activity
20192025
most citedTopological Persistence Guided Knowledge Distillation for Wearable Sensor Data

8 citations · 15 across the 5 of their papers we have counts for

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cs.LG2025

Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics

Fengxu Li, Stephanie M. Carpenter, Matthew P. Buman +1

A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action'…

cs.LG20257 cited

LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet

Abdullah Mamun, Asiful Arefeen, Susan B. Racette +4

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in pe…

cs.LG2025

Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data

Eun Som Jeon, Hongjun Choi, Matthew P. Buman +1

The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topologi…

cs.LG2024

Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions

Abdullah Mamun, Krista S. Leonard, Megan E. Petrov +2

Objective: This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient's activity behavior to allow for early and personalized interve…

cs.LG2020

Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns

Anirudh Som, Narayanan Krishnamurthi, Matthew Buman +1

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challeng…