Multimodal Monitoring of Activities of Daily Living for Elderly Care

Abstract

In this article, we presented a multimodal approach to monitor older adults’ activities of daily living (ADLs) using the combination of a wearable device and a companion robot. A dynamic Bayesian network (DBN) model was developed for activity recognition, which fuses different data, including location, object, sound event, body action, and time. The walking action is detected as the transition between consecutive activities, which helps capture the inception of activities and save energy on the wearable device. Three tests were conducted to evaluate the proposed approach. First, multiple daily activities were simulated and evaluated the approach based on a public ADL dataset. Second, the proposed approach was tested based on an offline dataset collected in our smart home testbed, which contains images, sound events, motion, and time data. Third, the proposed approach was tested in real time and a web-based interface was developed, which helps caregivers better monitor the ADLs of older adults and provide further assistance. In the offline test and the real-time test, the results show that the system achieved 91% and 93% activity detection ratio, respectively, which significantly outperformed the baseline periodic sampling methods. In addition, the camera and microphone sensor trigger times were reduced from 1537 to 140 and 78, leading to energy reduction of 36.0% and 37.6% on the wearable device, respectively.