Publications

TransNet: Minimally-Supervised Deep Transfer Learning for Dynamic Adaptation of Wearable Systems
Wearables are poised to transform health and wellness through automation of cost-effective, objective, and real-time health monitoring. However, machine learning models for these systems are designed based on labeled data collected, and feature representations engineered, in controlled environments. This approach has limited scalability of wearables because (i) collecting and labeling sufficiently large amounts of sensor data is a labor-intensive and expensive process; and (ii) wearables are deployed in highly dynamic environments of the end-users whose context undergoes consistent changes. We introduce TransNet, a deep learning framework that minimizes the costly process of data labeling, feature engineering, and algorithm retraining by constructing a scalable computational approach. TransNet learns general and reusable features in lower layers of the framework and quickly reconfigures the underlying models from a small number of labeled instances in a new domain, such as when the system is adopted by a new user or when a previously unseen event is to be added to event vocabulary of the system. Utilizing TransNet on four activity datasets, TransNet achieves an average accuracy of 88.1% in cross-subject learning scenarios using only one labeled instance for each activity class. This performance improves to an accuracy of 92.7% with five labeled instances.
TransNet: Minimally-Supervised Deep Transfer Learning for Dynamic Adaptation of Wearable Systems
Proximity-Based Active Learning for Eating Moment Recognition in WearableSystems
A Dynamic Programming Framework for DVFS-Based Energy-Efficiency in Multicore Systems
A Dynamic Programming Framework for DVFS-Based Energy-Efficiency in Multicore Systems
Share-n-Learn: A Framework for Sharing Activity Recognition Models in Wearable Systems with Context-Varying Sensors
Resource-Efficient Computing in Wearable Systems
Mindful Active Learning
LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition
Human-in-the-Loop Learning for Personalized Diet Monitoring from Unstructured Mobile Data
Trading Off Power Consumption and Prediction Performance in Wearable Motion Sensors: An Optimal and Real-Time Approach
Toward visual field assessment using head-worn sensing devices
Speech2Health: A Mobile Framework for Monitoring Dietary Composition From Spoken Data
Personalized Human Activity Recognition Using Convolutional Neural Networks
New Technologies in Geriatric Oncology Care
Digital Health for Geriatric Oncology
Autonomous Training of Activity Recognition Algorithms in Mobile Sensors: A Transfer Learning Approach in Context-Invariant Views
Architectural considerations for FPGA acceleration of machine learning applications in MapReduce
Autonomous sensor-context learning in dynamic human-centered internet-of-things environments
A Hardware-Assisted Energy-Efficient Processing Model for Activity Recognition Using Wearables
Toward robust and platform-agnostic gait analysis
Patient-centric on-body sensor localization in smart health systems
Data Aggregation in Body Sensor Networks: A Power Optimization Technique for Collaborative Signal Processing
Applications of sensing platforms with wearable computers
A greedy buffer allocation algorithm for power-aware communication in body sensor networks
Sport training using body sensor networks: a statistical approach to measure wrist rotation for golf swing
A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks
A phonological expression for physical movement monitoring in body sensor networks
A New Approach for Design and Verification of Transaction Level Models
Modified Pseudo LRU Replacement Algorithm