Zobrazeno 1 - 10 of 793 pro vyhledávání: '"Buman, Matthew P"'
1
Report
Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions
Autor: Mamun, Abdullah, Leonard, Krista S., Petrov, Megan E., Buman, Matthew P., Ghasemzadeh, Hassan
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 interventions in real-world clinical environments. Methods: We conducted two
Externí odkaz: http://arxiv.org/abs/2410.09643
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2
Report
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Autor: Jeon, Eun Som, Choi, Hongjun, Shukla, Ankita, Wang, Yuan, Lee, Hyunglae, Buman, Matthew P., Turaga, Pavan
Publikováno v: Engineering Applications of Artificial Intelligence, 130, 107719 (2024)
Deep learning methods have achieved a lot of success in various applications involving converting wearable sensor data to actionable health insights. A common application areas is activity recognition, where deep-learning methods still suffer from li
Externí odkaz: http://arxiv.org/abs/2407.05315
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3
Akademický článek
Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies
Autor: Hekler, Eric B, Buman, Matthew P, Grieco, Lauren, Rosenberger, Mary, Winter, Sandra J, Haskell, William, King, Abby C
Publikováno v: JMIR mHealth and uHealth, Vol 3, Iss 2, p e36 (2015)
BackgroundThere is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach. ObjectiveThe purpose of this work was to d
Externí odkaz: https://doaj.org/article/a0441f33da1a403693117a737fc65323
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4
Report
Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
Autor: Jeon, Eun Som, Som, Anirudh, Shukla, Ankita, Hasanaj, Kristina, Buman, Matthew P., Turaga, Pavan
Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge de
Externí odkaz: http://arxiv.org/abs/2201.00111
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5
Report
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns
Autor: Som, Anirudh, Krishnamurthi, Narayanan, Buman, Matthew, Turaga, Pavan
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 challenging as they require large amounts of training data that capture differ
Externí odkaz: http://arxiv.org/abs/2005.02589
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6
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7
Akademický článek
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9
Report
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Autor: Som, Anirudh, Choi, Hongjun, Ramamurthy, Karthikeyan Natesan, Buman, Matthew, Turaga, Pavan
Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustn
Externí odkaz: http://arxiv.org/abs/1906.01769
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10
Akademický článek
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