Datasets

The recognition of Activities of Daily Living (ADLs) in smart-home environments is crucial for complex health-care systems that continuously monitor the behavior of fragile elderly subjects in their homes. For instance, the sequence of ADLs performed by a subject and their execution modalities may reveal early symptoms of cognitive decline.

A limitation of most of the existing ADLs datasets is that they only include data from single-inhabitant settings, where only one subject is living in the home. This scenario is actually realistic considering the large amount of elderly subjects living alone in their homes. However, multiple subjects may live in the same home (e.g., married couples of elderly subjects, an elderly and her caregiver, a whole family). At the same time, the main publicly available multi-inhabitant datasets considered only environmental sensors for data collection.

MARBLE is a 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 

 

 

Please cite this article if you use MARBLE in your research: 

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.

 

Download the Marble Dataset

 

Multi-inhabitant

Multiple Sensing Sources

Accurate Annotations


MARBLE contains data collected in a simulated smart home, where up to 4 residents perform activities both jointly and independently in different scenarios.

MARBLE includes sixteen hours of ADLs. Twelve volunteers participated in data collection.


MARBLE includes a combination of environmental and wearable sensors data. 

Among environmental sensors, we include magnetic sensors to detect open/close of drawers and doors, mat (pressure) sensors to detect when residents are sitting on chairs/sofa, plug sensors to detect the usage of home appliances.


MARBLE includes the ground truth labels about 13 ADLs and the rooms in which they are performed. The ADLs are answering the phone, clearing the table, cooking, eating, entering home, leaving home, making a phone call, preparing a cold meal, setting up the table, taking medicines, using pc, washing dishes, and watching tv. 

MARBLE also includes the ground truth about the association between each environmental sensors event and the subject that triggered it. In this way, researchers can evaluate the performance of novel data association strategies.