Fine-grained non-intrusive monitoring of activities of daily living (ADL) enables various smart building applications, including ADL pattern assessments for older adults at risk for loss of safety or independence. We utilize structural vibration sensing and electrical load sensing to acquire multiple fine-grained kitchen activities under a lab structure setting. Each file contains the following values: -RawData: time series of data for each channel (vibration on the table, vibration on the floor, load) -Label: manually fine-grained labels of events -Table: detected events start/stop index for vibration sensor on the table -Floor: detected events start/stop index for vibration sensor on the floor -Load: detected events start/stop index for load sensor Label notation: 1 -- operating the kettle 2 -- kettle on 3 -- operating the microwave 4 -- microwave on 5 -- put things on the stove 6 -- operating with stove 7 -- stove on 8 -- operating vacuum 9 -- sweep floor 10 -- walking/step 11 -- miscellaneous 12 -- synchronization signal (knock on the floor) 13 -- vacant 14 -- microwave door open

{"references": ["Pan, Shijia, Mario Berges, Juleen Rodakowski, Pei Zhang, and Hae Young Noh. \"Fine-grained recognition of activities of daily living through structural vibration and electrical sensing.\" In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 149-158. 2019.", "Pan, Shijia, Mario Berges, Juleen Rodakowski, Pei Zhang, and Hae Young Noh. \"Fine-grained Activity of Daily Living (ADL) Recognition through Heterogeneous Sensing Systems with Complementary Spatiotemporal Characteristics.\" Frontiers in Built Environment 6 (2020): 167."]}

This research was supported in part by Highmark, the NationalScience Foundation (under grants CMMI-1653550), Intel and Google. The views and conclusions contained here are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either express or implied, of CMU, UCM, Stanford, UPitts, NSF, or the U.S. Government or any of its agencies