## 5 Discussion and Conclusions
For future studies on the use of \"fitness exercise\" features that also aim to measure the quality of the interaction, a 3D camera system for gesture recognition, such as the Kinect or Orbbec sensor [68], may turn out to be useful. Such changes in technologies could easily be addressed by the usage measurement framework as it allows for the adoption of additional data sources and usage measures with respect to the data of the new sensor. In terms of insights gained by evaluating an AAL system's use, we observed that a significant share of the trial participants had used CARIMO for eight months on a regular basis. The goal of usingCARIMO to keep care-dependent people physically and mentally active was thus achieved. This seems remarkable given the vulnerable target group and their restricted experience with ICT-based solutions. In addition, we learned that incentives, such as new games (introduced in month 6), increased the usage during the field trial. At the end of the field trial, however, for both body- and mind-related features, the usage decreased in Austria and Italy, which may have been due to trial participants preparing themselves for the end of the field trial or due to users who just lost interest in the system over time. The application of \(RR_{v2}\) (only including frequent and regular users) on mind-related features showed that only considering \(RR_{v1}\) could be misleading due to the high proportion of infrequent users in Italy. If the proportion of infrequent users is high, the calculation of \(RR_{v2}\) allows for better insight into the system's use. Compared to existing work, the presented framework was applied to an AAL project that involved many users over a longer period of time [14]. As AAL systems normally consist of more than one device, the framework foresees the possibility to integrate more than one device [17]. In its first application on CARIMO, it was applied to two devices: a tablet and a fitness tracker. This study has some limitations. First, as detailed above, the definition of an appropriate measure for the usage of an app or feature that neither overestimates nor underestimates the use is not always trivial. In particular, features that require users to engage in activities that are encouraged by an app but cannot be further tracked, e.g., fitness exercises on a smartphone or tablet app, rely on proxy measures, which may only approximate the actual usage as intended by the developers of an app. If actual tracking is the main aim, then other technologies, such as 3D cameras or reliable body sensors, may be useful for future work given that users' privacy can still be guaranteed. Second, by using the proposed usage measurement framework, we were able to assess which features were used, how often, when, and by whom. We were not able to make statements on how or why the system was used. To learn more about the characteristics of users in AAL projects, future work could aim to combine logging data with survey data. Finally, while the usage data evaluation framework was designed to guide systematic, comparable, and reproducible usage data evaluation in the AAL field, the general applicability of the framework has yet to be validated. Conceptualization, methodology, validation, formal analysis: [PERSON] and B.T. ; software and data curation: H.R. ; writing--original draft preparation: [PERSON] supported by B.T. ; writing--review and editing: [PERSON] and H.R. ; project administration: [PERSON]; funding acquisition: [PERSON] and B.T. All authors have read and agreed to the published version of the manuscript. This work was supported by the Active Assisted Living Programme grant number AAL 2014-1-021. It received funding from the Active Assisted Living Programme, cofounded by the European Commission, National Funding Authorities of Austria, Italy, and Switzerland, and the individual project partners. Collaborating partners in the CiM project were Salzburg Research Forschungsgesellschaft m.b.H.