## 5 Discussion and Conclusions  
AAL projects aim to make a positive change in older people's lives by facilitating access to new technologies. To assess whether the target groups effectively adopt the developed prototypes, automatically recorded usage data are a valuable source of information. However, publications on the actual use of AAL-technologies are rare, as are publications that apply usage data for other types of analysis, such as usability assessments or effectiveness analysis. We thus aimed to support the usage data assessment for AAL solutions by both providing a comprehensive approach for the evaluation and by reporting on the results of applying this approach on a complete and fully operational AAL prototype, called CARIMO. We presented a usage data evaluation framework that offers a systematic, comparable, and reproducible process to improve usage data logging, preparation, and analysis in (longer) field trials of AAL prototypes. In comparison to previous work [8; 14; 17; 18; 36; 37], it covers all steps relevant for a comprehensive usage data assessment and, by offering options for usage data evaluation, allows adjustments according to the research objectives or technical requirements of the AAL solution. Data collection and analysis is guided by a step-by-step process to generate awareness and to prepare for important decisions and point to tasks that may be easily overlooked (e.g., calculating logging capacity and bandwidth, and preparation for ethical clearance). The presented framework thus seeks to support preparing and conducting a systematic usage data measurement for AAL projects. Usage data can be recorded without the knowledge of the users and may thus serve as important information on the actual use of an AAL system. On the other hand, usage data in general have become a \"currency\" in many areas of the ICT economy. Access to many apps and ICT services seem to be for free, but user behavior is being tracked and data are stored or used and sold for purposes that are often not transparent to users. To balance both interests, i.e., generating knowledge and assuring privacy, ethical approval of usage data collection and analysis is important for responsible research in the field of AAL. For this reason, ethical considerations have been included into the usage data evaluation framework. In a first application, the framework's systematics was used to measure the take-up of the AAL prototype CARIMO, an ICT-supported fitness and entertainment program for older, care-dependent people, in an eight months' field trial. In general, logging generates a mass of data, which need to be prepared for a meaningful analysis. \"One-size-fits-all\" definitions do not always work. Thus, we suggested a context-dependent classification scheme. The framework offers options to decide on tools, measures, and groupings depending on the research aims and technologies to be assessed. For example, it proposes both data-driven and concept-driven approaches for usage groupings, with the latter based on expert knowledge and/or previous work. In the case of CARIMO, a concept-driven approach was taken to define user groups for the body-related features of CARIMO as well-accepted globally recommended levels of physical activities were available for the group of older adults. Since there are differences in how people use an app, it is important to define what usage means in a certain context e.g., a person just having a look at fitness exercises versus performing those exercises for several minutes. Such definitions set beforehand may avoid misinterpretations of the data. A particular challenge was to assess the use of the \"fitness exercise\" feature. As there were no technologies (such as 3D cameras) that could ensure that the exercises had actually been performed, we had to rely on a proxy measure. In order to avoid overestimating the use of the \"fitness exercise\" feature by, for example, simply counting the clicks on the training program, we only counted an exercise if a predefined minimal screen time had been exceeded.