# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 1 Introduction
[13] proposed three distinct classification methods, such as hidden Markov model, support vector machine, and artificial neural network, to categorize body activities. While these methods deliver acceptable performance, they are either constrained in handling significant intraclass variations or hindered by the complexity of adjusting model parameters. Furthermore, the integration of contextual information and multi-modal sensor fusion techniques has enhanced the system's ability to distinguish between similar activities and detect transitions between different movement states, making HAR systems increasingly reliable for real-world applications. Furthermore, [PERSON] et al. have introduced a novel deep learning classifier for gym activities named CNN-ResBiGRU. They collected raw EMG and IMU signals from 10 healthy subjects and achieved a classification accuracy of 97.29% [14]. [PERSON] et al. [15] have introduced a load-free hand rehabilitation system based on virtual reality (VR) made from ionic hydrogels. The system can identify 14 hand gestures with an accuracy of 97.9%. Another activity recognition system is developed by [PERSON] et al. [16]. As they have produced a 5G Narrowband Internet of Things (NB-IoT) system, it is developed for human healthcare data collection, transmission, and reproduction together. The system is integrated with a bionic crack-spring fiber sensor (CSFS) inspired by Cirrus and Spider Structures. This system is characterized by its high sensitivity and long sensing range. Another study is presented by [PERSON] et al. [17], this study investigates the feasibility of estimating the vertical component of the ground reaction force (VGRF) using only EMG signals from the thigh and shank muscles. Two deep learning models were used across three experimental setups. The findings demonstrate that EMG signals can be effectively leveraged to estimate VGRF during walking. [PERSON] et al. [18] has proposed a new phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset was used to evaluate PHASOR. Additionally, data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. Moreover, myoelectric activity of muscles was used to estimate ankle kinematics as proposed by [PERSON] et al. [19]. sEMG signals were recorded for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals in the time domain (TD) and wavelet (WT) and compared. Then, they were used for feeding three machine learning models (artificial neural networks, random forest, and least squares support vector machine (LS-SVM)). However, the usage of such highly complex and high processing-based classifiers is considered a costly process and needs a lot of raw data for developing a dependable classifier. At the same time, accurate classification and signal conditioning of raw and complex data such as EMG and IMU signals are a necessity. Normally, open issues and challenges that still exist in previous works related to HAR can be categorized into five categories: data collection, data pre-processing, hardware/sensors used, complex activity discovery, and non-overlap between activities. The continuous evolution of sensor technology, coupled with sophisticated data processing algorithms, has made HAR an indispensable tool for applications ranging from fall detection in elderly care to performance analysis in sports science [20]. Recently, to achieve more flexibility in data recording, smartphones and wearables such as wristbands  
Figure 1: General description of conventional HAR systems. and smartwatches are integrated with sensors to improve the flexibility of data recording further. Such devices were used for daily health and sport users. These HAR systems can also be used for assistive and biomedical devices. For controlling an exoskeleton or artificial limb, EMGs or IMUs are typically the most convenient sensors that can be used for HAR systems.