A Framework for Monitoring Data from a Smart Home Environment

Abstract: This master thesis presents the design and implementation of a framework for monitoringdata related to activities of daily living (ADL) in a smart home environment, conducted for theHuman Health and Activity Laboratory (H2Al) at Luleå University of Technology. The generalaim of such environments is to increase the quality of life by enabling elderly to live longer athome while reducing the consumption of resources necessary. The complexity of collection,filtering and storing of data in smart home environments is however inherent due to oftenmany interworking sensor-systems, which allmay have different APIs and communicationpathways. This means that knowing whether ‘all systems are go’ when for example doing astudy is not easy, especially for persons not trained in data science.This work therefore aim to design and implement a framework for datamonitoring thattargets smart home environments in which activities of daily living are important for analysisof health-related conditions and for the personalised tailoring of interventions. The frameworkprimarily collects data from four selected systems, that for example track the position andmovements of a person. The data is stored in a database and visualised on a website toallow for monitoring of individual sensor data being collected. The framework was validatedtogether with a occupational therapist through a proof-of-concept trial in the Human Healthand Activity Laboratory, for which healthy subjects conducted a typical test (making a salad)used when assessing human performance.In conclusion, the developed framework works as expected, collecting data frommanysensor systems and storing the data in a common format, while the visualisation on a websiteis perceived as giving an easy overview of monitored data. Additional data can easily be addedto the framework and other processes beyond monitoring can be linked to the data, suchas further data refinement and algorithms for activity recognition (possibly using machinelearning techniques). Future work include to better distinguish data from multiple occupants,develop themanagement of synchronous and asynchronous data, and refine the web interfacefor additional simplicity