Bayesian approach improves activity recognition accuracy and uncertainty quantification.
problem Lack of predictive uncertainty in multimodal audiovisual activity recognition.
method Uncertainty aware multimodal Bayesian fusion framework combining deterministic and variational layers.
result Improved precision-recall AUC by 10.2% on MiT dataset.
Improved audio event recognition using audiovisual transformers.
problem Weakly labeled audio event classification with limited temporal information.
method Adapted transformer architecture that combines audio and video inputs.
result State-of-the-art performance on audiovisual data set.
Paper presents an audiovisual model to recognize sounds from weakly labeled video data.
problem Sound recognition from weakly labeled video data.
method Audiovisual fusion model with attention mechanism.
result The model achieves a mean Average Precision (mAP) of 46.16 on AudioSet, outperforming state-of-the-art models.
Improved animated faces using audiovisual and modality dropout.
problem Creating realistic animated faces using speech and visual cues.
method Training a deep learning model with modality dropout to balance audio and visual inputs.
result Modality dropout improves viewer preference for audiovisual-driven animation.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest earning methods: i) transfer learnin…
Deep learning improves human affect recognition in natural settings.
problem Automatic recognition of human emotions in real-world scenarios.
method Review and analysis of 233 studies using deep neural networks.
result Deep learning significantly enhances affect recognition from multimodal sensor data.
Study uses HMM for real-time activity recognition from sensor data.
problem Real-time activity recognition from streaming sensor data.
method Online hierarchical hidden Markov model.
result Improved activity recognition accuracy compared to existing methods.
EmbraceNet fusion model for multi-sensor activity recognition.
problem Human activity recognition using multiple sensors is challenging.
method Independent processing of each sensor, feature fusion with EmbraceNet, post-processing, and additional processes.
result Improved performance in SHL recognition challenge.
Method converts facial expressions and voice of a source speaker into a target speaker.
problem Separate conversion of facial and acoustic features leads to unnatural results.
method Uses three neural networks: conversion, waveform generation, and image reconstruction.
result Significantly higher naturalness achieved when converting both features together.
TransFall uses transfer learning to improve activity recognition from mobile sensors.
problem Performance degradation due to platform and user movement differences.
method Two-tier data transformation, label estimation, and model generation layers.
result TransFall enhances activity recognition accuracy for new scenarios.
Paper tackles zero-shot activity recognition using video features and text embeddings.
problem Zero-shot activity recognition with videos.
method Auto-encoder model for multimodal joint embedding, 3D convolutional action recognition for visual features, GloVe word embeddings for textual features.
result Improved zero-shot recognition results with top-n accuracy and mean Nearest Neighbor Overlap.
Personalized activity recognition improves performance for diverse users.
problem Poor performance of impersonal algorithms for individual users.
method Personalized activity recognition using deep embeddings from a fully convolutional neural network with triplet loss.
result Novel subject triplet loss provides the best performance overall.
A framework uses deep learning for activity recognition in IoT devices.
problem Activity recognition in IoT devices without physical contact.
method Background subtraction followed by 3D-Convolutional Neural Networks.
result Enhanced activity recognition using small IoT devices.
Wi-Fringe recognizes named gestures using WiFi CSI without training data.
problem Lack of training data for WiFi-based gesture recognition.
method WiFi CSI values paired with activity names for recognition.
result Wi-Fringe detects activities with or without training data.
Bayesian inference improves visual activity recognition accuracy.
problem Uncertainty estimation in deep neural networks for safety-critical applications.
method Bayesian deep learning with stochastic variational inference.
result Improves visual activity recognition precision-recall AUC by 6.2%.
Enhances activity recognition in wearable computing with context awareness and uncertainty quantification.
problem Context-dependent activity recognition and unknown contexts in wearable computing.
method Developed the α-{eta} network coupled with uncertainty quantification (UQ) based on maximum entropy.
result Improved accuracy and F-score by 10% through high-level context identification.
Paper proposes a few-shot learning method for human activity recognition.
problem Efficiently recognize human activities with limited labeled data.
method Uses deep learning for feature extraction and knowledge transfer from existing models.
result Promising results show the proposed method's advantages over traditional approaches.
New RL method optimizes power and accuracy for activity recognition.
problem Balancing power consumption and accuracy in wearable devices for activity recognition.
method Reinforcement Learning with multiple feedback sources for feature selection.
result Achieved good trade-off between power consumption and accuracy.
Transfer learning improves activity recognition accuracy on smartwatches.
problem Inconsistent activity recognition accuracy across different users.
method On-device calibration of GBM by tuning parameters without retraining.
result Significant improvement in user-based accuracy for activity recognition.
A new HAR algorithm uses U-Net for pixel-level gesture recognition.
problem Multi-class window problem in traditional HAR methods.
method U-Net network for activity labeling and prediction at each sampling point.
result Highest accuracy and F1-score compared to other methods.
Deep belief network improves smartphone activity recognition.
problem Activity recognition on mobile devices.
method Categorization through deep belief network.
result 98.25% correct diagnosis in training data, 93.01% in test data.
This paper improves human activity recognition using LSTM-RNN models.
problem Improving accuracy in human activity recognition from sensor data.
method Design and training of LSTM-RNN models on WISDM dataset.
result Achieved an accuracy of above 94% and a loss of less than 30% in 500 epochs.
Subject Cross Validation improves Human Activity Recognition performance by up to 16%.
problem Overestimation of Human Activity Recognition performance using k-fold cross validation.
method Investigated Subject Cross Validation vs. k-fold cross validation for Human Activity Recognition.
result Subject Cross Validation increases performance by up to 16%.
New approach improves human activity recognition with wearables.
problem Improving human activity recognition with wearables.
method Exploiting latent relationships between multi-channel sensor modalities, data-agnostic augmentation, and a classification loss criterion.
result Achieves new state-of-the-art performance on four diverse activity recognition benchmarks.
Enhances 2D face recognition with 3D features using active illumination.
problem Improving robustness of 2D face recognition to spoofing attacks and low-light conditions.
method Projecting a high spatial frequency pattern onto the face to recover 3D information and a 2D image simultaneously.
result Significantly boosts face recognition performance and dramatically improves robustness to spoofing attacks.
In training speech recognition systems, labeling audio clips can be expensive, and not all data is equally valuable. Active learning aims to label only the most informative samples to reduce cost. For speech recognition, confidence scores and other likelihood-based active learning methods have been shown to be effectiv…
Visualizes deep neural networks for speech recognition using learned topographic filter maps.
problem Unintuitive internal structure of deep neural networks complicates activation visualization.
method Trains a convolutional speech recognition model with filters arranged in a 2D grid, highlighting similar filters.
result Topographic filter maps visualize artificial neuron activations more intuitively.
Human activity recognition plays an important role in people's daily life. However, it is often expensive and time-consuming to acquire sufficient labeled activity data. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labe…
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
Two attention models improve human activity recognition by focusing on important signals and sensor modalities.
problem Noise and unimportant signal components in recurrent networks for human activity recognition.
method Temporal and sensor attention mechanisms with continuity constraints.
result State-of-the-art results on three datasets, showing improved understandability and mean F1 score.
SparseSense improves HAR from sparse sensor data, outperforming state-of-the-art models.
problem Learning activity recognition from highly sparse sensor data streams.
method Set-based neural networks for end-to-end learning from sparse data.
result Significant performance improvements in HAR from passive sensor datasets.
New feature representation improves HAR model accuracy.
problem Improving human activity recognition model performance.
method Proposed new feature representation and comparison with existing methods.
result Techniques based on the proposed representation outperform baselines.
RAN model recognizes multiple activities from unlabeled sensor data.
problem Handling weakly labeled multi-activity data from wearable sensors.
method Recurrent Attention Networks (RAN) for sequential multi-activity recognition and localization.
result RAN model can infer multiple activities and determine activity locations from unlabeled data.
Recognizes collective sheep movement activities online.
problem Recognizing collective animal movement activities.
method Discriminative framework for tracking flock positions and velocities online.
result Good accuracy in learning skewed collective activities.
Proposes privacy-preserving sensor data transformations to prevent user re-identification and sensitive activity inference.
problem Privacy threats from shared sensor data and potential user re-identification.
method Mechanisms to transform sensor data to eliminate patterns for re-identification and sensitive activity inference, while maintaining minor utility loss.
result Reduced user re-identification accuracy to random guess level and prevented inference of sensitive activities.
ESE-FN improves elderly activity recognition accuracy.
problem Recognizing individual actions and human-object interactions in elderly activities.
method Exploits multi-modal features from RGB videos and skeleton sequences using ESE attentions and a new Multi-modal Loss.
result ESE-FN achieves best accuracy on ETRI-Activity3D dataset.
Paper explores activity recognition and prediction in real homes using sensor data and video.
problem Improving accuracy of activity recognition and prediction in real home environments.
method Binary sensor data, depth video data, field trial, probabilistic methods, LSTM networks, transfer learning, IIR filter.
result Achieved good accuracy in predicting next sensor event and its mean time of occurrence using LSTM model.
Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity reco…
Paper presents a neural network for recognizing human activities from unlabeled sensor data.
problem Time-consuming annotation of sensor data for activity recognition.
method Attention-based convolutional neural network for weakly labeled data.
result Attention model improves accuracy in recognizing human activities.
Paper presents a novel online HAR method using Hierarchical Hidden Markov Models.
problem Challenges in robust online activity recognition in smart environments.
method Two-phase approach: 1) Segmentation and activity reporting using Hierarchical Hidden Markov Models, 2) Correction of labels based on statistical features.
result Proposes a method that can detect and correct interrupted activities, outperforming state-of-the-art methods.
Introspects convolutional speech recognition models using Gradient-adjusted Neuron Activation Profiles.
problem Lack of interpretability in deep learning ASR models.
method Gradient-adjusted Neuron Activation Profiles (GradNAPs) for feature and representation visualization.
result Gains insight into how data is processed in convolutional ASR models.
Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Study develops sign recognition system for DHH users.
problem Accessibility of voice-controlled devices for Deaf and Hard-of-Hearing users.
method Multimodal data (RGB video and skeletal data) for sign language recognition using deep learning.
result Validation on GMUASL51 dataset of 12 users and 13107 samples across 51 signs.
Paper uses SSD to detect miners' activities in a mining environment.
problem Tracking miners' activities in a mining environment with little obstruction.
method Used SSD trained on COCO dataset to detect miners' activities. Implemented machine learning algorithms using Tensorflow and C++.
result Improved accuracy of detecting miners' activities through data fusion.
CNNs classify human activities from IMU data.
problem Automatic identification of physical activities using motion sensors.
method Used Convolutional Neural Networks (CNNs) with raw IMU data.
result CNNs perform well in classifying 16 lower-limb activities.
Proposes a method to improve deep active learning for NER tasks.
problem Weaknesses of existing deep active learning algorithms in practice.
method Estimates error decay curves of feature-defined subsets to improve sampling efficiency and robustness.
result Significantly outperforms diversification-based methods for black-box NER taggers and makes sampling more robust to labeling noise.
Paper proposes an active learning method for surgical workflow recognition using long-range temporal dependency.
problem Challenges in automatic surgical workflow recognition due to lack of large-scale labelled datasets.
method NL-RCNet with non-local block for capturing long-range temporal dependency and intra-clip dependency score for selection.
result Our approach outperforms state-of-the-art methods by selecting only 50% of samples for training.