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Found 11 papers, 3 papers with code
Development and Evaluation of Three Chatbots for Postpartum Mood and Anxiety Disorders
In collaboration with Postpartum Support International (PSI), a non-profit organization dedicated to supporting caregivers with postpartum mood and anxiety disorders, we developed three chatbots to provide context-specific empathetic support to postpartum caregivers, leveraging both rule-based and generative models.
Cheating off your neighbors: Improving activity recognition through corroboration
From solely an individual's perspective, it can be difficult to differentiate between these activities as they may appear very similar, even though they are markedly different.
Understanding Postpartum Parents' Experiences via Two Digital Platforms
In the current work, we provide a descriptive analysis of the concerns, psychological states, and motivations shared by healthy and distressed postpartum support seekers on two digital platforms, a one-on-one digital helpline and a publicly available online forum.
Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks
To push this field forward, we build on recent advances in the area of continual machine learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation.
HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data
The problem of human activity recognition from mobile sensor data applies to multiple domains, such as health monitoring, personal fitness, daily life logging, and senior care.
Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study
Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life.
Infant Crying Detection in Real-World Environments
Most existing cry detection models have been tested with data collected in controlled settings.
Quantifying the Chaos Level of Infants' Environment via Unsupervised Learning
Acoustic environments vary dramatically within the home setting.
Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos
Over the years, activity sensing and recognition has been shown to play a key enabling role in a wide range of applications, from sustainability and human-computer interaction to health care.
Leveraging Context to Support Automated Food Recognition in Restaurants
The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat.
Predicting Daily Activities From Egocentric Images Using Deep Learning
We collected a dataset of 40, 103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities.
