Project Conclusion

During my FMP project, I continued upon the exploration and design criteria that concluded from my Pre-FMP. This project has explored and addressed the design challenges that emerge when implementing a Machine Learning system (AI-Kit) in Smart Home environments.

During the initial phases of the project, the features a potential user would require to interact with such a system were defined. Over the course of several iterations, the defined features were integrated in an interactive prototype used for evaluation in an Expert Panel study. Concluding from the iterations and the findings of the study, two distinct feature modes were defined as crucial to allow the functionality of AI-Kit to be integrated into daily life.

In the Label Monitoring mode, a user is able to provide feedback to AI-Kit while monitoring the overall state of the system. In this mode, the labels detected by AI-Kit are communicated to the user including details on duration and detection certainty. Moreover, a user is able to confirm, flag, edit, and remove (in case of a false positive) labels using the interface. On top of that, users can select a non-detected label (false negative) through various layers, providing more detailed control as the interaction progresses.

The Output Control mode enables users to easily set their output parameters to their preferences. By allocating an RFID sticker to a certain output, users can control their IoT device by tapping the sticker with the designed interface. The functionalities have been integrated into one design (Oci), housing all the required functionalities for a daily life integration of AI-Kit in the home environment. An interactive prototype of the concept has been developed for communication and evaluation. In a brief study, the concept features and their integration have been evaluated hinting towards an improvement in comparison to the previous version (as evaluated during the Expert Panel study). The proposed concept is a significant step in the search of facilitating an easy interaction with AI-Kit, allowing users to design and develop their own Machine Learning Functionality. Future work is required to evaluate the presented features in a contextual deployment, allowing features to be prioritized while moving towards a viable product.

AI-Kit • Oci

February 2020
Eindhoven University of Technology
Individual
Graduation Project (Grade: 8.0)

This project has explored and addressed the design challenges that emerge when implementing a Machine Learning system (AI-Kit) in Smart Home environments.

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