Robust Detection of Social Isolation in Older Adults by Combining Biometrics with Social Interaction Data

Several recent studies, in the aftermath of Covid-19, point to dangers of social isolation that negatively impacts both mental and physical health especially amongst older adults. Isolation often leads to self-destructive behaviour such as drug and alcohol misuse deteriorating the quality of life and compounding health complications. Recent research has explored several technologies to detect the onset of isolation and to trigger interventions (e.g., nudge caregivers, etc.) to mitigate its impact. Such mechanisms span a range from using survey instruments, using biometrics to detect stress (an effect of isolation), and those using monitoring social interactions to detect loneliness amongst individuals. This paper studies biometric based and social interaction-based methods with the objective to understand their relative benefits/disadvantages and explores their combined usage to create a robust isolation detection mechanism. In particular, we explore the design of an integrated system based on both biometric (via wearables) and social interaction data (using call log analysis) to study their efficacy both individually and in combination.