# Human Activity Recognition (HAR) in Healthcare
## 2 An Overview of Published Articles
In contrast, the proposed hierarchical architecture can split the action window and extract features from the sensor sequence data with finer granularity to recognize the finer action processes effectively. To validate the efficacy of the proposed model, the authors carried out experiments on several public HAR datasets: Opportunity, PAMAP2, USC-HAD, and Skoda. Their model was built using Google's open-source TensorFlow 2.9.0 deep learning framework. The proposed model achieves competitive performance compared to several state-of-the-art approaches. The evaluation results also show that the proposed HMA Conv-LSTM can effectively obtain the temporal context and spatial information from sensor sequence data. Again, [PERSON] et al. (contribution 4) used an innovative approach based on a DL network and the nature of the data. Exploiting the potential offered by WiFi-based detection techniques, they used channel status information (CSI) [14] rather than the received signal strength indicator (RSSI). The authors proposed a hybrid deep learning network called CNN-GRU-AttNet that leverages the strengths of CNN and GRU to extract informative spatio-temporal features from raw CSI data automatically and to efficiently classify tasks. They also integrated an attention mechanism into the network that prioritizes important features and time steps, thereby improving recognition performance. The network consists of five layers: the input layer, two CNN layers, a GRU layer, an attention layer, a fully connected layer, and an output layer. To assess the effectiveness of the proposed model, the authors used two publicly accessible datasets, CSI-HAR and Stan WIFI. They refer to seven activities: walking, running, sitting, lying down, standing up, bending, and falling. Because these datasets did not have predefined training and test sets, they adopted the cross-validation technique five times to evaluate the model's performance. They also performed a comparative evaluation of the performance of five core deep learning models: CNN, LSTM, BiLSTM, GRU, and BiGRU. The results show exceptional efficacy in the classification of HAR activities, superior to the five basic DL models, producing an average accuracy of 99.62%, an accuracy of 99.61%, and an F1 score of 99.61% in all movements. [PERSON] and [PERSON] (contribution 5), aware that some physical activities may include similar features that lead the automatic classification phase to incorrect evaluations, proposed a new approach to improve recognition accuracy. Their proposed method uses a smartphone's three-axis acceleration and gyroscopic data to define activity patterns visually. In particular, the method expands the sensor data into 2D and 3D images. This generates new characteristics of human activities that cannot be detected in one-dimensional data. These new features allow, on the one hand, the recognition of more diverse types of human physical activity and, on the other hand, the identification of unique characteristics among similar types of activities. The raw values from the accelerometer and gyroscope that correspond to the breadth of the continuous data of the activities performed are used to represent 2D image models. Each time-series value is transformed into a luminosity value, obtaining the Brightness Intensity Distribution Model (BIDP) for each physical activity data. Each point is expressed as a distinct brightness value based on the measured value. This type of representation includes areas of intense and low brightness depending on the location of the data waveform that can degrade the model's performance. To overcome this problem, the authors carried out a processing step to generate a standardized visual image. The image data were used in the training phase along with the raw 1D data to increase the precision and accuracy of the HAR. The sensor data from the triaxial accelerometer and gyroscope used in this study came from the \"WISDM Activity and Biometrics for Smartphones and Smartwatches\" published by [PERSON] [14].