Abstract is: Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology. Due to its multifaceted nature, different fields may refer to activity recognition as plan recognition, goal recognition, intent recognition, behavior recognition, location estimation and location-based services.
| computer science | Q21198 |
| P646 | Freebase ID | /m/03nvl_6 |
| P6366 | Microsoft Academic ID | 121687571 |
| P10283 | OpenAlex ID | C121687571 |
| C2987834672 | ||
| C2988656282 | ||
| P3417 | Quora topic ID | Activity-Recognition |
| P366 | has use | science | Q336 |
| P361 | part of | psychology terminology | Q77468620 |
| Q56916168 | 3-D body joint-specific HMM-based approach for human activity recognition from stereo posture image sequence |
| Q60491125 | A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data |
| Q57444385 | A Benchmark Dataset for Depth Sensor Based Activity Recognition in a Manufacturing Process |
| Q59296833 | A Benchmark Dataset for Human Activity Recognition and Ambient Assisted Living |
| Q102138207 | A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects |
| Q64921098 | A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones. |
| Q112628104 | A Case Study of Novel Landslide Activity Recognition Using ALOS-1 InSAR within the Ragged Mountain Western Hillslope in Gunnison County, Colorado, USA |
| Q91780339 | A Combination of Indoor Localization and Wearable Sensor-Based Physical Activity Recognition to Assess Older Patients Undergoing Subacute Rehabilitation: Baseline Study Results |
| Q100511290 | A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition |
| Q64273372 | A Comparison of Machine Learning and Deep Learning Techniques for Activity Recognition using Mobile Devices |
| Q38921334 | A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition |
| Q118173685 | A Data Analytics Schema for Activity Recognition in Smart Home Environments |
| Q100542708 | A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition |
| Q59533609 | A Deep Structured Model with Radius–Margin Bound for 3D Human Activity Recognition |
| Q93015993 | A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone |
| Q57701958 | A Feature Set Evaluation for Activity Recognition with Body-Worn Inertial Sensors |
| Q39238892 | A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks. |
| Q104140524 | A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition |
| Q62491309 | A Hierarchical Deep Fusion Framework for Egocentric Activity Recognition Using a Wearable Hybrid Sensor System |
| Q64108881 | A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM |
| Q36368567 | A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data. |
| Q31074676 | A Human Activity Recognition System Using Skeleton Data from RGBD Sensors |
| Q118335773 | A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data |
| Q96115702 | A Lean and Performant Hierarchical Model for Human Activity Recognition Using Body-Mounted Sensors |
| Q100761593 | A Machine Learning Approach for Human Activity Recognition |
| Q97521230 | A Method for Sensor-Based Activity Recognition in Missing Data Scenario |
| Q62678817 | A Multimodal Deep Learning Network for Group Activity Recognition |
| Q62669898 | A Novel Activity Recognition Approach Based on Mobile Phone |
| Q42367231 | A Novel Energy-Efficient Approach for Human Activity Recognition |
| Q58522107 | A Novel Framework for Human Activity Recognition with Time Labelled Real Time Sensor Data |
| Q90750112 | A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction |
| Q30377273 | A Novel Wearable Device for Food Intake and Physical Activity Recognition |
| Q39614468 | A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks |
| Q57961862 | A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer’s Patients |
| Q91962932 | A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors |
| Q59890507 | A QUALITATIVE APPROACH FOR ONLINE ACTIVITY RECOGNITION |
| Q91377009 | A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors |
| Q40137668 | A Random Forest-based ensemble method for activity recognition |
| Q62680958 | A Review and Taxonomy of Activity Recognition on Mobile Phones |
| Q114100173 | A Review of Deep Learning-based Human Activity Recognition on Benchmark Video Datasets |
| Q58643632 | A Review of Human Activity Recognition Methods |
| Q42874106 | A Review on Human Activity Recognition Using Vision-Based Method. |
| Q58578257 | A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition |
| Q89242157 | A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band |
| Q64226382 | A Semi-Automatic Annotation Approach for Human Activity Recognition |
| Q111517645 | A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals |
| Q92068907 | A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition |
| Q90773367 | A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory |
| Q60491285 | A Smith-Waterman Local Alignment Approach for Spatial Activity Recognition |
| Q97687370 | A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory |
| Q121089110 | A Study on the Application of TensorFlow Compression Techniques to Human Activity Recognition |
| Q110541578 | A Survey on Deep Learning for Human Activity Recognition |
| Q61908239 | A Triaxial Accelerometer-Based Human Activity Recognition via EEMD-Based Features and Game-Theory-Based Feature Selection |
| Q55406452 | A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution. |
| Q112826840 | A Web-based semantic tagging and activity recognition system for species' accelerometry data |
| Q115377871 | A compact and recursive Riemannian motion descriptor for untrimmed activity recognition |
| Q92314580 | A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system |
| Q104143616 | A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments |
| Q112689331 | A deep neural network model for multi-view human activity recognition |
| Q34209876 | A depth video sensor-based life-logging human activity recognition system for elderly care in smart indoor environments |
| Q59186981 | A framework for unsupervised change detection in activity recognition |
| Q62679629 | A k-nearest neighbors based approach applied to more realistic activity recognition datasets |
| Q33441603 | A lightweight hierarchical activity recognition framework using smartphone sensors |
| Q62669895 | A low power and high accuracy MEMS sensor based activity recognition algorithm |
| Q45399256 | A model for abnormal activity recognition and alert generation system for elderly care by hidden conditional random fields using R-transform and generalized discriminant analysis features |
| Q114830651 | A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data |
| Q62489104 | A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition |
| Q57729053 | A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data |
| Q114959904 | A review of machine learning-based human activity recognition for diverse applications |
| Q38468144 | A sensor and video based ontology for activity recognition in smart environments |
| Q27006329 | A survey of online activity recognition using mobile phones |
| Q57940578 | A survey of video datasets for human action and activity recognition |
| Q83272010 | A system for activity recognition using multi-sensor fusion |
| Q38923221 | A triboelectric motion sensor in wearable body sensor network for human activity recognition |
| Q57701891 | A typology of wearable activity recognition and interaction |
| Q59347099 | A wearable human activity recognition system on a chip |
| Q114384673 | ARService: A Smartphone based Crowd-Sourced Data Collection and Activity Recognition Framework |
| Q51854285 | Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets. |
| Q102051899 | Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network |
| Q82323017 | Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly |
| Q37537709 | Accelerometry-Based Activity Recognition and Assessment in Rheumatic and Musculoskeletal Diseases. |
| Q62829509 | Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study |
| Q62829515 | Accuracy of Samsung Gear S Smartwatch for Activity Recognition: Validation Study (Preprint) |
| Q58880275 | Achieving Pro-Active Guidance of Patients through ADL using Knowledge-Driven Activity Recognition and Complex Semantic Workflows |
| Q62680874 | Active learning with uncertainty sampling for large scale activity recognition in smart homes |
| Q114857006 | Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes |
| Q118356941 | Activity Recognition Based on Spatial-Temporal Attention LSTM |
| Q38632452 | Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors |
| Q58766290 | Activity Recognition Invariant to Wearable Sensor Unit Orientation Using Differential Rotational Transformations Represented by Quaternions |
| Q58049333 | Activity Recognition Model Based on GPS Data, Points of Interest and User Profile |
| Q28596719 | Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances |
| Q47803446 | Activity Recognition Using Complex Network Analysis |
| Q58667377 | Activity Recognition Using Dynamic Instance Activation |
| Q92049959 | Activity Recognition Using Wearable Physiological Measurements: Selection of Features from a Comprehensive Literature Study |
| Q60491305 | Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model |
| Q33453646 | Activity Recognition and Semantic Description for Indoor Mobile Localization |
| Q57717205 | Activity Recognition for Context-aware Hospital Applications: Issues and Opportunities for the Deployment of Pervasive Networks |
| Q33451050 | Activity Recognition for Diabetic Patients Using a Smartphone. |
| Q92571596 | Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion |
| Q58134811 | Activity Recognition for Medical Teamwork Based on Passive RFID |
| Q33454680 | Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting. |
| Q61906211 | Activity Recognition for a Smartphone Based Travel Survey Based on Cross-User History Data |
| Q61906189 | Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System |
| Q90155952 | Activity Recognition from Newborn Resuscitation Videos |
| Q59678421 | Activity Recognition from Trajectory Data |
| Q57860192 | Activity Recognition in Individuals Walking With Assistive Devices: The Benefits of Device-Specific Models |
| Q39208580 | Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle. |
| Q30800037 | Activity Recognition on Streaming Sensor Data |
| Q37170536 | Activity discovery and activity recognition: a new partnership |
| Q60570239 | Activity recognition and resource optimization in mobile cloud through MapReduce |
| Q98662523 | Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers |
| Q70607468 | Activity recognition in beach volleyball using a Deep Convolutional Neural Network |
| Q51124210 | Activity recognition in long‐term electromyograms |
| Q31171809 | Activity recognition in patients with lower limb impairments: do we need training data from each patient? |
| Q34568563 | Activity recognition in planetary navigation field tests using classification algorithms applied to accelerometer data. |
| Q112690848 | Activity recognition of FMCW radar human signatures using tower convolutional neural networks |
| Q48598723 | Activity recognition of assembly tasks using body-worn microphones and accelerometers |
| Q57661017 | Activity recognition of railway passengers by fusion of low-power sensors in mobile phones |
| Q114086559 | Activity recognition on smartphones using an AKNN based support vectors |
| Q51295429 | Activity recognition using a mixture of vector fields. |
| Q37227824 | Activity recognition using a single accelerometer placed at the wrist or ankle |
| Q57790259 | Activity recognition using a wrist-worn inertial measurement unit: A case study for industrial assembly lines |
| Q58642666 | Activity recognition using conditional random field |
| Q51443736 | Activity recognition using correlated pattern mining for people with dementia. |
| Q86021470 | Activity recognition using dynamic multiple sensor fusion in body sensor networks |
| Q30452556 | Activity recognition using hybrid generative/discriminative models on home environments using binary sensors |
| Q59678731 | Activity recognition via user-trace segmentation |
| Q62680894 | Activity recognition with Hidden Markov models using active learning |
| Q56594425 | Activity recognition with smartphone sensors |
| Q33440711 | Activity recognition with smartphone support |
| Q42370279 | Activity recognition with wearable sensors on loose clothing |
| Q60491288 | AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition |
| Q58799716 | Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer |
| Q51854412 | Advancing from offline to online activity recognition with wearable sensors. |
| Q58992294 | Affine-Invariant Feature Extraction for Activity Recognition |
| Q61913639 | Agent-oriented activity recognition in the event calculus: An application for diabetic patients |
| Q57961876 | Ambient Activity Recognition in Smart Environments for Cognitive Assistance |
| Q50422336 | An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial AccelerometerWrist-Band. |
| Q91962235 | An Activity-Aware Sampling Scheme for Mobile Phones in Activity Recognition |
| Q59130331 | An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks |
| Q57497720 | An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork |
| Q92875055 | An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning |
| Q94566789 | An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare |
| Q41548785 | An activity recognition model using inertial sensor nodes in a wireless sensor network for frozen shoulder rehabilitation exercises |
| Q41196372 | An adaptive Hidden Markov model for activity recognition based on a wearable multi-sensor device |
| Q38923706 | An asynchronous multi-view learning approach for activity recognition using wearables |
| Q45751036 | An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. |
| Q33439393 | An investigation into non-invasive physical activity recognition using smartphones |
| Q64275868 | Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring |
| Q114932695 | Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition |
| Q91964938 | Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition |
| Q52621472 | Anticholinesterase activity; recognition and detection in the field and hospital. |
| Q64272443 | Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data |
| Q57701892 | Assessing activity recognition feedback in long-term psychology trials |
| Q50543329 | Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals. |
| Q92371561 | Assessment of an On-board Classifier for Activity Recognition on an Active Back-Support Exoskeleton |
| Q42140598 | Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing |
| Q96131205 | Author Correction: Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks |
| Q89481673 | Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone |
| Q64057203 | Automatic Detection of Faults in Race Walking: A Comparative Analysis of Machine-Learning Algorithms Fed with Inertial Sensor Data |
| Q98619448 | Badminton Activity Recognition Using Accelerometer Data |
| Q68693253 | Bayesian Optimization of Neural Architectures for Human Activity Recognition |
| Q96296463 | Behavioral Activity Recognition Based on Gaze Ethograms |
| Q57657723 | Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition |
| Q50998841 | Body movement activity recognition for ambulatory cardiac monitoring. |
| Q59635348 | Bootstrapping Personalised Human Activity Recognition Models Using Online Active Learning |
| Q121074412 | Building Lightweight Deep learning Models with TensorFlow Lite for Human Activity Recognition on Mobile Devices |
| Q92696386 | Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks |
| Q45944606 | CNN based approach for activity recognition using a wrist-worn accelerometer. |
| Q56878446 | CapTable and CapShelf - Unobtrusive Activity Recognition Using Networked Capacitive Sensors |
| Q57961857 | Challenging Issues of Ambient Activity Recognition for Cognitive Assistance |
| Q91378253 | Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition |
| Q43972744 | Child activity recognition based on cooperative fusion model of a triaxial accelerometer and a barometric pressure sensor |
| Q93150148 | Children Activity Recognition: Challenges and Strategies |
| Q58692996 | Chord-Length Shape Features for Human Activity Recognition |
| Q91482031 | Classifier Personalization for Activity Recognition Using Wrist Accelerometers |
| Q96748969 | Clustering Approach to the Problem of Human Activity Recognition using Motion Data |
| Q28655657 | Clustering-based ensemble learning for activity recognition in smart homes |
| Q64099352 | Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition |
| Q92072311 | Coherence Constrained Graph LSTM for Group Activity Recognition |
| Q57961868 | Combining Pervasive Computing with Activity Recognition and Learning |
| Q57715739 | Combining discriminative spatiotemporal features for daily life activity recognition using wearable motion sensing suit |
| Q40646399 | Combining users' activity survey and simulators to evaluate human activity recognition systems. |
| Q96947336 | Comparing Person-Specific and Independent Models on Subject-Dependent and Independent Human Activity Recognition Performance |
| Q92511403 | Comparison of Automated Activity Recognition to Provider Observations of Patient Mobility in the ICU |
| Q58427361 | Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 |
| Q60930174 | Comparison of Different Sets of Features for Human Activity Recognition by Wearable Sensors |
| Q50421355 | Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. |
| Q33447902 | Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors |
| Q92278069 | Consistency of Outputs of the Selected Motion Acquisition Methods for Human Activity Recognition |
| Q105575903 | Context aware approach for activity recognition based on precondition-effect rules |
| Q110923628 | Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors |
| Q89778160 | Convolutional and recurrent neural network for human activity recognition: Application on American sign language |
| Q114959888 | Correction to: A review of machine learning-based human activity recognition for diverse applications |
| Q98618857 | Correction: Garcia-Gonzalez, D.; Rivero, D.; Fernandez-Blanco, E.; Luaces, M.R. A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors. Sensors 2020, 20, 2200 |
| Q114670792 | Corrigendum to “A web-based semantic tagging and activity recognition system for species' accelerometry data” [Ecol. Inf. 13 (2013) 47–56] |
| Q59678665 | Cross-domain activity recognition |
| Q59678459 | Cross-domain activity recognition via transfer learning |
| Q44283298 | Cross-person activity recognition using reduced kernel extreme learning machine |
| Q114380708 | Crowd activity recognition in live video streaming via 3D‐ResNet and region graph convolution network |
| Q100002722 | Daily Activity Recognition Based on Triaxial Accelerometer of Elderly People |
| Q59119884 | Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine |
| Q48411790 | Daily activity recognition system for the elderly using pressure sensors |
| Q100448773 | Data Mining and Fusion of Unobtrusive Sensing Solutions for Indoor Activity Recognition |
| Q99562847 | Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition |
| Q62773348 | Day or Night Activity Recognition From Video Using Fuzzy Clustering Techniques |
| Q33986566 | Dealing with the effects of sensor displacement in wearable activity recognition |
| Q35900770 | Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition |
| Q114891876 | Deep Ensemble Learning for Human Activity Recognition Using Wearable Sensors via Filter Activation |
| Q104493748 | Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone |
| Q92706767 | Deep Learning for RFID-Based Activity Recognition |
| Q110541568 | Deep Learning for Sensor-based Human Activity Recognition |
| Q102065504 | Deep Learning-Based Human Activity Recognition for Continuous Activity and Gesture Monitoring for Schizophrenia Patients With Negative Symptoms |
| Q92104868 | Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition |
| Q90090328 | Deep Neural Network for RFID-Based Activity Recognition |
| Q111518307 | Deep transfer learning based human activity recognition by transforming IMU data to image domain using novel activity image creation method |
| Q114959834 | Deep-learning-based human activity recognition for Alzheimer’s patients’ daily life activities assistance |
| Q26859894 | Design and test of a hybrid foot force sensing and GPS system for richer user mobility activity recognition |
| Q91454757 | Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research |
| Q45901520 | Designing a robust activity recognition framework for health and exergaming using wearable sensors. |
| Q59892259 | Designing and Testing HealthTracker for Activity Recognition and Energy Expenditure Estimation within the DAPHNE Platform |
| Q115074826 | Development and validation of smartwatch-based activity recognition models for rigging crew workers on cable logging operations |
| Q89604357 | Development of a Human Activity Recognition System for Ballet Tasks |
| Q57426384 | Device-Free Human Activity Recognition Using Commercial WiFi Devices |
| Q114960197 | Device-free cross location activity recognition via semi-supervised deep learning |
| Q57701905 | Diary-Like Long-Term Activity Recognition: Touch or Voice Interaction? |
| Q58012175 | Discriminative Hierarchical Rank Pooling for Activity Recognition |
| Q52609669 | Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening. |
| Q92695521 | Domain Adaptation in Children Activity Recognition |
| Q118421227 | Domestic Activity Recognition Based on Attention Capsule Network |
| Q93148744 | Dynamic Time-frequency Feature Extraction for Brain Activity Recognition |
| Q39620437 | Dynamic detection of window starting positions and its implementation within an activity recognition framework |
| Q59296856 | Dynamic sensor event segmentation for real-time activity recognition in a smart home context |
| Q58799920 | Dynamic sliding window method for physical activity recognition using a single tri-axial accelerometer |
| Q92336411 | Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors |
| Q60491224 | Efficient duration and hierarchical modeling for human activity recognition |
| Q40843542 | Egocentric daily activity recognition via multitask clustering. |
| Q109299271 | Emotions and Activity Recognition System Using Wearable Device Sensors |
| Q101048060 | Empirical Mode Decomposition Based Multi-Modal Activity Recognition |
| Q61455256 | Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition |
| Q100447032 | Enabling context aware data analysis for long-duration repetitive stooped work through human activity recognition in sheep shearing |
| Q59129866 | Energy Optimization for Outdoor Activity Recognition |
| Q62598919 | Energy and Accuracy Trade-Offs in Accelerometry-Based Activity Recognition |
| Q59130375 | Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices |
| Q92630926 | Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model |
| Q58799801 | Enhancing ontological reasoning with uncertainty handling for activity recognition |
| Q58813865 | Enlarge the Training Data for Activity Recognition |
| Q58667199 | Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition |
| Q104809232 | Ensemble residual network-based gender and activity recognition method with signals |
| Q57629672 | Epileptic activity recognition in EEG recording |
| Q112937954 | Evaluating a single-modality ground-based activity recognition sensor for human inclusion into digital systems |
| Q90773489 | Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition |
| Q105575901 | Evaluating the robustness of activity recognition using computational causal behavior models |
| Q33446451 | Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment |
| Q60584220 | Evaluation of a skeleton-based method for human activity recognition on a large-scale RGB-D dataset |
| Q33446875 | Evaluation of a smartphone human activity recognition application with able-bodied and stroke participants |
| Q51101512 | Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. |
| Q60570404 | Evidential fusion of sensor data for activity recognition in smart homes |
| Q54989139 | Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments. |
| Q57149263 | Examining Sensor-based Physical Activity Recognition and Monitoring for Healthcare Using Internet of Things: A Systematic Review |
| Q92696393 | Experimental Analysis of Artificial Neural Networks Performance for Physical Activity Recognition Using Belt and Wristband Devices |
| Q114782285 | Exploiting Smart Meter Power Consumption Measurements for Human Activity Recognition (HAR) with a Motif-Detection-Based Non-Intrusive Load Monitoring (NILM) Approach |
| Q30670955 | Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones |
| Q57702008 | Exploring semi-supervised and active learning for activity recognition |
| Q36830273 | Exploring techniques for vision based human activity recognition: methods, systems, and evaluation |
| Q113312369 | Extreme Learning Machine-Based Deep Model for Human Activity Recognition With Wearable Sensors |
| Q29544919 | Eye Movement Analysis for Activity Recognition Using Electrooculography |
| Q55932510 | Eye movement analysis for activity recognition |
| Q66682370 | Eye movement analysis for activity recognition based on one web camera |
| Q98885944 | Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones |
| Q92561539 | Fast Collective Activity Recognition Under Weak Supervision |
| Q77682295 | Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition |
| Q57790223 | Feature Selection and Activity Recognition to Detect Water Waste from Water Tap Usage |
| Q59123441 | Feature Selection for Tracker-Less Human Activity Recognition |
| Q59891697 | Feature Space Analysis for Human Activity Recognition in Smart Environments |
| Q58667452 | Feature Sub-set Selection for Activity Recognition |
| Q59525143 | Feature extraction for robust physical activity recognition |
| Q39209124 | Feature selection and activity recognition system using a single triaxial accelerometer |
| Q33443302 | Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients |
| Q41601297 | Feature selection in classification of eye movements using electrooculography for activity recognition |
| Q56928755 | Fitness Activity Recognition on Smartphones Using Doppler Measurements |
| Q112557443 | Framework for Simultaneous Indoor Localization, Mapping, and Human Activity Recognition in Ambient Assisted Living Scenarios |
| Q57664410 | From Activity Recognition to Motion Assessment: Delimitate against the Other Class within a WBAN |
| Q62041884 | From action to activity: Sensor-based activity recognition |
| Q90233682 | Fusing Object Information and Inertial Data for Activity Recognition |
| Q33441120 | Fusion of smartphone motion sensors for physical activity recognition |
| Q36213326 | Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement |
| Q115060918 | Gait and activity recognition using commercial phones |
| Q59606449 | Gated spatio and temporal convolutional neural network for activity recognition: towards gated multimodal deep learning |
| Q43831572 | Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people. |
| Q56524736 | Graph formulation of video activities for abnormal activity recognition |
| Q118357742 | Group Activity Recognition Based on GFU and Hierarchical LSTM |
| Q118357719 | Group Activity Recognition Method Based on Pseudo 3D Residual Network and Interaction Modeling |
| Q60570248 | HARF: A Hierarchical Activity Recognition Framework Using Smartphone Sensors |
| Q90811961 | Habit Representation Based on Activity Recognition |
| Q61655511 | Hamon: An activity recognition framework for health monitoring support at home |
| Q35279998 | Hessian-regularized co-training for social activity recognition |
| Q101239896 | HiGCIN: Hierarchical Graph-based Cross Inference Network for Group Activity Recognition |
| Q64110891 | Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables |
| Q90109468 | Hidden Markov model-based activity recognition for toddlers |
| Q37380880 | Hierarchical Activity Recognition Using Smart Watches and RGB-Depth Cameras |
| Q33439813 | Hierarchical classifier approach to physical activity recognition via wearable smartphone tri-axial accelerometer |
| Q118380618 | Hierarchical multi-observation model for multi-person activity recognition |
| Q90076652 | Hierarchical multi-view aggregation network for sensor-based human activity recognition |
| Q36205455 | High Accuracy Human Activity Recognition Based on Sparse Locality Preserving Projections |
| Q58624303 | High dimensional low sample size activity recognition using geometric classifiers |
| Q92422241 | Highly Accurate Bathroom Activity Recognition using Infrared Proximity Sensors |
| Q91805086 | Host-Parasite: Graph LSTM-in-LSTM for Group Activity Recognition |
| Q114370800 | HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data |
| Q98200845 | Human Activity Recognition Based on Dynamic Active Learning |
| Q59794124 | Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors |
| Q59112624 | Human Activity Recognition Based on the Hierarchical Feature Selection and Classification Framework |
| Q95574407 | Human Activity Recognition Supported on Indoor Localization: A Systematic Review |
| Q113222548 | Human Activity Recognition System Using Smartphone Data Sensors with Python and Machine Learning |
| Q59050271 | Human Activity Recognition Using Binary Sensors, BLE Beacons, an Intelligent Floor and Acceleration Data: A Machine Learning Approach |
| Q92504076 | Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields |
| Q92082607 | Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview |
| Q62043212 | Human Activity Recognition Using Radial Basis Function Neural Network Trained via a Minimization of Localized Generalization Error |
| Q58886307 | Human Activity Recognition Using Recurrent Neural Networks |
| Q42575472 | Human Activity Recognition and Pattern Discovery. |
| Q59119419 | Human Activity Recognition as Time-Series Analysis |
| Q62040305 | Human Activity Recognition by Class Label LLE |
| Q50877546 | Human Activity Recognition by Combining a Small Number of Classifiers. |
| Q58796315 | Human Activity Recognition for Physical Rehabilitation |
| Q63443095 | Human Activity Recognition from Accelerometer Data Using a Wearable Device |
| Q52319440 | Human Activity Recognition from Body Sensor Data using Deep Learning. |
| Q30982611 | Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring |
| Q57722644 | Human Activity Recognition from automatically labeled data in RGB-D videos |
| Q37060693 | Human Activity Recognition in AAL Environments Using Random Projections |
| Q58778693 | Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine |
| Q58778574 | Human Activity Recognition on Smartphones with Awareness of Basic Activities and Postural Transitions |
| Q104508573 | Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey |
| Q113222710 | Human Activity Recognition using OpenCv & Python |
| Q62669836 | Human Activity Recognition with Smart Watch Based on H-SVM |
| Q114730687 | Human Activity Recognition: Review, Taxonomy and Open Challenges |
| Q59125934 | Human Depth Sensors-Based Activity Recognition Using Spatiotemporal Features and Hidden Markov Model for Smart Environments |
| Q64273440 | Human Physical Activity Recognition Using Smartphone Sensors |
| Q104477736 | Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning |
| Q60240296 | Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture |
| Q39827923 | Human activity recognition based on Evolving Fuzzy Systems. |
| Q51068033 | Human activity recognition based on feature selection in smart home using back-propagation algorithm. |
| Q57856031 | Human activity recognition based on kinematic features |
| Q58624324 | Human activity recognition by separating style and content |
| Q62040031 | Human activity recognition from accelerometer data using Convolutional Neural Network |
| Q93144490 | Human activity recognition from inertial sensor time-series using batch normalized deep LSTM recurrent networks |
| Q112689262 | Human activity recognition in artificial intelligence framework: a narrative review |
| Q58589328 | Human activity recognition in the context of industrial human-robot interaction |
| Q90664315 | Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks |
| Q64461850 | Human activity recognition using multisensor data fusion based on Reservoir Computing |
| Q59243826 | Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer |
| Q57922589 | Human activity recognition: Various paradigms |
| Q30674160 | Human body contour data based activity recognition |
| Q43880591 | Human daily activity recognition with sparse representation using wearable sensors |
| Q46244917 | IDEEA activity monitor: validity of activity recognition for lying, reclining, sitting and standing |
| Q58309523 | IMPROVING MULTI-CAMERA ACTIVITY RECOGNITION BY EMPLOYING NEURAL NETWORK BASED READJUSTMENT |
| Q104493466 | IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition |
| Q60485558 | Identifying Important Action Primitives for High Level Activity Recognition |
| Q87675526 | Impact of Sensor Misplacement on Dynamic Time Warping Based Human Activity Recognition using Wearable Computers |
| Q40272639 | Implementation study of wearable sensors for activity recognition systems |
| Q38503644 | Implementing evidential activity recognition in sensorised homes. |
| Q95574467 | Importance of Personalized Health-Care Models: A Case Study in Activity Recognition |
| Q99360546 | Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals |
| Q38416882 | Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis |
| Q58493883 | Improved eigenspectrum regularisation for human activity recognition |
| Q35995179 | Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients. |
| Q51154136 | Improving activity recognition using temporal coherence. |
| Q57701935 | Improving activity recognition without sensor data |
| Q48049306 | Improving human activity recognition and its application in early stroke diagnosis. |
| Q62043642 | Improving kNN for Human Activity Recognition with Privileged Learning Using Translation Models |
| Q58667418 | Improving the Quality of User Generated Data Sets for Activity Recognition |
| Q113311502 | In-Home Activity Recognition: Bayesian Inference for Hidden Markov Models |
| Q38976014 | In-lab versus at-home activity recognition in ambulatory subjects with incomplete spinal cord injury |
| Q60570377 | Incorporating Duration Information in Activity Recognition |
| Q91490754 | Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration |
| Q51854287 | Independent component feature-based human activity recognition via Linear Discriminant Analysis and Hidden Markov Model. |
| Q30370425 | Inferring Human Activity Recognition with Ambient Sound on Wireless Sensor Nodes. |
| Q59044602 | Interaction analysis: An algorithm for interaction prediction and activity recognition in adaptive systems |
| Q114973334 | Internet-of-Things-Based Suspicious Activity Recognition Using Multimodalities of Computer Vision for Smart City Security |
| Q59678485 | Introduction to the special issue on intelligent systems for activity recognition |
| Q59809391 | Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition |
| Q111897505 | KDA-Based WKNN-SVM Method for Activity Recognition System From Smartphone Data |
| Q115740768 | KU-HAR: An open dataset for heterogeneous human activity recognition |
| Q97687726 | LARa: Creating a Dataset for Human Activity Recognition in Logistics Using Semantic Attributes |
| Q57337089 | Layered representations for human activity recognition |
| Q57180943 | Learning Compact Features for Human Activity Recognition via Probabilistic First-Take-All |
| Q57770255 | Learning Deep and Shallow Features for Human Activity Recognition |
| Q60491243 | Learning Discriminative Sequence Models from Partially Labelled Data for Activity Recognition |
| Q91882362 | Learning Semantics-Preserving Attention and Contextual Interaction for Group Activity Recognition |
| Q56529374 | Learning discriminative context models for concurrent collective activity recognition |
| Q56504300 | Learning general model for activity recognition with limited labelled data |
| Q41227871 | Learning person-person interaction in collective activity recognition |
| Q51529825 | Letter by Iosa et al regarding article, "Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke". |
| Q99348040 | Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders |
| Q113310995 | Lifelong Learning in Sensor-Based Human Activity Recognition |
| Q115409413 | Local Domain Adaptation for Cross-Domain Activity Recognition |
| Q38758033 | Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons. |
| Q77685629 | Location-based activity recognition |
| Q30872592 | Long-term activity recognition from wristwatch accelerometer data |
| Q38922912 | Longitudinal wearable tremor measurement system with activity recognition algorithms for upper limb tremor |
| Q33442964 | Low energy physical activity recognition system on smartphones |
| Q90383247 | Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor |
| Q58422576 | Lower limb gait activity recognition using Inertial Measurement Units for rehabilitation robotics |
| Q60907207 | MBOSS: A Symbolic Representation of Human Activity Recognition Using Mobile Sensors |
| Q111288335 | MBOSS: a symbolic representation of human activity recognition using mobile sensors |
| Q112820539 | MIMU-Wear: Ontology-based sensor selection for real-world wearable activity recognition |
| Q101562257 | Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review |
| Q62489556 | Machine and deep learning for workflow recognition during surgery |
| Q59340238 | Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy |
| Q30863805 | Machine learning for activity recognition: hip versus wrist data |
| Q33446248 | Making Activity Recognition Robust against Deceptive Behavior |
| Q90774574 | Margin-Based Deep Learning Networks for Human Activity Recognition |
| Q38630522 | Matrix and Tensor Completion on a Human Activity Recognition Framework |
| Q58613677 | MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns |
| Q114859120 | MiTAR: a study on human activity recognition based on NLP with microscopic perspective |
| Q99630628 | Mobile sensor based human activity recognition: distinguishing of challenging activities by applying long short-term memory deep learning modified by residual network concept |
| Q63971438 | Model systems for flavoenzyme activity: Recognition and redox modulation of flavin mononucleotide in water using nanoparticles |
| Q57790107 | Model update in wearable sensors based human activity recognition |
| Q51447829 | Modelling of distributed activity recognition in the home environment. |
| Q105583391 | Monitoring children’s developmental progress using augmented toys and activity recognition |
| Q112684581 | Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations |
| Q118382509 | Multi-sensor data fusion for complex human activity recognition |
| Q85744965 | Multi-sensor physical activity recognition in free-living |
| Q42257732 | Multi-view human activity recognition in distributed camera sensor networks |
| Q90579958 | Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound |
| Q58589176 | Multimodal Human Activity Recognition for Industrial Manufacturing Processes in Robotic Workcells |
| Q30418919 | Multimodal physical activity recognition by fusing temporal and cepstral information |
| Q92443975 | Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments |
| Q91567720 | Novel evaluation of surgical activity recognition models using task-based efficiency metrics |
| Q82565647 | Nursing activity recognition using an inexpensive game controller: An application to infection control |
| Q94317446 | O11.3. DEEP LEARNING-BASED HUMAN ACTIVITY RECOGNITION FOR CONTINUOUS ACTIVITY AND GESTURE MONITORING FOR SCHIZOPHRENIA PATIENTS WITH NEGATIVE SYMPTOMS |
| Q115351975 | On advances in differential-geometric approaches for 2D and 3D shape analyses and activity recognition |
| Q59635386 | On strategies for budget-based online annotation in human activity recognition |
| Q39549633 | On the Design of Smart Homes: A Framework for Activity Recognition in Home Environment. |
| Q41860488 | On the use of sensor fusion to reduce the impact of rotational and additive noise in human activity recognition |
| Q58667161 | Ontology-based feature generation to improve accuracy of activity recognition in smart environments |
| Q89634433 | Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes |
| Q56928874 | Opportunities for activity recognition using ultrasound doppler sensing on unmodified mobile phones |
| Q58667246 | Optimizing the configuration of an heterogeneous architecture of sensors for activity recognition, using the extended belief rule-based inference methodology |
| Q58051209 | Out-of-Home Activity Recognition from GPS Data in Schizophrenic Patients |
| Q114022670 | PDEs on graphs for semi-supervised learning applied to first-person activity recognition in body-worn video |
| Q57961852 | POSSIBILISTIC ACTIVITY RECOGNITION IN SMART HOMES FOR COGNITIVELY IMPAIRED PEOPLE |
| Q114960116 | Patient activity recognition using radar sensors and machine learning |
| Q115075515 | Patient handling activity recognition through pressure-map manifold learning using a footwear sensor |
| Q58887778 | Pedestrian Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition |
| Q62680988 | Performance evaluation of classification methods for online activity recognition on smart phones |
| Q57790115 | Personal models for eHealth - improving user-dependent human activity recognition models using noise injection |
| Q44048907 | Personalization Algorithm for Real-Time Activity Recognition Using PDA, Wireless Motion Bands, and Binary Decision Tree |
| Q93146244 | Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer |
| Q64324890 | Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band |
| Q34371761 | Personalized adherence activity recognition via model-driven sensor data assessment. |
| Q92437871 | Personalizing Activity Recognition Models with Quantifying Different Types of Uncertainty Using Wearable Sensors |
| Q112687065 | PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos |
| Q35112189 | Physical Activity Recognition Based on Motion in Images Acquired by a Wearable Camera |
| Q36378511 | Physical Activity Recognition using Posterior-adapted Class-based Fusion of Multi-Accelerometers data |
| Q26774234 | Physical Human Activity Recognition Using Wearable Sensors |
| Q92154924 | Physical Workload Tracking Using Human Activity Recognition with Wearable Devices |
| Q30883512 | Physical activity recognition based on rotated acceleration data using quaternion in sedentary behavior: a preliminary study |
| Q62812699 | Physical activity recognition via minimal in-shoes force sensor configuration |
| Q105575899 | Plan Synthesis for Probabilistic Activity Recognition |
| Q59889337 | Planning meets activity recognition: Service coordination for intelligent buildings |
| Q35986207 | Posture and activity recognition and energy expenditure estimation in a wearable platform |
| Q41605991 | Posture and activity recognition and energy expenditure prediction in a wearable platform |
| Q114070915 | Predicting Performance Improvement of Human Activity Recognition Model by Additional Data Collection |
| Q92779522 | Proactively Guiding Patients Through ADL via Knowledge-Based and Context-Driven Activity Recognition |
| Q57913138 | Probabilistic human daily activity recognition towards robot-assisted living |
| Q51589055 | Probabilistic image modeling with an extended chain graph for human activity recognition and image segmentation. |
| Q62048683 | Proceedings of the 4th international Workshop on Sensor-based Activity Recognition and Interaction - iWOAR '17 |
| Q59898235 | Qualitative and Quantitative Spatio-temporal Relations in Daily Living Activity Recognition |
| Q90393468 | Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition |
| Q115221680 | Rate-Invariant Modeling in Lie Algebra for Activity Recognition |
| Q57790182 | Ready-to-use activity recognition for smartphones |
| Q59678758 | Real world activity recognition with multiple goals |
| Q93145033 | Real-Time Human Physical Activity Recognition with Low Latency Prediction Feedback Using Raw IMU Data |
| Q61832716 | Real-time Activity Recognition by Discerning Qualitative Relationships Between Randomly Chosen Visual Features |
| Q100448556 | Recurrence Quantification Analysis for Human Activity Recognition |
| Q58667495 | Reducing the Response Time for Activity Recognition Through use of Prototype Generation Algorithms |
| Q58133668 | Region-based Activity Recognition Using Conditional GAN |
| Q77655802 | Relevance Topic Model for Unstructured Social Group Activity Recognition |
| Q30620986 | Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke |
| Q118420918 | Research Advances on Human Activity Recognition Datasets |
| Q58780719 | Research on Construction Workers' Activity Recognition Based on Smartphone |
| Q118357758 | Research on Family Activity Recognition Method Based on Additive Margin Capsule Network |
| Q58813845 | Review of Sensor-based Activity Recognition Systems |
| Q89556172 | Robust Activity Recognition for Aging Society |
| Q35972074 | Robust Indoor Human Activity Recognition Using Wireless Signals |
| Q90775404 | Robust Stride Detector from Ankle-Mounted Inertial Sensors for Pedestrian Navigation and Activity Recognition with Machine Learning Approaches |
| Q62048376 | Robust multi-dimensional motion features for first-person vision activity recognition |
| Q100447251 | S-Convnet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images |
| Q57337075 | S-SEER: Selective Perception in a Multimodal Office Activity Recognition System |
| Q58613962 | SP-ACT: A hybrid framework for complex activity recognition combining OWL and SPARQL rules |
| Q92528762 | Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition |
| Q112683870 | Self-supervised representation learning for surgical activity recognition |
| Q94944708 | SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition |
| Q38395063 | Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition |
| Q91801534 | Semi-Automated Data Labeling for Activity Recognition in Pervasive Healthcare |
| Q64062006 | Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning |
| Q113311479 | Sensor Placement Variations in Wearable Activity Recognition |
| Q96686919 | Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks |
| Q87092196 | Sensor positioning for activity recognition using wearable accelerometers |
| Q112691507 | Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning |
| Q41606776 | Sensor-based activity recognition using extended belief rule-based inference methodology |
| Q39267704 | Sensor-based surgical activity recognition in unconstrained environments |
| Q57970862 | Shopper Analytics: A Customer Activity Recognition System Using a Distributed RGB-D Camera Network |
| Q114988666 | SimHumalator: An Open-Source End-to-End Radar Simulator for Human Activity Recognition |
| Q114105997 | Simulation framework for activity recognition and benchmarking in different radar geometries |
| Q38652046 | Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors |
| Q100448925 | Smartphone Based Human Activity Recognition with Feature Selection and Dense Neural Network |
| Q33454202 | Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis |
| Q64226943 | Smartphone Sensors for Monitoring Cancer-Related Quality of Life: App Design, EORTC QLQ-C30 Mapping and Feasibility Study in Healthy Subjects |
| Q64277745 | Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network |
| Q33448373 | Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring |
| Q57913112 | Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data |
| Q57633331 | Sparse composition of body poses and atomic actions for human activity recognition in RGB-D videos |
| Q115741723 | Split BiRNN for real-time activity recognition using radar and deep learning |
| Q91552454 | Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications |
| Q35195753 | Step detection and activity recognition accuracy of seven physical activity monitors |
| Q105575906 | Strategies for Modelling Human Behaviour for Activity Recognition with Precondition-Effect Rules |
| Q55128857 | Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries. |
| Q56666538 | Sum Product Networks for Activity Recognition |
| Q118358407 | Summarization of Group Activity Recognition Algorithms Based on Deep Learning Frame |
| Q50938468 | Super Normal Vector for Human Activity Recognition with Depth Cameras. |
| Q105575896 | Supporting activity recognition by visual analytics |
| Q92688731 | Switching Structured Prediction for Simple and Complex Human Activity Recognition |
| Q64118746 | SynSys: A Synthetic Data Generation System for Healthcare Applications |
| Q93000808 | TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition |
| Q45954713 | Team activity recognition in Association Football using a Bag-of-Words-based method. |
| Q62776110 | Temporal Learning Using Echo State Network for Human Activity Recognition |
| Q91897137 | Temporal Reasoning Graph for Activity Recognition |
| Q113311845 | The Mobile Sensing Platform: An Embedded Activity Recognition System |
| Q38665034 | The Role of Heart-Rate Variability Parameters in Activity Recognition and Energy-Expenditure Estimation Using Wearable Sensors |
| Q40502374 | The application of EMD in activity recognition based on a single triaxial accelerometer |
| Q91946251 | Timely daily activity recognition from headmost sensor events |
| Q50668858 | Toward Unobtrusive Patient Handling Activity Recognition for Injury Reduction Among At-Risk Caregivers. |
| Q97557147 | Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition |
| Q58117521 | Towards Human Activity Recognition: A Hierarchical Feature Selection Framework |
| Q58421963 | Towards Intelligent Lower Limb Prostheses with Activity Recognition |
| Q62041880 | Towards unsupervised physical activity recognition using smartphone accelerometers |
| Q30405650 | Tracheal activity recognition based on acoustic signals |
| Q100761626 | Track My Health: An IoT Approach for Data Acquisition and Activity Recognition |
| Q59147033 | Tracking a Subset of Skeleton Joints: An Effective Approach towards Complex Human Activity Recognition |
| Q41987807 | Tracking and activity recognition through consensus in distributed camera networks |
| Q58778682 | Training Computationally Efficient Smartphone–Based Human Activity Recognition Models |
| Q61816126 | Training a classifier for activity recognition using body motion simulation |
| Q42592999 | Transfer Learning for Activity Recognition: A Survey |
| Q89242166 | Transfer Learning for Improved Audio-Based Human Activity Recognition |
| Q96117988 | Transition Activity Recognition System based on Standard Deviation Trend Analysis |
| Q58778433 | Transition-Aware Human Activity Recognition Using Smartphones |
| Q59124411 | Two-Layer Hidden Markov Model for Human Activity Recognition in Home Environments |
| Q108126979 | Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics |
| Q89557370 | Unobtrusive Activity Recognition of Elderly People Living Alone Using Anonymous Binary Sensors and DCNN |
| Q62627637 | Unsupervised Domain Adaptation for Human Activity Recognition |
| Q102073037 | Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition |
| Q94591782 | Unsupervised Human Activity Recognition Using the Clustering Approach: A Review |
| Q58642603 | Unsupervised learning of sensor topologies for improving activity recognition in smart environments |
| Q30408360 | User Activity Recognition in Smart Homes Using Pattern Clustering Applied to Temporal ANN Algorithm |
| Q61447774 | User activity recognition system to improve the performance of environmental control interfaces: a pilot study with patients |
| Q62680972 | User, device and orientation independent human activity recognition on mobile phones |
| Q57790190 | User-Independent Human Activity Recognition Using a Mobile Phone: Offline Recognition vs. Real-Time on Device Recognition |
| Q57790252 | User-independent activity recognition for industrial assembly lines-feature vs. instance selection |
| Q62680895 | Using Active Learning to Allow Activity Recognition on a Large Scale |
| Q114904290 | Using Auditory Features for WiFi Channel State Information Activity Recognition |
| Q58198085 | Using Constraint Optimization for Conflict Resolution and Detail Control in Activity Recognition |
| Q89879892 | Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition |
| Q92659988 | Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments |
| Q90632976 | Using Rough Sets to Improve Activity Recognition Based on Sensor Data |
| Q36377840 | Using mobile phones for activity recognition in Parkinson's patients |
| Q60570353 | Using model-based clustering to discretise duration information for activity recognition |
| Q57702014 | Using rhythm awareness in long-term activity recognition |
| Q114960337 | Utilizing deep learning models in CSI-based human activity recognition |
| Q92376902 | Validity of Instrumented Insoles for Step Counting, Posture and Activity Recognition: A Systematic Review |
| Q92026411 | Video Activity Recognition: State-of-the-Art |
| Q55922317 | Video surveillance and counterterrorism: the application of suspicious activity recognition in visual surveillance systems to counterterrorism |
| Q33644833 | Video-based human activity recognition using multilevel wavelet decomposition and stepwise linear discriminant analysis |
| Q112301806 | Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos |
| Q114950317 | Visual-semantic graph neural network with pose-position attentive learning for group activity recognition |
| Q92696376 | Visualizing Inertial Data For Wearable Sensor Based Daily Life Activity Recognition Using Convolutional Neural Network |
| Q100448423 | Visualizing Worklog Based on Human Working Activity Recognition Using Unsupervised Activity Pattern Encoding |
| Q117284854 | WBAN Path Loss Based Approach For Human Activity Recognition With Machine Learning Techniques |
| Q60484971 | WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes |
| Q36394794 | Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework |
| Q37537375 | Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform |
| Q91741243 | Wearable Sensor-Based Human Activity Recognition via Two-Layer Diversity-Enhanced Multiclassifier Recognition Method |
| Q111517630 | Wearable Sensors for Activity Recognition in Ultimate Frisbee Using Convolutional Neural Networks and Transfer Learning |
| Q58493923 | Within-class subspace regularization for human activity recognition |
| Q89597340 | Zero-Shot Human Activity Recognition Using Non-Visual Sensors |
| Q45943721 | [-25]A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks. |
| Q98718126 | [Human activity recognition based on the inertial information and convolutional neural network] |
| Q57770272 | kNN Sampling for Personalised Human Activity Recognition |
| Q99605365 | w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices |
| Arabic (ar / Q13955) | تعرف الأفعال | wikipedia |
| Activity recognition | wikipedia | |
| Persian (fa / Q9168) | شناسایی فعالیت | wikipedia |
| 행동 인식 | wikipedia |
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