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Found 18 papers, 5 papers with code
Transfer Learning in Human Activity Recognition: A Survey
In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR.
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition
Yet they can fail when the differences between source and target conditions are too large and/ or only few samples from a target application domain are available, each of which are typical challenges in real-world human activity recognition scenarios.
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Based on an extensive experimental evaluation on a suite of wearables-based benchmark HAR tasks, we demonstrate the potential of our learned discretization scheme and discuss how discretized sensor data analysis can lead to substantial changes in HAR.
ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer -- we develop three "boosters" -- R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively.
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition
What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non periodic, and occur less frequently when compared with the often large amount of irrelevant background activities.
Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition
The dichotomy between the challenging nature of obtaining annotations for activities, and the more straightforward nature of data collection from wearables, has resulted in significant interest in the development of techniques that utilize large quantities of unlabeled data for learning representations.
Finding Islands of Predictability in Action Forecasting
We address dense action forecasting: the problem of predicting future action sequence over long durations based on partial observation.
Ubi-SleepNet: Advanced Multimodal Fusion Techniques for Three-stage Sleep Classification Using Ubiquitous Sensing
Experimental results demonstrate important evidence that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep.
Explainable Activity Recognition for Smart Home Systems
In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods to generate natural language explanations that explain what about an activity led to the given classification.
PLAN-B: Predicting Likely Alternative Next Best Sequences for Action Prediction
Action prediction focuses on anticipating actions before they happen.
Contrastive Predictive Coding for Human Activity Recognition
Our work focuses on effective use of small amounts of labeled data and the opportunistic exploitation of unlabeled data that are straightforward to collect in mobile and ubiquitous computing scenarios.
Transfer Learning for Activity Recognition in Mobile Health
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation.
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR).
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements).
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades.
On Attention Models for Human Activity Recognition
Most approaches that model time-series data in human activity recognition based on body-worn sensing (HAR) use a fixed size temporal context to represent different activities.
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables
We demonstrate, both formally and empirically, that Ensembles of deep LSTM learners outperform the individual LSTM networks.
Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
Human activity recognition (HAR) in ubiquitous computing is beginning to adopt deep learning to substitute for well-established analysis techniques that rely on hand-crafted feature extraction and classification techniques.
