# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 4 Proposed Approach for Lower Limb Activity Detection
### Feature Extraction  
Feature extraction is the process of identifying distinctive characteristics in segmented data. By eliminating extraneous noise and highlighting significant elements, feature extraction turns unprocessed signal data into a meaningful representation. A sliding window technique is used to accomplish segmentation.  
There are several methods for identifying important features, including looking at data in the frequency and temporal domains. This method of signal evaluation facilitates the detection of muscle activity. These characteristics facilitate the classification of unclear recordings with poor signal clarity into structured categories. To take advantage of the autocepstrum's built-in noise cancellation, we utilize features that have been analyzed via quefrency analysis. Sometimes, to fully extract qualities from highly variable data--where traits are distributed unevenly across sections, further segmentation is required. After that, several statistical features can be extracted, such as the variance, skewness, kurtosis, mobility, complexity and average of autocepstral peaks [47]. The most significant features that affected the results obtained are summarized in Table 1.  
Figure 12: (**a**) Autocepstral peaks of IMU signals representing ramp walking. (**b**) Segmented autocepstrum of preprocessed raw sensory signals.  
Since this work does not include multimodal signals or sensor fusion, our objective is to decide on the most reliable sensor between sEMG and IMU based on the classification results obtained. In the feature extraction phase, we chose three features, namely, autocepstral peak, skewness and kurtosis of the denoising signals, and we evaluated the average of each feature through all segments. As compared to the work presented in [21], the authors suggested the use of eight handcrafted features for the classification of different human activities, whereas in the proposed manuscript, we utilized only three features to achieve higher classification accuracy.  
Therefore, our general preprocessing approach that is explained in Section 4 can be compared to the signal conditioning process approach in [27]. This approach uses combined wavelet denoising and EEMD to investigate its capability for pattern recognition of human activities. The signals of these muscles were preprocessed using wavelet denoising and EEMD. To denoise the IMFs, hard thresholding was adopted, and denoised signals were segmented with 1024 samples per segment with 50% overlapping windowing. The utilized features are slope sign changes (SSC), root mean square (RMS), and average standard deviation value (ASDV) [48]. Classification is based on SVM to classify between different activities using the Georgia Tech. dataset and is conducted for the following three cases:  
* Case 1: three activities, namely, slow, fast, and normal walking patterns.
* Accuracy obtained: 87.5%.
* Case 2: four activities representing walking on stairs with four different heights: 'Step Height: 4 in', Step Height: 5 in', Step Height: 6 in', Step Height: 6 in', and 'Step Height: 7 in'.
* Accuracy obtained: 89%.
* Case 3: two activities: upstairs and downstairs.
* Accuracy obtained: 93.5%.  
These results, when compared to the ACA-based approach, show that our approach had a better effect on the later classification stage and superior classification results. The details for data classification are shown in the next section.