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Session: Behavior & Monitoring
Session starts: Thursday 24 January, 13:30
Presentation starts: 13:30
Room: Lecture room 535

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Yiyuan Zhang ()
Ine D'Haeseleer ()
Vero Vanden Abeele ()
Bart Vanrumste ()

Abstract:
Background: Activities of daily living (ADLs) can be monitored to determine the physical health status of older adults. In particular, bathroom activities seem promising, and may be detected via the acoustic system, camera, or internet-of-things (IoTs) sensors, with or without activity monitors (accelerometer, gyroscope, and magnetometer). The aim of the research: In this research, the aim is to test whether it is feasible to monitor bathroom activities only using one tri-axis accelerometer. Methodology: The activities of interest in this research are the common bathroom activities: dressing, undressing, washing hands, washing the face, brushing teeth and toileting. During the experiment, participants wore one Empatica E4 (including a tri-axis accelerometer) on their dominant wrist. Additionally, they were asked to annotate the starting and stopping time of each activity, via an APP. Each participant performed the experiment for two consecutive weeks. The experiment took place in each participant’s own home. Data from 9 participants (aged over 65 years old) were analyzed. The RBF kernel support vector machine (SVM) multi-class classification model was trained using the one-against-one method, with 5-fold cross-validation. The performance of the classification model was trained intra- and inter- subject. Results: 908 activities were successfully recorded: Dressing, undressing, washing face, washing hands, brushing teeth and toileting with fraction of instances of 9.5%, 9.7%, 6.4%, 25.7%, 10.8% and 37.8%, respectively. In total, 54 features were extracted in the time and frequency domain. In the intra-subject, the ratio of training and the testing dataset is 7:3. The average f1-score of each activity over all the subjects ranged from 36.51% (dressing) to 85.26% (toileting), for the test data. In the inter-subject case, the data of 3 subjects were assigned to be the testing data, others as the train data. The f1-score result ranged from 80.4% (washing face) to 89.5% (toileting) for the training data and from 29.4% (dressing) to 55.6% (brushing teeth) for the test data, which illustrates the subject variability of physical activities, especially for non-periodic activities. Conclusion & discussion: The performance of the classification model varies among activities and participants. In future work, we will analyze the misclassification error of each activity and check whether it is a bias or variance problem. Additionally, considering the complexity of some of the activities (e.g. toileting includes actions such as washing hand, dressing, flushing, undressing, etc.) another important step is to firstly classify the actions of the activities. Acknowledgement: This research study was possible with the support of KIC EIT Health funding for GRaCE-AGE.