In the same study, it was stated that one of the major challenges in the field of mobile health technology is the discovery and validation of meaningful digital biomarkers, and this is the main aim of the current article. The availability of new technologies, such as embedded sensors in different accessories, and the capability of artificial intelligence to perform complex tasks should be used to improve health outcomes, especially in geriatric populations. One of the most concerning subjects in geriatric medicine is frailty. As [PERSON] et al. [8] have stated, it can be considered synonymous with disability and comorbidity, and it is highly prevalent in old age, conferring a high risk of disability, hospitalization, and mortality. Frailty is classified as a syndrome which can be prevented or treated if is detected in its early stages [9], but it is also an important component in decision making and the effective prevention and treatment of perioperative complications in elderly patients [10]. [PERSON] et al. [6] proposed a phenotype composed of five criteria for determining frailty: low physical activity, weight loss, exhaustion, slowness, and weakness. Multiple research projects on the detection of frailty using technology have been initiated based on [PERSON]'s phenotype [11, 12], while others have developed their own methodology [13]. There remain several research gaps which justify further research in this area:  
* Objective measures: [PERSON] relies on subjective measures, which can lead to inaccurate estimations of frailty. This can be improved by using cutting-edge technologies that are able to measure precisely and overcome human error. * Professional assessment dependency: while most of the criteria require a health care professional's assessment, because of the nature of frailty, smart devices can assist the elderly in a non-intrusive manner, enabling them to more easily provide correct information for further computation and insight extraction. * Ease of use: most projects propose complex sensor setups for data collection, and these may be difficult for elderly patients to use. * Personalization: A new dimension should be taken into consideration, i.e., assessing the dynamic nature of frailty. For instance, various factors, such as changes in social environment, can interfere with systems that have a predefined baseline for health conditions and therefore lack personalization, and this can lead to false outcomes. The current work is part of a Eureka European funded project ([[https://cinnamonproject.eu/en/home-page/](https://cinnamonproject.eu/en/home-page/)]([https://cinnamonproject.eu/en/home-page/](https://cinnamonproject.eu/en/home-page/)), accessed on 11 June 2023) and is intended to develop a system able to detect early signs of frailty in elderly people. The main component of this system will be an affordable commercial off-the-shelf (COTS) product. The project has the following objectives:  
Research objective 1: conduct research to determine the COTS product and sensors necessary to obtain relevant insights concerning frailty based on an already validated phenotype;  
Research objective 2: develop and administer a questionnaire to determine the functional and non-functional requirements that the elderly and their caregivers have of the system;  
Research objective 3: design, develop, and test a prototype that can meet the initial requirements and record and process raw sensor data;  
Research objective 4: assess insights related to one or more criteria of the selected phenotype based on data recorded by the prototype;  
Research objective 5: conduct a clinical study using data collected from elderly subjects to discover patterns and create an artificial intelligence model that can assess frailty;  
Research objective 6: demonstrate a proof of concept of a system able to identify early signs of frailty via developed software and self-reporting tools and integrated in a master dashboard to alert caregivers to early signs of frailty in monitored elderly patients.