Analysis of Accelerometer Data for Personalised Mood Detection in Activities of Daily Living

This paper proposes a novel approach to identify moods in Activities of Daily Living (ADLs) using accelerometer sensor data from 15 participants over 7 sessions each. Monitoring ADLs and detecting moods are of particular importance due to the potential life-changing consequences. The ADLs considered relate to preparing and drinking a hot beverage, and they were segmented into four sub-activities: (i) entering kitchen, (ii) preparing beverage, (iii) drinking beverage, and (iv) exiting kitchen. The accelerometer was attached to the participants' wrists, and prior to collecting the data, they were asked about their current mood. Two approaches were considered in the analysis according to the moods reported by the participants (happy, calm, tired, stressed, excited, sad, and bored), firstly using all trials, and secondly using a balanced sample of data. A set of statistical, temporal, and spectral features were extracted from acceleration data, and personalised classification models were built and evaluated using the Random Forest algorithm. The experimental results showed that the average F -measure for all personalized classifiers was 0.75
$(\sigma\ 0.20)$
considering all data, and 0.76
$(\sigma\ 0.22)$
using balanced data. The best classification results were obtained with the “preparing” and “drinking” activities, and with the “happy”,”calm”, and “stressed” moods. This suggests that the use of accelerometers, such as those incorporated into smartwatches or activity trackers, may be useful in detecting moods in ADLs.