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Found 25 papers, 1 papers with code
Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting
Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial.
Context-Aware Stress Monitoring using Wearable and Mobile Technologies in Everyday Settings
We achieved the F1-score of 70\% with a Random Forest classifier using both PPG and contextual data, which is considered an acceptable score in models built for everyday settings.
Reducing Intraspecies and Interspecies Covariate Shift in Traumatic Brain Injury EEG of Humans and Mice Using Transfer Euclidean Alignment
We tested the robustness of this transfer learning technique on various rule-based classical machine learning models as well as the EEGNet-based deep learning model by evaluating on different datasets, including human and mouse data in a binary classification task of detecting individuals with versus without traumatic brain injury (TBI).
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings
Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models.
Demand Layering for Real-Time DNN Inference with Minimized Memory Usage
In our approach, a DNN is loaded and executed in a layer-by-layer manner, minimizing the memory usage to the order of a single layer.
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics.
Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.
Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks
To this end, we propose a reinforcement-learning-based computation offloading solution that learns optimal offloading policy considering deep learning model selection techniques to minimize response time while providing sufficient accuracy.
Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networks
Furthermore, we deploy Hybrid Learning (HL) to accelerate the RL learning process and reduce the number of direct samplings.
Objective Prediction of Tomorrow's Affect Using Multi-Modal Physiological Data and Personal Chronicles: A Study of Monitoring College Student Well-being in 2020
Results showed that our model was able to predict next-day affect with accuracy comparable to state of the art methods.
Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks
Continuous monitoring of blood pressure (BP)can help individuals manage their chronic diseases such as hypertension, requiring non-invasive measurement methods in free-living conditions.
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa.
Personal Mental Health Navigator: Harnessing the Power of Data, Personal Models, and Health Cybernetics to Promote Psychological Well-being
Traditionally, the regime of mental healthcare has followed an episodic psychotherapy model wherein patients seek care from a provider through a prescribed treatment plan developed over multiple provider visits.
Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS
In contrast, vital signs (e. g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring.
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving
For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized.
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging.
Trustworthy AI Inference Systems: An Industry Research View
Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time.
The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis
Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average.
GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
There are different methods and algorithms for stress detection using different physiological signals.
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Our framework first extracts the precise times at which a charge pump in the hardware is activated to support neural computations within a workload.
Mapping Spiking Neural Networks to Neuromorphic Hardware
SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency.
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware.
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization.
