Abstract
Abnormal behaviour detection in the performance of Activities of Daily Living (ADLs) can be an indicator of a progressive health problem or the occurrence of a hazardous incident. This paper presents an initial sensor fusion approach for data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps that are part of ADLs. An accurate recognition of the main steps involved in ADLs can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment considered is a smart kitchen and the ADLs considered are: (i) preparing and drinking tea, and (ii) preparing and drinking coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The sensor fusion approach presented considers the extraction of the most relevant features of the data collected from the types of sensors used and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial sensor fusion approach presented slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just the wearable data.
Original language | English |
---|---|
Title of host publication | 5th IEEE PerCom Workshop on Pervasive Health Technologies |
Publication status | Accepted/In press - 1 Jan 2020 |
Event | PerHealth 2020: 5th IEEE PerCom Workshop on Pervasive Health Technologies - Austin, United States Duration: 23 Mar 2020 → 27 Mar 2020 |
Conference
Conference | PerHealth 2020 |
---|---|
Country/Territory | United States |
City | Austin |
Period | 23/03/20 → 27/03/20 |