# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 6 Discussion  
This paper proposes a model for recognizing human movements developed as part of an assistance service for vulnerable or moderately fragile older adults. The project aims to promote healthy lifestyles and facilitate health monitoring. The system has several key functions, including the following:  
* Motion tracking;
* Data analysis to identify potential anomalous situations. Caregivers guide older adults to perform activities such as grasping, walking, flexing their legs, and making circular arm movements to monitor their motor skills. The chosen technology is user-friendly and space-efficient, which enhances its acceptability among older users. The system is designed to minimize any discomfort elderly individuals may experience while using technology. Furthermore, caregivers play a crucial role in helping older adults overcome their reluctance towards technology by providing practical guidance on how to use these tools. This model has several distinguishing features compared to other studies and applications in human activity recognition. Technology: This system utilizes a multi-protocol dual-core wireless STM55 WB microcontroller, renowned for its energy efficiency and robust processing capabilities. Itis well-suited for developing IoT applications in edge machine learning. It is ideal for wearable devices requiring low power consumption while maintaining high performance. The technology integrates a high-performance 3D digital accelerometer and a 3D digital gyroscope with machine learning cores. This combination effectively captures linear acceleration and rotational movements, enabling the precise detection of specific actions such as grasping, arm circular movements, leg flexion, and walking. The model is optimized to recognize these movements, which are essential for applications in the physical rehabilitation, health monitoring, and gesture control of devices. Methodology: The approach employs Random Forest as a predictive model. Random Forest is advantageous as it can manage large datasets and deliver accurate, robust predictions. Its resistance to overfitting makes it ideal for real-world applications. The model is also scalable, including additional movements or sensors, which enhances its detection and recognition capabilities. This flexibility enables the system to be adapted to meet various needs and applications. Furthermore, the methodology includes a continuous learning algorithm that improves the system's accuracy over time by adapting to the specific movement patterns of individual users. Type of classes used: The movements analyzed signify a significant advancement in monitoring and supporting older adults. This focused approach generates valuable data, enhances quality of life, and facilitates personalized interventions. The system categorizes movements into various classes, such as fine motor skills (e.g., grasping small objects), gross motor skills (e.g., walking), and complex movements (e.g., arm circular movements). This classification allows for a comprehensive assessment of an individual's motor abilities and helps in tailoring specific rehabilitation programs. Features: The device is designed for ease of use and low power consumption. It includes a mode of use that makes it easy for seniors to interact, ensuring they can use the device with minimal assistance. The system's compact design makes it easy to wear and carry, enhancing its convenience for everyday use. In addition, the device offers real-time feedback and alerts, allowing for an immediate response to any anomalies detected in the user's movements. Integration with healthcare applications allows caregivers and healthcare professionals to remotely monitor the health status of the elderly, ensuring timely interventions and continuous support. Many human activity recognition systems rely on multiple accelerometers or various combinations of sensors, which can be cumbersome.