Exploring Effective Sensing Indicators of Loneliness For Elderly Community in US and Japan.

Loneliness has long affected the elderly community. This issue is significantly worsened by the social isolation resulting from the COVID-19 pandemic. To address this pressing issue, we employed a sensor-based methodology to predict loneliness and potentially inform interventions. We deployed sensors in the residences of 22 elderly participants from US and Japan, gathering daily activities data through 22 sensor features. Given the extensive feature set, we identify the most effective sensors to ensure unobtrusiveness while upholding privacy. Regression analysis of these features revealed that our best-performing Random Forest model achieved an R2 value of 0.86, on par with existing literature. In addition, we found that the sleep mattress sensor and temperature-humidity sensor were particularly indicative of loneliness. In summary, our research contributes to the HCI literature with effective non-invasive sensing modalities in assessing elderly loneliness, together with insights from our real-world sensor deployments in US and Japan-based elderly communities.