# Human Activity Recognition (HAR) in Healthcare
## 2 An Overview of Published Articles
The neural network used was of the multidimensional convolutional type. The model achieved a 90% or higher performance for all 18 classes of physical activity examined. This model's HAR performance was superior to previous studies' corresponding performance. [PERSON] et al. (contribution 6) experimented with a model for the recognition of human activity independent of the orientation of the worn device that classified five predefined activities within a range of actions that could occur in a clinical setting. Their proposal stems from the study of how changes in sensor orientation affect the classification of deep learning (DL) human activity recognition (HAR) targeting activities such as slow and assisted walking and wheelchair use. The HAR model is orientation-agnostic, uses data augmentation, and is trained with acceleration measurements recorded from five sensor positions on the participant's trunk. The wearable sensor data augmentation approach, first used by [PERSON] et al. [15], positively affects time-series computing and potentially improves data-driven tasks such as HAR. They used two datasets. The first is the Wearing Position Study (WPS) acquired at Philips Research Laboratories (2022). It contains three-axis acceleration measurements from nineteen healthy volunteers, comprising ten males and nine females. The second is the Simulated Hospital Study (SHS) acquired at Philips Research Laboratories (2019). It includes ten healthy male and ten female volunteers. Five GENEActive (GA) sensors were used for monitoring: two in contact with the skin, two dangling from the neck, and one in the pocket of the clinical gown. The implemented HAR model is a modified version of the DNN proposed by [PERSON] et al. [16]. The main difference is replacing the long short-time memory layer with a convolutional layer. This change in architecture was introduced to simplify the model and did not generate significantly different results from the previous DNN. The performance achieved by the two sets was evaluated to choose the number of augmented rotation intervals to be applied to the training data. The first set consisted of seven rotations between 0 and 90 degrees, while the second set consisted of seven rotations between 0 and 180 degrees. In light of this preliminary analysis, the final augmentation settings for the augmented model's training set consisted of ten rotations from 0 to 180, with a 20-degree pitch on the frontal, longitudinal, and sagittal axes separately. Cross-validation was used five times to train both the base and augmented model. The cross-validation performance was used to evaluate the augmentation approach (i.e., the range of rotations) and the effect of rotation on the baseline model. The control data results confirmed the augmented model's good performance obtained during cross-validation. Testing showed that as the data increased, the model could learn additional configurations not provided by the initial dataset. Adherence to cardiac rehabilitation does not currently produce the expected results, negatively affecting the health status of patients and the use of available resources. To improve this trend, [PERSON] et al. (contribution 7) set up a study based on machine learning techniques to predict the adherence of patients with cardiovascular disorders to a six-month home cardiac telerehabilitation program. Their approach is based on the use of clinical information available before the start of a program and behavioral and cardiovascular fitness characteristics acquired during the preliminary phase of familiarization with the program. As a first step, the methodology applied involves classifying patients into different clusters. Hierarchical clustering, an algorithm that groups objects with similar characteristics in a tree hierarchy, was used for classification. The baseline data led to the formation of three groupsof patients: an active, low-risk patient group, sedentary, high-risk patients, and a group of patients at high cardiovascular risk but who are fit and motivated.