###### Abstract
Technology can lead to improvements in the assessment of functional capacity through the monitoring of activities and the behaviors required [5]. In addition, technology facilitates the development of more effective assessments [15; 21] by enabling objective quantification [15], and can even contribute to the early detection of changes in functionality [5]. The emergence of wearable devices in recent years has meant the inclusion of a new powerful and flexible tool for monitoring, storing, and transferring different clinical measurements of the human body: movement, temperature, blood pressure, sleep pattern, location, etc. [22]. Data from wearables can be used in isolation or together with other sensors [23] and are a valid method to characterize activity parameters [24] such as frequency, intensity, or duration [21]. Most of the current technological methods of functional assessment only focus on one functional component [5; 25] and not on the functionality, as this requires the integration of several tools that include more than one domain. Among the works related to the recognition of human actions available in the literature [26; 27], the protocol by [PERSON] et al. [28] seeks to assess the symptoms of [PERSON]'s disease in an everyday environment, using the Sensor Platform for Healthcare in a Residential Environment (SPHERE) technology. By analyzing the metrics obtained by the sensors, its objective is to infer clinical and functional data on [PERSON]'s disease. Our current objectives seek to create and clinically validate a multisensory technological system capable of automatically assessing a subject's level of dependence when performing activities of daily living in a controlled environment that reflects an everyday home. The value of the assessments performed in real environments [5; 17; 24; 29] is that these can eliminate biases [18] and provide a more representative measure of the person's real capacity [24; 29]. In addition, intelligent systems have been shown to reduce the socioeconomic impact of dependent people [21], as they can be more accurate, objective, avoid biases, and can measure several parameters at the same time, which translates into a cost-efficient assessment of the person's health status [24]. The protocol presented in this paper corresponds to a guide for the development of an automatic multisensory system to analyze human activity for functional evaluation, specifically the EYEFUL system. This protocol aims to set out the sequences and actions necessary to develop a final system of these characteristics so that it can be applied to other initiatives in which human functional evaluations by automatic technological systems are required. The operational objectives for the development and clinical validation of systems similar to EYEFUL are as follows:  
* To design and develop a multisensory system suitable for the objective and automatic assessment of functional capacity and determination of dependency status. * To validate the system in clinical settings, ensuring its reliability, sensitivity, and specificity in assessment of functionality during the performance of ADLs. * To provide a practical guide for the development of a multisensory automatic assessment system and the process to clinically validate it. By integrating technological innovation with clinical expertise, the EYEFUL project aims to advance the field of functional evaluation and contribute to the improvement of care for people with different levels of dependency. The description of the protocol to be used aims to allow similar initiatives for automatic functional evaluations to benefit from its structured approach.