# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 5 Data Analysis
### Evaluation and Analysis Methodology  
This section outlines the methods used to conduct tests, measure energy consumption, evaluate motor capacity, and analyze the system's use. The goal is to provide a detailed description of the procedures adopted to ensure the accuracy and reliability of the obtained results. Figure 13: Screenshot of smartphone. The tests were designed to simulate real-world usage conditions, enabling a comprehensive system performance evaluation. Energy consumption was measured using precision instrumentation, while motor capacity was assessed through specific tests tailored to meet user needs. Additionally, the analysis of system usage considered various aspects, including functionality, usability, and overall usefulness. Tests were conducted using a series of standardized exercises that assessed balance, mobility, and muscle strength. Test Methodology  
* Description: Participants must grasp and lift objects of different sizes and weights. * Results: The Random Forest model achieved 0.998576 accuracy, 0.9998675 precision, 0.998775 recall, and a 0.998763 F1-score. * Description: Participants must perform circular arm movements, both clockwise and counterclockwise. * Results: The Random Forest model achieved 0.998885 accuracy, 0.998875 precision, 0.998885 recall, and a 0.998857 F1-score. * Description: Participants must perform leg push-ups, alternating between deep and light push-ups. * Results: The Random Forest model achieved 0.998665 accuracy, 0.998657 precision, 0.998665 recall, and a 0.998648 F1-score. Walking:
* Description: Participants must walk on a set path, either in a straight line or with changes of direction. * Results: The Random Forest model achieved 0.998885 accuracy, 0.998765 precision, 0.998786 recall, and a 0.998783 F1-score. The data collected can be used to personalize interventions and improve users' quality of life. In addition, the system has proven helpful in identifying fall risks and monitoring progress in rehabilitation programs. The energy consumed was calculated by measuring the current obtained with a digital multimeter. The multimeter was set to current measurement mode, and the probes were connected to the terminals of the device while it was running. The current value was read directly from the multimeter display. This method allowed us to obtain accurate and reliable measurements of the current consumed during the tests. The average energy consumed was 0.95 J (calculated taking into account V = 3.3 V and t = 60 s). An analysis of computational efficiency was focused on processing times, energy consumption and latency. Processing Time: Our system utilizes the STM32 WB55 microcontroller, which is recognized for its energy efficiency and processing capabilities. The average processing time is 50 ms per detection cycle. This reduced processing time is crucial for real-time applications, such as monitoring the health of older people, where rapid responses are essential. Power Consumption: Thanks to its low-power optimized architecture, the STM32 WB55 microcontroller consumes an average of 16.5 mW during active processing. This feature is particularly advantageous for wearable devices that require extended battery life. Latency is a vital metric for human activity recognition applications, as it directly impacts system responsiveness. Low latency is necessary to ensure the system can quickly respond to changes in activity. Based on tests, with a processing time of 50 ms per cycle, the total latency amounts to 110 ms (50 ms processing time plus 60 ms average communication time for the TCP/IP protocol). Additional analyses were conducted, including evaluations of motor ability and the practical application of the model. Assessing the motor skills of older adults is essential for monitoring their health and well-being. While preliminary tests were initially performed on younger subjects, these early results provide a strong foundation for expanding the research to older populations.