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.
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.
Two-stream model recognizes affect from audio and video.
problem Human affect recognition in real-world settings.
method Two-stream aural-visual analysis model with separate audio and visual processing.
result Model achieves promising results on Aff-Wild2 database.
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence …
Models predict emotional valence from narratives, matching human raters.
problem Predicting emotional valence from multimodal time-series data.
method Adapted attention-based mechanisms (Transformer, Memory Fusion Network) to emotional narratives.
result Models perform well, matching human raters on emotional valence prediction.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
problem Automated emotion recognition from gait patterns.
method Data collection, preprocessing, and classification techniques.
result Gait patterns can indicate different emotion states, making them a promising source for emotion detection.
Automatic understanding of human affect using visual signals is a problem that has attracted significant interest over the past 20 years. However, human emotional states are quite complex. To appraise such states displayed in real-world settings, we need expressive emotional descriptors that are capable of capturing an…
The paper offers a checklist for comparing human and machine visual perception.
problem Comparing human and machine visual perception.
method Designing, conducting, and interpreting experiments to investigate mechanisms.
result Feedback mechanisms may not be necessary for visual reasoning tasks.
Reduces gender classification bias by learning race-invariant face representations.
problem Societal bias in gender recognition systems.
method Adversarially trained autoencoder model to learn race-invariant face representations.
result Achieved a significant drop of over 40% in racial bias surrogate metric with race invariant representations.
INVERT connects neural representations to human-understandable concepts.
problem Lack of understanding and statistical significance in existing explainability methods.
method Inverse Recognition (INVERT) approach that connects learned representations to human-understandable concepts.
result INVERT provides interpretable metrics and statistical significance for representation alignment.
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.
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 approach uses graphs for sign language recognition.
problem Challenges in recognizing sign language for deaf individuals.
method Spatial-Temporal Graph Convolutional Network.
result Improved sign language recognition using human skeletal movements.
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%.
Generative classifiers show surprising human-like performance.
problem Comparing generative and discriminative models for object recognition.
method Built on recent advances in generative modeling to create classifiers and compared them to discriminative models.
result Generative classifiers outperform discriminative models in several key areas, including shape bias and out-of-distribution accuracy.
Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algo…
Moon phases added to stock market analysis for better pattern recognition.
problem Finding meaningful patterns in stock market data using irregular time sampling.
method Incorporating Moon phases into the Gregorian calendar time sampling methods for stock market analysis.
result Moon phases provide unique, irregular sampling features for stock market pattern recognition.
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.
As artificial intelligence is increasingly affecting all parts of society and life, there is growing recognition that human interpretability of machine learning models is important. It is often argued that accuracy or other similar generalization performance metrics must be sacrificed in order to gain interpretability.…
SVM with local features improves human action recognition.
problem Improving human action recognition in videos.
method Local appearance and motion features extracted using CNNs, concatenated, and used with SVM for classification.
result SVM with local features outperforms previous methods on benchmark datasets.
Model improves emotion recognition using multiple physiological signals.
problem Single physiological signal is insufficient for accurate emotion recognition.
method Fused multiple modal physiological signals (EEG, EMG, EOG) for emotion classification.
result Best classification accuracy of 94.42% on arousal and 94.02% on valence in two-class tasks.
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.
First ABAW 2020 Competition analyzes affective behavior tasks.
problem Automatic analysis of valence-arousal, basic expressions, and action units in real-world scenarios.
method Provided Aff-Wild2 database, described Challenges, evaluation metrics, and top-performing systems.
result Demonstrated the feasibility of automatic affective behavior analysis in real-world settings.
Unimodal approach for group-level emotion recognition without individual features.
problem Privacy issues in individual-based emotion recognition models.
method Frugal approach using global features, state-of-the-art and synthetic corpora.
result 59.13% accuracy on VGAF test set, 11th place in EmotiW Challenge 2020.
Improved speech emotion recognition using pre-trained language models.
problem Challenging task of speech emotion recognition for natural human-machine interaction.
method Fine-tuning pre-trained language models for text emotion recognition, combining with speech emotion recognition.
result 73.5% accuracy in speech emotion recognition on a subset of IEMOCAP dataset.
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.
Study investigates impact of labeler socio-cultural background on affect detection model performance.
problem Impact of labeler socio-cultural background on affect detection model performance.
method Investigates the impact of labeler socio-cultural background on affect detection model performance.
result Differences in labeler background impact the performance of affect detection models.
Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the r…
Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS…
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.
We proposed a probabilistic approach to joint modeling of participants' reliability and humans' regularity in crowdsourced affective studies. Reliability measures how likely a subject will respond to a question seriously; and regularity measures how often a human will agree with other seriously-entered responses coming…
Robots learn intentions from multiple cues to reduce uncertainty.
problem Uncertainty in human-robot interaction for vulnerable users.
method Multimodal classifier fusion using Bayesian Independent Opinion Pool.
result Fused classifiers outperform individual modalities in accuracy and uncertainty reduction.
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.
Improved action recognition in live videos with hybrid FR-DL method.
problem High computational costs and lack of temporal information in conventional action recognition.
method Automated selection of representative frames, feature extraction, background subtraction, HOG, deep neural network, LSTM, Softmax-KNN classifier.
result Significant improvement in accuracy and speed compared to state-of-the-art methods.
Study shows stress affects emotion recognition models, improving generalizability.
problem Stress affects emotion recognition models, reducing their generalizability.
method Used adversarial networks to control for stress effects on emotion recognition.
result Emotion recognition models that control for stress during training have better generalizability.
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.
Bayesian Neural Networks improve uncertainty modeling in facial emotion recognition.
problem High aleatoric uncertainty and visual ambiguity in facial emotion recognition.
method Bayesian Neural Networks approximated using MC-Dropout, MC-DropConnect, or Ensemble methods.
result Bayesian Neural Networks produce more human-like output probabilities.
Study shows semi-supervised learning improves human activity recognition with minimal user input.
problem Improving human activity recognition models using incremental learning.
method Three approaches: non-supervised, semi-supervised, and supervised learning were compared.
result Semi-supervised learning achieves similar accuracy to supervised learning with minimal user input.
Deep learning model classifies concurrent human interactions from WiFi data with high accuracy.
problem Classifying concurrent human interactions from WiFi data with high accuracy.
method Attention-BiGRU deep learning model using Multiple Input Multiple Output radio link.
result Maximum benchmark accuracy of 94% for a single subject-pair, 88% for ten subject pairs.
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.
SA-GAN improves HAR model performance across new users.
problem Poor performance of HAR models on new user data.
method Generative Adversarial Network (GAN) for cross-subject transfer learning.
result SA-GAN outperformed other methods in HAR tasks.
The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the varia…
HHAR-net uses neural networks to recognize human activities at different levels of abstraction.
problem Recognizing different layers of human activities concealed in behavior.
method Hierarchical classification with Neural Networks.
result 95.8% accuracy for low-level activities and 92.8% overall accuracy.
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learnin…
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.
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.
ESPRESSO segments time-series data for better human activity recognition.
problem Segmenting high-dimensional time-series data for applications like HAR.
method ESPRESSO combines entropy and shape analysis for multi-dimensional time-series segmentation.
result ESPRESSO outperforms four state-of-the-art methods across seven datasets.
Improved heart rate and activity recognition with low-power wrist sensors.
problem Challenges in battery life, cost, and sensor performance in wrist-worn sensing applications.
method Used photoplethysmography (PPG) for heart rate and activity recognition, applying transfer learning and CNNs.
result Low sampling frequencies (5 Hz and 10 Hz) achieved good performance in heart rate and activity recognition.