Abnormal behaviour 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 fusion approach of data collected from ambient (contact and thermal) and wearable (accelerometer) sensors in a smart environment to improve the recognition of the main steps of ADLs. An accurate recognition of these steps can support detecting abnormal behaviour in the form of deviations from the expected steps. The smart environment used is a smart kitchen and the ADLs considered are (i) prepare and drink tea, and (ii) prepare and drink coffee. These ADLs are deemed to have many occurrences during a typical day of a (elderly) person. The fusion approach presented considers the extraction of the most relevant features of the data collected from the two types of sensors (ambient and wearable) and the subsequent data analysis to recognise the main steps involved in the ADLs. Results show that this initial approach slightly improves the recognition of the main steps involved in the ADLs compared to the results obtained with just using data from the wearable sensors.
|Title of host publication||2020 IEEE International Conference on Pervasive Computing and Communications|
|Publication status||Accepted/In press - 6 Jan 2020|
|Event||IEEE International Conference on Pervasive Computing and Communications 2020 - Austin, United States|
Duration: 23 Mar 2020 → 27 Mar 2020
|Conference||IEEE International Conference on Pervasive Computing and Communications 2020|
|Period||23/03/20 → 27/03/20|
Garcia-Constantino, M., Konios, A., Mustafa, M., Nugent, C., & Morrison, G. (Accepted/In press). Ambient and Wearable Sensor Fusion for Abnormal Behaviour Detection in Activities of Daily Living. In 2020 IEEE International Conference on Pervasive Computing and Communications (pp. (In-Press)). IEEE.