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Monitoring activities of daily life (ADLs) allows to evaluate health conditions for older adults. However, there are still a limited number of studies on bathroom activities monitoring using a wrist-mounted accelerometer. To fill this gap, in this study, researchers collected data from 15 older adults wearing a wrist-mounted accelerometer. Six bathroom activities, i.e., dressing, undressing, brushing teeth, using toilet, washing face, and washing hands, were investigated. In total, 49.4-hour data for bathroom activities were collected. A hybrid convolutional neural network (CNN) is introduced for bathroom activity recognition. This hybrid CNN model is developed using both hand-crafted and CNN-based features as input. The proposed hybrid CNN model is compared to four machine learning models, i.e., Multilayer Perceptron (MLP), Support Vector Machines (SVM), K-nearest Neighbors (KNN), and Decision Trees (DT), and a conventional CNN model. Based on the classification results of leave-one-subject-out cross-validation (LOSO), the hybrid CNN model outperformed the other models. The hybrid CNN model is also tested based on a transfer learning method. As a calibration step based on LOSO, the transfer learning method additionally trains the model with an example of each activity from the test subject. The transfer learning method obtained better classification performance than LOSO. With transfer learning, the f1-score for using toilet was improved from 0.7784 to 0.8437. This study proposes a deep learning model fusing hand-crafted features and CNN-based features. Besides, the transfer learning method offers a way to build subject-dependent models to improve the classification performance.Clinical relevance &#x2013;This provides a model that helps monitoring older adults' bathroom activities using a single wrist-mounted accelerometer.

Bathroom activities monitoring for older adults by a wrist-mounted accelerometer using a hybrid deep learning model