# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 1 Introduction  
Life expectancy has increased due to advancements in the medical field and the evolution of technological solutions that support medicine. These advancements include diagnostic imaging [1], analyses of biomedical signals [2], the use of artificial intelligence to read medical records and formulate therapeutic plans [3], virtual reality for training and practicing surgical procedures [4], health trackers [5], augmented reality [6], and emotional state recognition through facial expressions [7]. The rapid increase in the aging population leads to challenges such as cognitive impairment and severe health conditions, negatively affecting independence, quality of life, and the sustainability of healthcare systems [8]. Several additional factors must be considered, including the shift in the traditional family model (where women typically cared for older adults), reduced opportunities for socialization, cuts in financial resources for the healthcare system, and a lack of an adequate social assistance system.  
An effective home care system can mitigate these issues by reducing public spending and improving quality of life. Digital technologies powered by artificial intelligence can create innovative solutions that enhance intelligent home care systems for older adults. These systems can monitor vital signs and daily activities [9] and detect critical situations that require immediate intervention. Recognizing daily activities is crucial for maintaining a healthy lifestyle, supporting patient rehabilitation, and identifying changes in older adults' activities that may indicate serious health problems.  
Task identification is achieved using artificial intelligence that analyzes activity data from various sources, including wearable sensors, smartphones [10], photographic devices, and CCTV cameras [11]. Moreover, since many older adults experience social isolation, digital assistive technologies can foster connections with family members and other stakeholders, enhancing their quality of life.  
In this context, an IoT application based on MEMS sensors integrated with a state-of-the-art microcontroller (STM55 WB) was developed for the recognition of movements such as grasping, circular movement, walking and leg flexion. The collected data are processed using machine learning algorithms and transmitted to a monitoring system server. The console's real-time visualization of detected movements enables healthcare professionals to analyze movement trends and gain valuable insights into the motor status of elderly individuals. This information allows for appropriate therapeutic actions.  
This system aims to enhance the quality of life for older adults by providing continuous support and reducing the need for hospital interventions. Additionally, it is designed to be energy-efficient and cost-effective, promoting its sustainability for widespread adoption. Below, we outline the system's implementation steps, from hardware configuration to data analysis and result visualization. This article is organized as follows: an introduction outlining the objectives and significance of integrated digital solutions for the care of older adults; Section 2, which describes the technologies used in healthcare systems, focusing on IoT wearable devices based on MEMS sensors for movement recognition; Section 3, which includes various testimonies related to the study; Section 4, detailing the methodology for analyzing movements (grasping, arm circular motion, leg flexion, and walking) and implementing the continuous monitoring system; Section 5, presenting the results of the study and highlighting the model's accuracy in recognizing specific movements and assessing motor skills; Section 6, in which we analyze the qualitative aspects of the methodology and the technology's acceptability and usability based on the results; and finally, the Section 7 summarizes the main findings of the study and discusses future implications for technology in enhancing the quality of life and independence of older adults.