# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 1 Introduction
But to utilize these two types, typically a complete framework of data collection, preprocessing, feature extraction and activity classification is utilized. For the usage of EMGs, the authors in research [21] proposed a data acquisition system for measuring EMG signals for human lower limb activity recognition. Five leg activities have been accomplished to measure EMG signals from two lower limb muscles to validate the developed hardware. Five subjects were chosen to acquire EMG signals during these activities. The raw EMG signal was first denoised using a hybrid of Wavelet Decomposition with Ensemble Empirical Mode Decomposition (WD-EEMD) approach to classify the recorded EMG dataset. Then, eight time domain (TD) features were extracted using the overlapping windowing technique. Another example of a complete frame for activity recognition is proposed in [22] with IMU sensors. The authors have collected data from four classes of body movement datasets, namely stand-up, sit-down, run, and walk. Wearable inertial measurement unit (IMU) sensors were used for sensing and data sampling of human activity. Then, data pre-processing and feature analysis were performed by PCA and the minimum redundancy-maximum relevance (mRMR) feature selection algorithm. Finally, activity recognition was performed by traditional machine learning, deep neural networks, transfer learning and hyperparameter optimization methods. Hence, in our research, the idea of presenting a complete framework for activity recognition for assistive devices is sought after. The first step is to provide biomechanical data from the HAR sensors. Normally, open-source datasets are used for the verification of classification and signal conditioning techniques. This can be considered the first step to producing real-time experimental hardware for exoskeleton mechanisms. Several open-source datasets were collected for HAR applications. [PERSON] et al. [23] presented a dataset with 15 healthy subjects. They were four females and eleven males with an average age of 24 \(\pm\) 4 years, height of 1.75 \(\pm\) 0.09 m, and mass of 74 \(\pm\) 13 kg. The recorded activities are walking at three different speeds (0.8 m s\({}^{-1}\), 1.2 m s\({}^{-1}\) and 1.6 m s\({}^{-1}\)). A total of approximately 1.5 h of normal walking and 6 h of perturbed walking are included in this dataset. The trials were performed on an R-Mill treadmill, which has dual six-degree-of-freedom force plates and independent belts for each foot. A USB 6255 card was used as a data acquisition unit. Four ADXL330 three-axis accelerometers were used as wearable sensors. An Osprey camera is used as the motion capture system. Another open-source dataset is presented by [PERSON] et al. [24], in which data were selected from 10 healthy subjects. There were seven males and three females. Their biometrics were 25.5 \(\pm\) 2 years; 174 \(\pm\) 12 cm; and 70 \(\pm\) 14 kg for age, height, and weight, respectively. The utilized sensors were sEMG, IMUs and goniometers. The sEMG (DE2.1 Delsys) was fixed on the following seven muscles in each leg: tibialis anterior (TA), soleus (SOL), vastus lateralis (VL), medial gastrocnemius (MG), rectus femoris (RF), semitendinosus (ST), and biceps femoris (BF). These signals were amplified by 1000\(\times\), band-pass filtered between 20 and 450 Hz and sampled at 1 kHz. Additionally, a 6-DOF inertial measurement unit named MPU 9250 IMUs was placed bilaterally on the subjects' thigh (below RF) and shank (adjacent to TA) and sampled at 500 Hz. All data were recorded by a 16-bit DAQ unit. The performed activities were a complete circuit of sitting (S), LW, ascending/descending a ramp with a 10\({}^{\circ}\) slope (RA/RD), standing (St), and ascending/descending a four-step staircase (SA/SD) step-over-step. A largerdataset was presented by [PERSON] et al. (2019), where data were collected from 50 healthy subjects. There were 25 males and 25 females. Their age range, mass and height were 6-72 years, 18.2-110 kg, and 116.6-187.5 cm, respectively.