# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 1 Introduction  
Lower limb exoskeletons represent a significant advancement in rehabilitation engineering and assistive technology, serving as wearable robotic devices that can augment, restore, or enhance human locomotor function. These devices have become increasingly important in addressing mobility challenges for individuals with spinal cord injuries, stroke, and other neurological conditions [1]. Lower limb exoskeletons can be broadly categorized into three types: medical exoskeletons for rehabilitation and mobility assistance, military exoskeletons for load-carrying and endurance enhancement, and industrial exoskeletons for worker support and injury prevention [2]. In medical applications, these devices demonstrate promise in gait rehabilitation, showing improved outcomes in walking speed, balance, and functional independence among patients [3]. Recent technological developments have led to lighter, more energy-efficient designs incorporating advanced control strategies like EMG-based systems and adaptive algorithms, making them more practical for daily use [4]. The applications of these devices extend beyond medical rehabilitation to include performance enhancement in military operations, support for industrial workers in physically demanding tasks, and assistance for elderly individuals in maintaining mobility and independence [5; 6]. Researchers use different types of vision-based and sensor-based input data for HAR for data collection. Although many research works have talked about the advantages of sensor-based data compared with vision-based data, most state-of-the-art studies still use video cameras (i.e., vision-based) for HAR due to their high accuracy [7]. Vision-based data collection approaches can be classified into two types: videos and images. For the videos in the HAR literature, collected from CCTV or smartphone devices are used, but for vision-based HAR, social media and camera images are used. On the other hand, mobile and wearable body sensors are the two types of sensor-based data sources that are found in the existing literature. Despite such importance, vision-based data are larger in size and take more processing than sensor-based data. One feasible option is increasing data density through sensors; however, while the cost of sensors has come down significantly over time compared to the alternatives (e.g., vision-based data capturing devices), it remains much more expensive. However, the computability of the body sensor system limits the execution of such a complex algorithm. Thus, sensor-based data are somehow advantageous. HAR is the human activity detection and identification challenge in different state-of-the-art techniques [8]. Activities are mainly Activities of Daily Living (ADLs) like walking, jogging, going up or down the stairs or ramp walking. Data availability and its nature are essential parts of a reliable HAR. It has been developed as an essential research domain in ubiquitous computing and human-computer interaction, offering significant implications for healthcare monitoring, assisted living, and rehabilitation applications. The integration of wearable sensors, particularly inertial measurement units (IMUs) comprising accelerometers, gyroscopes, and magnetometers, has revolutionized the ability to accurately detect, classify, and monitor human movements in real-time environments [9]. Modern HAR systems leverage these sensor networks to capture complex motion patterns and physiological signals, enabling the detection of both basic activities (walking, sitting, standing) and more intricate movements, as can be seen in Figure 1[10]. The advancement of machine learning and deep learning techniques has substantially improved the robustness and accuracy of activity recognition systems [11]. The authors of [12] proposed methods for identifying human activities based on a decision tree classifier. However, the classification accuracy rate is considered unsatisfactory. [PERSON] et al.