# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 6 Discussion
Using several sensors often leads to inconsistent motion observations across devices, resulting in increased costs and complications in application usage, which can be uncomfortable for the wearer. In contrast, this model utilizes a single sensor that has proved efficient during the classification phase, achieving satisfactory accuracy. The reliable data obtained support our choice to use an inertial module as the sole input for the prediction model. Previous studies have successfully employed single sensors in their methodologies [23; 41; 42]. In classifying human activities, the literature reveals numerous studies that employ different prediction models, yielding varying results. The model hypothesized in the article is designed for monitoring the health of the elderly in home or nursing home settings, and the testimonials report varied applications, including physical rehabilitation contexts, health monitoring, and the gesture control of devices. It also focuses on specific movements such as grasping, leg flexion, circular arm movement and walking, while the testimonies examine a different range of activities, including daily and sports movements. For example, [PERSON] et al. [27] analyzed daily, sports, and automotive maintenance activities. This difference may reflect the different needs and objectives of the various studies, as well as the different technologies and methodologies used. Other authors offer an overview of various studies and similar applications using inertial sensors for monitoring human activities. For example, [PERSON] et al. [22] examined the strengths and weaknesses of IMU-based approaches and multi-view images, while [PERSON] et al. [23] demonstrated that it is possible to identify six fundamental human activities with a simple sensor. These testimonials highlight the effectiveness of MEMS sensors in activity monitoring and their applicability in various settings, such as physical rehabilitation and health monitoring. One of the main differences between the hypothetical model and the testimonies concerns the technology used. While the hypothesized model focuses on a specific microcontroller (STM55 WB) with built-in MEMS sensors, the testimonies report a variety of sensors and technologies, including multiple sensors and different data fusion techniques. For example, [PERSON] et al. [22] use multi-view images, while [PERSON] et al. [25] employ wearable sensors that can be recharged via body energy. This variety of approaches can offer different benefits, such as increased accuracy and robustness, but also increased complexity and cost. Another significant difference concerns the artificial intelligence algorithms used. The model hypothesized in the article uses the Random Forest for the classification of activities, while the testimonies vary the algorithms used, including neural networks, hidden Markov models, and other machine learning techniques. For example, [PERSON] et al. [24] apply hidden Markov models, while [PERSON] et al. [25] use Random Forest. This diversity of algorithms can affect the accuracy and robustness of the task recognition system. This study chose to use the Random Forest algorithm as an alternative to the neural networks commonly employed by other researchers. The transformation of accelerometric data into classes that were input into the prediction model demonstrated the efficiency of the Random Forest algorithm. The results indicate that, in most cases, events were accurately identified, with performance exceeding that of previous studies using neural networks. The validity of the Random Forest algorithm as a predictive model has been established in earlier experiments, where it was compared with various neural networks [43; 44]. Random Forest enhances classification outcomes by randomizing data. Shuffling the order of the data, a valuable preprocessing step in machine learning offers several benefits for model training.