{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/human-activity-recognition/papers/6","list_of":"/task/human-activity-recognition","task":"Human Activity Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":6,"pages_in_order":8,"rows_per_page":100,"rows":[501,600],"of":744,"counts":{"archive_papers_tagged":744,"with_a_code_link":195,"where_syntology_ran_a_sample":18,"not_listed_spam_title":0,"listed":744,"listed_where_code_ran":18,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":13,"every_run_a_failure_of_syntologys_instrument":5,"listed_with_a_run_with_no_instrument_failure":13,"listed_every_run_a_failure_of_syntologys_instrument":5,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/human-activity-recognition","prev":"/task/human-activity-recognition/papers/5","next":"/task/human-activity-recognition/papers/7","papers":[{"url":null,"slug":"a-prospective-approach-for-human-to-human","title":"A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation","date":"2022-02-16","arxiv_id":"2202.08146","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-fusion-transformer-for-sensor","title":"Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition","date":"2022-02-16","arxiv_id":"2202.12949","repositories_listed":0,"syntology":null},{"url":null,"slug":"video2imu-realistic-imu-features-and-signals","title":"Video2IMU: Realistic IMU features and signals from videos","date":"2022-02-14","arxiv_id":"2202.06547","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-tools-of","title":"Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects","date":"2022-02-02","arxiv_id":"2202.03274","repositories_listed":0,"syntology":null},{"url":null,"slug":"activity-recognition-in-assembly-tasks-by","title":"Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs","date":"2022-02-01","arxiv_id":"2202.00332","repositories_listed":0,"syntology":null},{"url":null,"slug":"collossl-collaborative-self-supervised","title":"ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition","date":"2022-02-01","arxiv_id":"2202.00758","repositories_listed":0,"syntology":null},{"url":"/paper/diat-m-radhar-micro-doppler-signature-dataset","slug":"diat-m-radhar-micro-doppler-signature-dataset","title":"DIAT-μ RadHAR (micro-doppler signature dataset) & μ RadNet (a lightweight DCNN)—For human suspicious activity recognition","date":"2022-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/diat-radharnet-a-lightweight-dcnn-for-radar","slug":"diat-radharnet-a-lightweight-dcnn-for-radar","title":"DIAT-RadHARNet: A lightweight DCNN for radar based classification of human suspicious activities","date":"2022-02-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-models-using","title":"Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning","date":"2022-01-21","arxiv_id":"2201.08565","repositories_listed":0,"syntology":null},{"url":null,"slug":"watch-wasserstein-change-point-detection-for","title":"WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data","date":"2022-01-18","arxiv_id":"2201.07125","repositories_listed":0,"syntology":null},{"url":null,"slug":"homogenization-of-existing-inertial-based","title":"Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition","date":"2022-01-17","arxiv_id":"2201.07891","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-on-wrist-worn","title":"Human Activity Recognition on wrist-worn accelerometers using self-supervised neural networks","date":"2021-12-22","arxiv_id":"2112.12272","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-sensor-fusion-for-human","title":"Attention-Based Sensor Fusion for Human Activity Recognition Using IMU Signals","date":"2021-12-20","arxiv_id":"2112.11224","repositories_listed":0,"syntology":null},{"url":null,"slug":"ssdl-self-supervised-dictionary-learning","title":"SSDL: Self-Supervised Dictionary Learning","date":"2021-12-03","arxiv_id":"2112.01790","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-3d","title":"Human Activity Recognition Using 3D Orthogonally-projected EfficientNet on Radar Time-Range-Doppler Signature","date":"2021-11-24","arxiv_id":"2111.12375","repositories_listed":0,"syntology":null},{"url":null,"slug":"flsys-toward-an-open-ecosystem-for","title":"FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps","date":"2021-11-17","arxiv_id":"2111.09445","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-human-activities-with-inertial","title":"Classifying Human Activities with Inertial Sensors: A Machine Learning Approach","date":"2021-11-09","arxiv_id":"2111.05333","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-mimo-radar-based-metric-learning-approach","title":"A MIMO Radar-Based Metric Learning Approach for Activity Recognition","date":"2021-11-02","arxiv_id":"2111.01939","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-in-human-activity-recognition","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","date":"2021-10-31","arxiv_id":"2111.00418","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-attribute","title":"Human Activity Recognition using Attribute-Based Neural Networks and Context Information","date":"2021-10-28","arxiv_id":"2111.04564","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-deep-feature-extraction-network","title":"Adversarial Deep Feature Extraction Network for User Independent Human Activity Recognition","date":"2021-10-23","arxiv_id":"2110.12163","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-human-activity-recognition-in","title":"A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning","date":"2021-10-18","arxiv_id":"2111.04418","repositories_listed":0,"syntology":null},{"url":null,"slug":"guided-gan-adversarial-representation","title":"Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables","date":"2021-10-12","arxiv_id":"2110.05732","repositories_listed":0,"syntology":null},{"url":null,"slug":"lightweight-transformer-in-federated-setting","title":"Lightweight Transformer in Federated Setting for Human Activity Recognition","date":"2021-10-01","arxiv_id":"2110.00244","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversify-to-generalize-learning-generalized","title":"DIVERSIFY to Generalize: Learning Generalized Representations for Time Series Classification","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segtime-precise-time-series-segmentation","title":"SegTime: Precise Time Series Segmentation without Sliding Window","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-techniques-for-online","title":"Incremental Learning Techniques for Online Human Activity Recognition","date":"2021-09-20","arxiv_id":"2109.09435","repositories_listed":0,"syntology":null},{"url":null,"slug":"rapid-retrofitting-ieee-802-11ay-access","title":"RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing","date":"2021-09-10","arxiv_id":"2109.04819","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-distillation-based-approach-integrating","title":"A distillation-based approach integrating continual learning and federated learning for pervasive services","date":"2021-09-09","arxiv_id":"2109.04197","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensor-data-augmentation-with-resampling-for","title":"Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition","date":"2021-09-05","arxiv_id":"2109.02054","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-activity-recognition-using","title":"Few Shot Activity Recognition Using Variational Inference","date":"2021-08-20","arxiv_id":"2108.08990","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-neurorobotics-approach-to-behaviour","title":"A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition","date":"2021-07-27","arxiv_id":"2107.12540","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-style-transfer-enhanced-training","title":"Neural Style Transfer Enhanced Training Support For Human Activity Recognition","date":"2021-07-27","arxiv_id":"2107.12821","repositories_listed":0,"syntology":null},{"url":"/paper/unsupervised-deep-anomaly-detection-for-multi","slug":"unsupervised-deep-anomaly-detection-for-multi","title":"Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals","date":"2021-07-27","arxiv_id":"2107.12626","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-accurate-human-activity-recognition","title":"A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation","date":"2021-07-06","arxiv_id":"2107.07331","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-mobility-recognition-for-intensive-care","title":"Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers","date":"2021-06-28","arxiv_id":"2106.15017","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-human-aware-robot-navigation","title":"A Survey on Human-aware Robot Navigation","date":"2021-06-22","arxiv_id":"2106.11650","repositories_listed":0,"syntology":null},{"url":null,"slug":"palmar-towards-adaptive-multi-inhabitant","title":"PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology","date":"2021-06-22","arxiv_id":"2106.11902","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-prototype-learning-for","title":"Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification","date":"2021-06-17","arxiv_id":"2106.09636","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-eye-tracking-using-deep","title":"Privacy-Preserving Eye-tracking Using Deep Learning","date":"2021-06-17","arxiv_id":"2106.09621","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-term-object-detection-and-tracking-in","title":"Long Term Object Detection and Tracking in Collaborative Learning Environments","date":"2021-06-02","arxiv_id":"2106.07556","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-embedding-networks-for-robust","title":"Similarity Embedding Networks for Robust Human Activity Recognition","date":"2021-05-31","arxiv_id":"2106.15283","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-deep-neural-network-ensembles-by","title":"Evaluating Deep Neural Network Ensembles by Majority Voting cum Meta-Learning scheme","date":"2021-05-09","arxiv_id":"2105.03819","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-models-in-ontology","title":"Human Activity Recognition Models in Ontology Networks","date":"2021-05-05","arxiv_id":"2105.02264","repositories_listed":0,"syntology":null},{"url":null,"slug":"three-stream-network-for-enriched-action","title":"Three-stream network for enriched Action Recognition","date":"2021-04-27","arxiv_id":"2104.13051","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalized-semi-supervised-federated","title":"Personalized Semi-Supervised Federated Learning for Human Activity Recognition","date":"2021-04-15","arxiv_id":"2104.08094","repositories_listed":0,"syntology":null},{"url":null,"slug":"affinity-based-hierarchical-learning-of","title":"Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition","date":"2021-04-11","arxiv_id":"2104.04889","repositories_listed":0,"syntology":null},{"url":"/paper/multi-gat-a-graphical-attention-based","slug":"multi-gat-a-graphical-attention-based","title":"Multi-GAT: A Graphical Attention-based Hierarchical Multimodal Representation Learning Approach for Human Activity Recognition","date":"2021-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-analysis-and-recognition-from","title":"Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques","date":"2021-03-30","arxiv_id":"2103.16490","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-of-human-activity-recognition","title":"An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence","date":"2021-03-29","arxiv_id":"2103.15990","repositories_listed":0,"syntology":null},{"url":null,"slug":"am-i-fit-for-this-physical-activity-neural","title":"Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors","date":"2021-03-22","arxiv_id":"2103.12095","repositories_listed":0,"syntology":null},{"url":null,"slug":"physical-activity-recognition-based-on-a","title":"Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers","date":"2021-03-02","arxiv_id":"2103.01859","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-deep","title":"Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data","date":"2021-02-28","arxiv_id":"2103.03836","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-future-wireless","title":"Transfer Learning for Future Wireless Networks: A Comprehensive Survey","date":"2021-02-15","arxiv_id":"2102.07572","repositories_listed":0,"syntology":null},{"url":null,"slug":"ahar-adaptive-cnn-for-energy-efficient-human","title":"AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices","date":"2021-02-03","arxiv_id":"2102.01875","repositories_listed":0,"syntology":null},{"url":null,"slug":"provably-secure-federated-learning-against","title":"Provably Secure Federated Learning against Malicious Clients","date":"2021-02-03","arxiv_id":"2102.01854","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-the-significance-of-adversarial","title":"Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems","date":"2021-01-26","arxiv_id":"2101.10562","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-interaction-recognition-framework-based","title":"Human Interaction Recognition Framework based on Interacting Body Part Attention","date":"2021-01-22","arxiv_id":"2101.08967","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-generated-hierarchical-structure-of","title":"Machine-Generated Hierarchical Structure of Human Activities to Reveal How Machines Think","date":"2021-01-19","arxiv_id":"2101.07855","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-multichannel","title":"Human Activity Recognition Using Multichannel Convolutional Neural Network","date":"2021-01-17","arxiv_id":"2101.06709","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-multi-stage-training-approach-for","title":"A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network","date":"2021-01-03","arxiv_id":"2101.00702","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-positive-unlabeled-learning-with-a","title":"Deep Positive Unlabeled Learning with a Sequential Bias","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-feature-selection-for-efficient-and","title":"Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"skipw-resource-adaptable-rnn-with-strict","title":"SkipW: Resource adaptable RNN with strict upper computational limit","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"invariant-feature-learning-for-sensor-based","title":"Invariant Feature Learning for Sensor-based Human Activity Recognition","date":"2020-12-14","arxiv_id":"2012.07963","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-predictive-coding-for-human","title":"Contrastive Predictive Coding for Human Activity Recognition","date":"2020-12-09","arxiv_id":"2012.05333","repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-human-activity","title":"Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks","date":"2020-12-05","arxiv_id":"2012.04479","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-with-heterogeneous-labels","title":"Federated Learning with Heterogeneous Labels and Models for Mobile Activity Monitoring","date":"2020-12-04","arxiv_id":"2012.02539","repositories_listed":0,"syntology":null},{"url":"/paper/meva-a-large-scale-multiview-multimodal-video","slug":"meva-a-large-scale-multiview-multimodal-video","title":"MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection","date":"2020-12-02","arxiv_id":"2012.00914","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compact-sequence-encoding-scheme-for-online","title":"A compact sequence encoding scheme for online human activity recognition in HRI applications","date":"2020-12-01","arxiv_id":"2012.00873","repositories_listed":0,"syntology":null},{"url":null,"slug":"yet-it-moves-learning-from-generic-motions-to","title":"Yet it moves: Learning from Generic Motions to Generate IMU data from YouTube videos","date":"2020-11-23","arxiv_id":"2011.11600","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-using-improved","title":"Human activity recognition using improved dynamic image","date":"2020-11-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generic-semi-supervised-adversarial-subject","title":"Generic Semi-Supervised Adversarial Subject Translation for Sensor-Based Human Activity Recognition","date":"2020-11-11","arxiv_id":"2012.03682","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-federated-learning-for","title":"Semi-supervised Federated Learning for Activity Recognition","date":"2020-11-02","arxiv_id":"2011.00851","repositories_listed":0,"syntology":null},{"url":null,"slug":"indrnn-based-long-term-temporal-recognition","title":"A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition","date":"2020-11-01","arxiv_id":"2011.00395","repositories_listed":0,"syntology":null},{"url":"/paper/bubblenet-a-disperse-recurrent-structure-to","slug":"bubblenet-a-disperse-recurrent-structure-to","title":"Bubblenet: A Disperse Recurrent Structure To Recognize Activities","date":"2020-10-30","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-wearable-based-activity","title":"Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion","date":"2020-10-21","arxiv_id":"2010.13713","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-human-activity-recognition-by","title":"Automated Human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network","date":"2020-10-07","arxiv_id":"2010.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-driven-body-pose-encoding-for-human","title":"Attention-Driven Body Pose Encoding for Human Activity Recognition","date":"2020-09-29","arxiv_id":"2009.14326","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-sequence-classification","title":"Semi-supervised sequence classification through change point detection","date":"2020-09-24","arxiv_id":"2009.11829","repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-generalization-for-human-activity","title":"Stacked Generalization for Human Activity Recognition","date":"2020-09-22","arxiv_id":"2009.10312","repositories_listed":0,"syntology":null},{"url":null,"slug":"mars-mixed-virtual-and-real-wearable-sensors","title":"MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model","date":"2020-09-20","arxiv_id":"2009.09404","repositories_listed":0,"syntology":null},{"url":null,"slug":"personalization-in-human-activity-recognition","title":"Personalization in Human Activity Recognition","date":"2020-09-01","arxiv_id":"2009.00268","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-human-activity-recognition-by","title":"Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks","date":"2020-08-26","arxiv_id":"2008.11755","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-intelligent-non-invasive-real-time-human","title":"An Intelligent Non-Invasive Real Time Human Activity Recognition System for Next-Generation Healthcare","date":"2020-08-06","arxiv_id":"2008.02567","repositories_listed":0,"syntology":null},{"url":null,"slug":"spinaps-a-high-performance-spintronic","title":"SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks","date":"2020-08-05","arxiv_id":"2008.02189","repositories_listed":0,"syntology":null},{"url":"/paper/hamlet-a-hierarchical-multimodal-attention-1","slug":"hamlet-a-hierarchical-multimodal-attention-1","title":"HAMLET: A Hierarchical Multimodal Attention-based Human Activity Recognition Algorithm","date":"2020-08-03","arxiv_id":"2008.01148","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-deep-clustering-of-human-activities","title":"Towards Deep Clustering of Human Activities from Wearables","date":"2020-08-02","arxiv_id":"2008.01659","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-a-large-scale-synthetic-dataset-for","title":"Creating a Large-scale Synthetic Dataset for Human Activity Recognition","date":"2020-07-21","arxiv_id":"2007.11118","repositories_listed":0,"syntology":null},{"url":null,"slug":"attend-and-discriminate-beyond-the-state-of","title":"Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors","date":"2020-07-14","arxiv_id":"2007.07172","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-data-imputation-technique-for","title":"An Efficient Data Imputation Technique for Human Activity Recognition","date":"2020-07-08","arxiv_id":"2007.04456","repositories_listed":0,"syntology":null},{"url":null,"slug":"arc-net-activity-recognition-through-capsules","title":"ARC-Net: Activity Recognition Through Capsules","date":"2020-07-06","arxiv_id":"2007.03063","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-activity-recognition-based-on-dynamic","title":"Human Activity Recognition based on Dynamic Spatio-Temporal Relations","date":"2020-06-29","arxiv_id":"2006.16132","repositories_listed":0,"syntology":null},{"url":null,"slug":"background-knowledge-injection-for","title":"Background Knowledge Injection for Interpretable Sequence Classification","date":"2020-06-25","arxiv_id":"2006.14248","repositories_listed":0,"syntology":null},{"url":null,"slug":"danhar-dual-attention-network-for-multimodal","title":"DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors","date":"2020-06-25","arxiv_id":"2006.14435","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-learn-personalised-human-activity","title":"Learning-to-Learn Personalised Human Activity Recognition Models","date":"2020-06-12","arxiv_id":"2006.07472","repositories_listed":0,"syntology":null},{"url":null,"slug":"adasense-adaptive-low-power-sensing-and","title":"AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices","date":"2020-06-10","arxiv_id":"2006.05884","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-human-activity-recognition-using","title":"Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices","date":"2020-06-05","arxiv_id":"2006.03259","repositories_listed":0,"syntology":null},{"url":null,"slug":"entropy-decision-fusion-for-smartphone-sensor","title":"Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition","date":"2020-05-30","arxiv_id":"2006.00367","repositories_listed":0,"syntology":null},{"url":null,"slug":"imutube-automatic-extraction-of-virtual-on","title":"IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition","date":"2020-05-29","arxiv_id":"2006.05675","repositories_listed":0,"syntology":null}],"record_sha256":"bb0ec3bb138116d36ae455c7822fe3468629c3092a5e8d9c9b01b9b3dbe5db62","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}