Paper Type
Complete
Paper Number
1626
Description
Activity detection is crucial in enabling ambient assisted living (AAL). In this paper, we propose the usage of room-based energy disaggregation for activity detection in AAL environments. Non-intrusive load monitoring (NILM) utilizes the aggregate energy consumption data from households to predict components in the aggregate, enabling more privacy-preserving solutions. Most approaches use NILM while focusing on an appliance level, making them expensive in practice while raising privacy concerns by including more personal usage data. Utilizing a room-based NILM approach, we reduce the personal data while enabling a binary activity estimation across different rooms, which we evaluate on multiple sampling rates. In conclusion, the presented approach contributes to offering support for independent living for older adults or people with special needs. By doing so, it also aligns with the principles of data minimization and privacy protection, focusing on semi-private areas in the households, i.e., kitchen and living room environments.
Recommended Citation
Kohl, Tobias Albert and Lowin, Maximilian, "Privacy by Design: Data-Saving Activity Estimation Using Room-Based Non-Intrusive Load Monitoring for Ambient Assisted Living" (2024). PACIS 2024 Proceedings. 10.
https://aisel.aisnet.org/pacis2024/track01_aibussoc/track01_aibussoc/10
Privacy by Design: Data-Saving Activity Estimation Using Room-Based Non-Intrusive Load Monitoring for Ambient Assisted Living
Activity detection is crucial in enabling ambient assisted living (AAL). In this paper, we propose the usage of room-based energy disaggregation for activity detection in AAL environments. Non-intrusive load monitoring (NILM) utilizes the aggregate energy consumption data from households to predict components in the aggregate, enabling more privacy-preserving solutions. Most approaches use NILM while focusing on an appliance level, making them expensive in practice while raising privacy concerns by including more personal usage data. Utilizing a room-based NILM approach, we reduce the personal data while enabling a binary activity estimation across different rooms, which we evaluate on multiple sampling rates. In conclusion, the presented approach contributes to offering support for independent living for older adults or people with special needs. By doing so, it also aligns with the principles of data minimization and privacy protection, focusing on semi-private areas in the households, i.e., kitchen and living room environments.
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