# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 5 Data Analysis
### Evaluation and Analysis Methodology
Extending its application to real-world environments involving older individuals is essential to fully validate the model, as it is currently regarded as a potential tool. In the context of this study, extending the application to older subjects will allow us to assess how the model responds to age-related factors such as slower movements, difficulty in movement, and tremors, which are commonly seen in conditions like [PERSON]'s disease. Older adults tend to move more slowly due to decreased muscle strength and coordination, which can impact the accuracy of the sensors in detecting and classifying movements. Additionally, difficulty in movement often results in irregular or incomplete motions, leading to potential errors in activity classification. Tremors can introduce noise into sensor data, interfering with the model's ability to recognize tasks accurately. To address these challenges, the model incorporates several features. Standardizing data characteristics ensures that all variables are measured on the same scale, allowing the model to treat data uniformly. This standardization helps the model recognize slower movements such as a leg flexion performed by an older person as valid, rather than classifying them as abnormalities. Figure 14: Statistic features. Data filtering is also crucial for reducing the noise caused by tremors, which are prevalent in older adults. Techniques like the Kalman filter can merge data from multiple sensors and minimize noise. For instance, if an elderly person experiences tremors, the Kalman filter can assist in differentiating genuine movements from the tremors, thereby enhancing the accuracy of task recognition. Furthermore, extracting statistical characteristics allows for the more effective capturing of variations in older adults' movements. Statistical features such as mean, variance, and standard deviation are particularly beneficial. For example, the average acceleration data can indicate the general activity level, while the variance may reveal the erratic movements. By utilizing these features, the model can better adapt to the specific movement patterns of older adults, accurately recognizing activities even when movements are slower or less coordinated. Finally, employing robust models such as Random Forest, which are less prone to overfitting, can significantly enhance the model's ability to generalize to older movements, thereby reducing classification errors. The movement patterns of older adults can vary significantly from one individual to another. Random Forest is a machine learning technique that creates multiple decision trees and combines their outcomes to make a final prediction. This method enhances the model's ability to recognize activities in various conditions accurately. Furthermore, customizing the model can help identify and rectify age-related errors. For instance, if the model detects a specific walking pattern for an older individual, it can adapt to recognize this pattern as expected for that person. This customization is achieved through continuous learning, where the model is regularly updated with new data to enhance its accuracy. Figure 15: Ergonomics and reliability rating. The extension of this model for elderly users has been postponed to a second phase, as it requires a careful and methodical approach, which will take time to define and organize. The key activities include the following:  
1. Obtaining Ethics Committee approval ensures that all activities are conducted ethically;
2. Identifying elderly participants and their available caregivers for involvement in the study;
3. Ensuring that all participants give their informed consent;
4. Involving experts to guarantee the quality and accuracy of evaluations;
5. Conducting tests to assess the cognitive abilities of participants;
6. Developing a detailed protocol to guide all activities;
7. Providing adequate training on the use of the model;
8. Carrying out tests according to the established protocol;
9. Collecting and analyzing the data obtained from the tests;
10.