TY - GEN
T1 - Augmenting Personalized Memory via Practical Multimodal Wearable Sensing in Visual Search and Wayfinding Navigation
AU - Ghosh, Indrajeet
AU - Jayarajah, Kasthuri
AU - Waytowich, Nicholas
AU - Roy, Nirmalya
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/6/13
Y1 - 2025/6/13
N2 - Working memory involves the temporary retention of information over short periods. It is a critical cognitive function that enables humans to perform various online processing tasks, such as dialing a phone number, recalling misplaced items' locations, or navigating through a store. However, inherent limitations in an individual's capacity to retain information often result in forgetting important details during such tasks. Although previous research has successfully utilized wearable and assistive technologies to enhance long-term memory functions (e.g., episodic memory), their application to supporting short-term recall in daily activities remains underexplored. To address this gap, we present Memento, a framework that uses multimodal wearable sensor data to detect significant changes in cognitive state and provide intelligent in situ cues to enhance recall. Through two user studies involving 15 and 25 participants in visual search navigation tasks, we demonstrate that participants receiving visual cues from Memento achieved significantly better route recall, improving approximately 20-23% compared to free recall. Furthermore, Memento reduced cognitive load and review time by 46% while also achieving a substantial reduction in computation time (3.86 secs vs. 15.35 secs), offering an average 75% effective compared to computer vision-based cues selection approaches.
AB - Working memory involves the temporary retention of information over short periods. It is a critical cognitive function that enables humans to perform various online processing tasks, such as dialing a phone number, recalling misplaced items' locations, or navigating through a store. However, inherent limitations in an individual's capacity to retain information often result in forgetting important details during such tasks. Although previous research has successfully utilized wearable and assistive technologies to enhance long-term memory functions (e.g., episodic memory), their application to supporting short-term recall in daily activities remains underexplored. To address this gap, we present Memento, a framework that uses multimodal wearable sensor data to detect significant changes in cognitive state and provide intelligent in situ cues to enhance recall. Through two user studies involving 15 and 25 participants in visual search navigation tasks, we demonstrate that participants receiving visual cues from Memento achieved significantly better route recall, improving approximately 20-23% compared to free recall. Furthermore, Memento reduced cognitive load and review time by 46% while also achieving a substantial reduction in computation time (3.86 secs vs. 15.35 secs), offering an average 75% effective compared to computer vision-based cues selection approaches.
KW - Affective Computing
KW - Multimodal Sensing
KW - Short-term Episode Recall
KW - User Modeling
KW - Verbal Cueing
KW - Working Memory
UR - https://www.scopus.com/pages/publications/105025527162
UR - https://www.scopus.com/pages/publications/105025527162#tab=citedBy
U2 - 10.1145/3699682.3728340
DO - 10.1145/3699682.3728340
M3 - Conference contribution
AN - SCOPUS:105025527162
T3 - UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
SP - 11
EP - 21
BT - UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery, Inc
T2 - 33rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2025
Y2 - 16 June 2025 through 19 June 2025
ER -