Monitoring behavioral symptoms of dementia using activity trackers.

Tertiary disease prevention for dementia focuses on improving the quality of life of the patient. The quality of life of people with dementia (PwD) and their caregivers is hampered by the presence of behavioral and psychological symptoms of dementia (BPSD), such as anxiety and depression. Non-pharmacological interventions have proved useful in dealing with these symptoms. However, while most PwD exhibit BPSD, their manifestation (in frequency, intensity and type) varies widely among patients, thus the need to personalize the intervention and its assessment. Traditionally, instruments to measure behavioral symptoms of dementia, such as NPI-NH and CMAI, are used to evaluate these interventions. We propose the use of activity trackers as a complement to monitor behavioral symptoms in dementia research. To illustrate this approach we describe a nine week Cognitive Stimulation Therapy conducted with the assistance of a social robot, in which the ten participants wore an activity tracker. We describe how data gathered from these wearables complements the assessment of traditional behavior assessment instruments with the advantage that this assessment can be conducted continuously and thus be used to tailor the intervention to each PwD.

Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare.

Activity recognition, a key component in pervasive healthcare monitoring, relies on classification algorithms that require labeled data of individuals performing the activity of interest to train accurate models. Labeling data can be performed in a lab setting where an individual enacts the activity under controlled conditions. The ubiquity of mobile and wearable sensors allows the collection of large datasets from individuals performing activities in naturalistic conditions. Gathering accurate data labels for activity recognition is typically an expensive and time-consuming process. In this paper we present two novel approaches for semi-automated online data labeling performed by the individual executing the activity of interest. The approaches have been designed to address two of the limitations of self-annotation: (i) The burden on the user performing and annotating the activity, and (ii) the lack of accuracy due to the user labeling the data minutes or hours after the completion of an activity. The first approach is based on the recognition of subtle finger gestures performed in response to a data-labeling query. The second approach focuses on labeling activities that have an auditory manifestation and uses a classifier to have an initial estimation of the activity, and a conversational agent to ask the participant for clarification or for additional data. Both approaches are described, evaluated in controlled experiments to assess their feasibility and their advantages and limitations are discussed. Results show that while both studies have limitations, they achieve 80% to 90% precision.

Incorporating Conversational Strategies in a Social Robot to Interact with People with Dementia.

BACKGROUND: Socially assistive robots (SARs) have the potential to assist nonpharmacological interventions based on verbal communication to support the care of persons with dementia (PwDs). However, establishing verbal communication with a PwD is challenging. Thus, several authors have proposed strategies to converse with PwDs. While these strategies have proved effective at enhancing communication between PwDs and their caregivers, they have not been used or tested in the domain of human-robot interaction. OBJECTIVES: This study aimed to assess the effectiveness of incorporating conversational strategies proposed in the literature for caregivers, during PwD-robot interactions. METHODS: We conducted a total of 23 group sessions based on music and conversation therapy, where a SAR interacted with 12 PwDs (mean = 80.25 years) diagnosed with mild to moderate-stage dementia. Using a single subject research approach, we designed an AB study to assess the effectiveness of the conversational strategies in the PwD-robot interaction. Our analysis focuses on the direct communication between the PwDs and the robot, and the perceived enjoyment of PwDs. RESULTS: The number of utterances made from a PwD to the robot increased significantly when the conversational strategies were included in the robot. In addition, PwDs engaged in more sustained conversations. Additionally, PwDs enjoyed conversing with the robot Eva, as much as listening to music. These results indicate that the use of these conversational strategies is -effective at increasing the interaction between PwD and a SAR. CONCLUSIONS: PwDs who participated in the study engaged and enjoyed the interaction with the SAR. The results provide evidence of the importance of incorporating appropriate conversational strategies in SARs that support interventions for the care and social stimulation of PwDs.