AI for Independent Living

Md Zia Uddin

Abstract: Trustworthy multimodal intelligent systems hold immense promise for enabling independent living among older adults, individuals with disabilities, and those managing chronic conditions. By integrating diverse data streams—such as motion sensors, audio inputs, environmental measurements, and wearable devices—these systems provide a comprehensive view of daily activities. Unlike single-sensor setups, multimodal approaches can capture more nuanced patterns, ensuring reliable event detection and personalized interventions without overburdening users or disrupting their usual routines. Establishing trust in such complex environments requires robust strategies to address privacy, transparency, and fairness. Because these systems operate in highly personal spaces, residents and caregivers need reassurance that their sensitive information will not be misused. One way to achieve this is by processing large portions of data locally, within edge-computing devices, which helps keep personal details off external servers. Additionally, synthetic data generation techniques support model development by simulating real-life patterns. This artificial data allows researchers to train algorithms on varied scenarios—like detecting falls or monitoring medication schedules—without exposing genuine, identifying information. Combined with strong encryption and access controls, these methods help preserve resident privacy while still providing accurate, data-driven insights. Multimodal inputs give rise to potent machine learning models capable of detailed behavior analysis, anomaly detection, and predictive analytics. For example, a system may integrate motion patterns from room sensors, heart-rate data from wearables, and acoustic signals from smart speakers. By synthesizing these signals, the system can more reliably detect events such as a fall, a sudden shift in routine, or signs of emotional distress. Furthermore, predictive analytics can forecast elevated health risks, giving residents and caregivers the opportunity to intervene proactively. Such anticipatory support offers real benefits, including early medical checks or personalized reminders that can prevent hospitalizations and maintain quality of life. Yet even the most accurate models will falter if they are perceived as opaque or prone to bias. Explainable AI (XAI) techniques can alleviate these concerns by revealing the factors behind an algorithm’s suggestions or alerts. Residents and family members who understand how the system interprets sensor data are more likely to accept its recommendations and respond correctly. This transparency also shines a light on possible biases, helping developers refine algorithms so they work equitably across varying health profiles, mobility levels, and cultural backgrounds. As part of a broader ethical approach, designers must also consider user autonomy, allowing residents to adjust the frequency or types of notifications and to override automated decisions when appropriate.

External IDs:doi:10.1007/978-3-031-97359-8_1