# Human Activity Recognition (HAR) in Healthcare
## 2 An Overview of Published Articles
The authors believe that combining care with digital app-based tools and support from healthcare professionals can help individuals to effectively achieve a healthy weight. In the ninth paper, [PERSON] et al. (contribution 9) proposed a gait and pose analysis study based on estimating the angle of the lower limb joint from a single inertial sensor. Gait analysis is critical in healthcare; it is mainly adopted for precise patient monitoring, the identification of movement abnormalities, the evaluation of surgical findings, and the detection of osteoarthritis of the knee and hip to diagnose [PERSON]'s disease. Gaits are interpreted through three types of parameters: spatiotemporal (e.g., stride speed and length/stride), kinematic (e.g., hip extension/ flexion), or kinetic parameters (e.g., ground reaction moments and forces). The authors used kinematic parameters, the joint angles of the lower limb, and preferred wearable sensors for data collection. These sensors are preferred to non-wearable ones, which generally consist of optical motion acquisition systems with high position accuracy, as they are expensive and require longer installation times and specific skills. Motion analysis in a real-world environment requires precise and reliable sensors. The investigations identified the Xsens inertial sensors as the most suitable for this purpose. The literature has various testimonies on the number of sensors, their positioning and estimation methods, and the analysis of movement. The authors employed various neural network algorithms to determine the number and placement of sensors for estimating the joint angle of both legs. To calculate the actual values of the lower limb joint angle, seven individual Awinda sensors were mounted on the lower half of the body of each of the sixteen subjects, in particular one on the pelvis at the height of the anterior-superior iliac spine, another on each of the lateral thighs, two more on the upper parts of the tibae and finally two more on the upper anterior parts of the feet. The goal was the estimation of leg kinematics (joint angles) from any of the sensors attached to the body. The authors used four different neural network models for the estimation: long-term bidirectional memory (BLSTM), convolutional neural network, wavelet neural network, and unidirectional LSTM. Two groups of target angles of the leg joint were examined. The first set contained only four corners of the leg joint in the sagittal plane, while the second included six angles of the leg joint in the sagittal plane and two angles of the leg joint in the coronal plane. By evaluating different combinations of networks and datasets, it was found that the BLSTM network was the best performer with both datasets, with an absolute mean error (MAE) of between 3.02\({}^{\circ}\) and 4.33\({}^{\circ}\) for the four dominant angles of the leg joint in the sagittal plane. The results improved with an increased number of sensors and the introduction of biometric information. From the investigation of the placement of the single sensor, it was found that the shin or thigh is the optimal position for estimating the angle of the leg joint. Actual leg movement was compared to a computer-generated simulation of leg joints, which demonstrated the possibility of estimating leg joint angles during walking with a single inertial sensor. [PERSON] et al. (contribution 10) developed an innovative model to detect subjects' emotional health using a self-normalizing neural network (SNN) containing an ensemble layer. In the context of HAR, computer vision technology can be applied to recognize emotional states through facial expressions using facial positions such as the nose, eyes, and lips. The recognition of facial emotions is important because, from the analysis of the face, it is possible to detect the subject's health status, such as anxiety, depression, stress, malaise, and neurodegenerative disorders, making facial diagnosis possible.