Tracking Together: A Robot-and-App-Based Speech Analysis System to Support Shared Meaning-Making Among Dementia Care Partners

Paper Title

Tracking Together: A Robot-and-App-Based Speech Analysis System to Support Shared Meaning-Making Among Dementia Care Partners

Publication Info

• Topic area: Human–Computer Interaction (HCI) for dementia care
• Keywords: Dementia care, speech analysis, conversational robots, tracking systems, care relationships, memory aid, caregiver support, Human–Computer Interaction, quantified self, autonomy

Background and Problem

• Problem / challenge: Existing tracking technologies for dementia care focus primarily on quantified symptom management for caregivers, often neglecting the autonomy and relational needs of people living with dementia. Tracking systems rarely address the multifaceted roles of care partners or provide meaningful engagement for both parties.
• Significance: Dementia care involves progressive cognitive decline, requiring tools that support autonomy, relational understanding, and meaningful engagement. Addressing these gaps can improve the quality of life for people living with dementia and their care partners.
• Motivation and related work: Prior research has explored tracking for older adults but often emphasizes techno-solutionist approaches, focusing on clinical data and caregiver management. Few studies consider relational perspectives or how tracking can support autonomy and shared meaning-making in dementia care relationships.

Solution

• Proposed approach: A robot-and-app-based speech analysis system that captures conversational data and visualizes it through a mobile application tailored to the needs of people living with dementia and their care partners.
• Novelty:
  1. Focus on tracking as a dialogic and engaging process rather than a purely clinical or quantified approach.
  2. Tailored interfaces for people living with dementia and care partners, addressing their distinct needs and roles.
  3. Integration of conversational robots to enhance autonomy and relational understanding through empathetic dialogue and memory aids.
• Procedure and key techniques:
  • Iterative design process involving three phases: semi-structured interviews, exploratory interviews with robot interaction, and focus group discussions.
  • Development of a speech-tracking system featuring a conversational robot and mobile app with tailored interfaces.
  • Reflexive thematic analysis to identify patterns of meaningful tracking and relational needs.

Results

• Concrete findings:
  • People living with dementia preferred tracking through conversational formats and memory aids, avoiding numerical indicators of decline.
  • Care partners valued actionable insights and educational tools to support caregiving and relational understanding.
  • Both groups appreciated the robot’s empathetic dialogue and ability to engage users in meaningful conversations.
• Advantage over baselines:
  • Moves beyond traditional quantified-self paradigms by emphasizing relational and dialogic tracking.
  • Supports autonomy for people living with dementia and provides multifaceted insights for care partners.
• Experiments / evaluation:
  • Conducted with 17 participants (7 people living with dementia, 9 care partners, 1 self-caring individual) across three iterative design phases.
  • Evaluated through semi-structured interviews, robot interactions, and focus group discussions.
• Limitations and future work:
  • Limited exploration of long-term engagement and technical performance of the system.
  • Future research could investigate broader relationship dynamics, alternative modalities, and extended usability testing.

Summary

This study introduces a robot-and-app-based speech analysis system designed to support shared meaning-making among people living with dementia and their care partners. By emphasizing dialogic tracking and tailoring interfaces to distinct user needs, the system fosters autonomy, relational understanding, and meaningful engagement. The iterative design process revealed that conversational robots and memory aids can enhance tracking experiences, while actionable insights and educational tools benefit care partners. Future research should explore long-term usability, broader relationship dynamics, and technical advancements to further refine the system’s applicability.