the way for easily implemented machine learning classifiers. Autocepstrum analysis (ACA) was chosen for signal conditioning after several trials with other techniques like WT, EMD and PCA. Three machine learning classifiers were chosen, and they were able to achieve superior accuracy for locomotion activities, particularly in gait rehabilitation applications. The resultant data were assessed by confusion matrices, precision, recall and F-measure indicators. The data were later validated by the K-fold validation technique. The results indicate that the random forest classifier performs better than KNN and neural networks across all muscle groups. As for EMG signals, the most accurate results were obtained from the soleus, gracilis, and vastus medialis muscles, with F-scores of 97.63%, 97.11% and 96.66%, respectively. On the other hand, shank and foot signals achieved the highest the F-scores, with 98.52% and 97.63%, respectively. These findings validate the necessity of sensor-based HAR for rehabilitation robotic devices. Future work will focus on obtaining a new custom dataset for real-time data collection and deep learning-based classification. Further research will include the development of a new data acquisition system with the previously recommended classification framework to be integrated with an actual rehabilitation device to enhance mobility assistance for individuals with neuromuscular impairments. Indeed, high-performance sensors with advanced specifications, such as precise calibration, reliable data transmission, and seamless integration with lower limb rehabilitation devices, must be carefully considered to ensure the accuracy and practicality of sensory data acquisition in real-world applications. Moreover, clinical validation of the acquired data with future rehabilitation devices should be monitored carefully to ensure its effectiveness, safety, and applicability in real-world therapeutic scenarios, particularly for patients with varying levels of mobility impairments. Finally, further investigation of using multimodal sensory data for signal processing and classification is planned. \begin{table}
\begin{tabular}{p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt}} \hline \hline \multicolumn{2}{c}{**Name**} & \multicolumn{2}{c}{**ADLs**} & \multicolumn{2}{c}{**Utilized Dataset**} & \multicolumn{2}{c}{**Utilized Classifiers**} & \multicolumn{2}{c}{**Utilized Sensors**} & \multicolumn{2}{c}{**Accuracy Indicators**} \\ \hline \(\bullet\) & New & \(\bullet\) & Walk & \(\bullet\) & Georgia Tech. & \(\bullet\) & KNN & \(\bullet\) & EMGs & \(\bullet\) & 97.63\% \\  & ACA-based framework & \(\bullet\) & Stairs down & \(\bullet\) & Ramp walk & \(\bullet\) & RF & \(\bullet\) & IMUs & \(\bullet\) & 98.52\% \\  & & \(\bullet\) & Stand & \(\bullet\) & Custom experimental dataset & \(\bullet\) & XGBost & \(\bullet\) & \(\bullet\) & IMUs & \(\bullet\) & 96.03\% \\ \(\bullet\) & [PERSON] et al. [22] & \(\bullet\) & Walk & \(\bullet\) & \(\bullet\) & EMG dataset for lower limb [50] & \(\bullet\) & CNN-based classifiers & \(\bullet\) & EMGs & \(\bullet\) & 99.86\% \\ \(\bullet\) & [PERSON] et al. [49] & \(\bullet\) & Walking circuit & (22 subjects) & \(\bullet\) & LSTM & \(\bullet\) & EMGs & \(\bullet\) & 99.86\% \\ \hline \(\bullet\) & \(\bullet\) & Walk & \(\bullet\) & Georgia Tech. & \(\bullet\) & KNN & \(\bullet\) & IMUs & \(\bullet\) & 90.1\% \\ \(\bullet\) & Stairs down & \(\bullet\) & Ramp walk & (22 subjects) & & \(\bullet\) & classifiers & \(\bullet\) & IMUs & \(\bullet\) & 90.1\% \\ \hline \hline \end{tabular}
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