# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 5 Data Analysis
### Prediction
Unlike traditional models, LSTMs are designed to store relevant information for extended periods, making them ideal for analyzing sequential data such as those from wearable sensors. They can better handle time variations in data, improving accuracy in recognizing complex tasks. In addition, their structure reduces the risk of overfitting, ensuring the greater generalization of the model. The LSTM architecture is composed of memory units that can retain information for long periods. Each LSTM unit has three main ports: the input port, the output port, and the forgetfulness port. These ports  
The model showed remarkable performance, with results such as the following (Table 5). Comparing the two models, it is observed that the Random Forest model performed better than the LSTM model. However, the LSTM model demonstrated a strong ability to recognize human activities, especially for movements involving complex timelines. Random Forest's integration with LSTM for human activity recognition can create a more robust, accurate, and adaptable system. This combination leverages the strengths of both models, improving performance and reducing errors, offering a complete solution for human activity recognition. Integration offers numerous advantages, especially when it comes to analyzing timelines. While Random Forest is very effective at providing an accurate classification of static features, it fails to capture temporal dependencies. In contrast, LSTM is designed to manage timelines and capture long-term dependencies in data. This makes it ideal for analyzing continuous and complex movements, improving accuracy in recognizing activities that develop over time. Another aspect to consider is the frequency of data sampling. Random Forest can effectively handle sampled data at different frequencies, providing robust classification. On the other hand, LSTM can leverage high-frequency data to capture fine details of movements, while low-frequency data can be used to reduce noise and improve model stability. The integration of the two models allows these aspects to be balanced, optimizing precision and overall robustness. When it comes to motion characteristics, Random Forest can effectively classify parameters such as acceleration, speed, and rotation. However, LSTM can analyze the timelines of these features, capturing dynamic variations and improving the understanding of complex movements. This combination allows for a more complete and accurate view of human activities. Managing transitions between different tasks is another strength of RF-LSTM integration. Random Forest can provide the accurate classification of individual activities, while LSTM can manage transitions between different activities, capturing time dependencies and improving accuracy in recognizing activities that change over time. This is especially useful for tasks that involve continuous movement and smooth transitions. Additionally, Random Forest can effectively classify tasks of different durations, providing a solid foundation for analysis. LSTM, on the other hand, can handle long-running tasks, capturing time dependencies and improving accuracy in recognizing tasks that develop over time. The integration of the two models allows them to effectively manage tasks of different durations, improving overall accuracy. Finally, noise filtering is a crucial aspect of improving data quality. Random Forest is robust to noisy data and can effectively handle noise in sensory data. LSTM can leverage preprocessing techniques, such as noise filtering, to improve data quality and capture time dependencies. The integration of the two models makes it possible to reduce noise and improve the overall quality of recognition  
In addition to the benefits of integrating the two models, it is important to consider some unique features of Random Forest that make it an excellent choice for asset classification.