Abstract
While the sensor-based recognition of Activities of Daily Living (ADLs) is a well-established research area, few high-quality labeled datasets are available to compare the results of different approaches. This is especially true for multi-inhabitant settings, where multiple residents live in the same home performing both individual and collaborative ADLs. The reference multi-inhabitant datasets consider only environmental sensors data and two residents in the same home. In this paper, we present MARBLE: a novel multi-inhabitant ADLs dataset that combines both smart-watch and environmental sensors data. MARBLE includes sixteen hours of ADLs considering scripted but realistic scenarios where up to four subjects live in the same home environment. Twelve volunteers participated in data collection. We describe MARBLE also providing details on the design of data collection and tools. We also present initial benchmarks of ADLs recognition on MARBLE, obtained by applying state-of-the-art deep learning methods. Our goal is to share the result of a complex and time consuming data acquisition and annotation task, hoping that the challenge of improving the current baselines on MARBLE will contribute to the progress of the research in multi-inhabitant ADLs recognition.
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Notes
- 1.
The dataset can be downloaded here: tinyurl.com/marbledataset.
- 2.
We used Huawei Sport 2 and other brands with similar features.
- 3.
In our experimental setup, we used machine learning methods to analyse RSSI signal of BLE beacons and WiFi APs in order to classify the semantic location of each subject in real-time.
- 4.
We proposed in [3] a data association method evaluated on MARBLE. However, the dataset was not public yet and it was not described in detail.
- 5.
Since the number of measurements in a window may slightly differ from \(L_w\), we interpolate missing values or downsample measurements when needed.
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Arrotta, L., Bettini, C., Civitarese, G. (2022). The MARBLE Dataset: Multi-inhabitant Activities of Daily Living Combining Wearable and Environmental Sensors Data. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_25
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