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1. Full Text Physical human activity recognition using wearable sensors
by Attal, Ferhat and Mohammed, Samer and Dedabrishvili, Mariam and Chamroukhi, Faicel and Oukhellou, Latifa and Amirat, Yacine
Sensors (Switzerland), ISSN 1424-8220, 12/2015, Volume 15, Issue 12, pp. 31314 - 31338
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial... 
Accelerometers | Activity recognition | Data classifiers | Physical activities | Smart spaces | Wearable sensors | ELECTROCHEMISTRY | CHEMISTRY, ANALYTICAL | VALIDATION | CLASSIFICATION | AMBULATORY SYSTEM | FEATURE-SELECTION | POSTURE | INSTRUMENTS & INSTRUMENTATION | MOTION | PATTERN | accelerometers | wearable sensors | TRIAXIAL ACCELEROMETER | smart spaces | data classifiers | physical activities | activity recognition | Monitoring, Ambulatory - instrumentation | Algorithms | Monitoring, Ambulatory - methods | Accelerometry - instrumentation | Clothing | Human Activities - classification | Humans | Adult | Pattern Recognition, Automated | Normal Distribution | Emerging Technologies | Artificial Intelligence | Computer Science
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2. Full Text Window size impact in human activity recognition
by Banos, Oresti and Galvez, Juan-Manuel and Damas, Miguel and Pomares, Hector and Rojas, Ignacio
Sensors (Switzerland), ISSN 1424-8220, 04/2014, Volume 14, Issue 4, pp. 6474 - 6499
Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing... 
Windowing | Human behavior inference | Window size | Segmentation | Activity recognition | Inertial sensing | Wearable sensors | ELECTROCHEMISTRY | CHEMISTRY, ANALYTICAL | windowing | SENSOR | CLASSIFICATION | AMBULATORY SYSTEM | GAIT EVENTS | human behavior inference | MOBILE PHONE | PHYSICAL-ACTIVITY RECOGNITION | INSTRUMENTS & INSTRUMENTATION | segmentation | window size | wearable sensors | TRIAXIAL ACCELEROMETER | BAROMETRIC-PRESSURE | SPATIOTEMPORAL PARAMETERS | inertial sensing | ACCELERATION SIGNALS | activity recognition | Physical Fitness | Human Activities | Exercise | Pattern Recognition, Automated | Humans | Intervals | Design engineering | Systems design | Tradeoffs | Human influences motion | Moving object recognition | Guidelines
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3. Full Text A Survey on Human Activity Recognition using Wearable Sensors
by Lara, Oscar D and Labrador, Miguel A
IEEE Communications Surveys & Tutorials, ISSN 1553-877X, 2013, Volume 15, Issue 3, pp. 1192 - 1209
Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable... 
Accelerometers | Pervasive computing | mobile applications | Feature extraction | context awareness | Human-centric sensing | machine learning | Wearable sensors | PLATFORM | COMPUTER SCIENCE, INFORMATION SYSTEMS | CLASSIFICATION | TELECOMMUNICATIONS | State of the art | Human motion | Architecture | Taxonomy | Medical | Sensors | Wearable | Recognition
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4. Full Text Semantic human activity recognition: A literature review
by Ziaeefard, Maryam and Bergevin, Robert
Pattern Recognition, ISSN 0031-3203, 08/2015, Volume 48, Issue 8, pp. 2329 - 2345
This paper presents an overview of state-of-the-art methods in activity recognition using semantic features. Unlike low-level features, semantic features... 
Human-object interaction | Survey | Attribute | Pose | Poselet | Human activity recognition | Scene | REPRESENTATION | HUMAN MOVEMENT | MODEL | OBJECT | CONTEXT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | EVENT RECOGNITION | TRACKING | BODY | SELECTIVITY | Machine vision
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5. Full Text Human Activity Recognition and Pattern Discovery
by Eunju Kim and Sumi Helal and Cook, D
IEEE Pervasive Computing, ISSN 1536-1268, 01/2010, Volume 9, Issue 1, pp. 48 - 53
In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and... 
activity modeling | Data analysis | Humans | Medical services | pattern discovery | Sensor systems | Pattern recognition | pervasive computing | Pattern analysis | activity recognition | Pervasive computing | Activity recognition | Pattern discovery | Activity modeling | Hidden Markov models | Markov processes | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | Data mining | Standards | ENGINEERING, ELECTRICAL & ELECTRONIC | Human | Alterations | State of the art | Utilities | Human behavior | Monitoring | Recognition
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6. Full Text Human activity recognition from 3D data: A review
by Aggarwal, J.K and Xia, Lu
Pattern Recognition Letters, ISSN 0167-8655, 10/2014, Volume 48, pp. 70 - 80
Human activity recognition has been an important area of computer vision research since the 1980s. Various approaches have been proposed with a great portion... 
3D data | Computer vision | Depth image | Human activity recognition | DENSE | RANGE IMAGES | VISION | INVARIANT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SHAPE | MOTION | OPTICAL-FLOW | TRACKING | HISTOGRAMS | STEREO | Algorithms
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7. Full Text Fusion of Smartphone Motion Sensors for Physical Activity Recognition
by Shoaib, M and Bosch, S and Durmaz, O and Scholten, Johan and Havinga, Paul J.M
Sensors (Switserland), ISSN 1424-8220, 06/2014, Volume 14, Issue 6, pp. 10146 - 10176
For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far,... 
Activity Recognition | Health Monitoring | smartphone sensors | well-being applications | Sensor fusion | Accelerometer | assisted living | Magnetometer | Gyroscope | METIS-305896 | IR-91463 | EWI-24775 | Health monitoring | Smartphone sensors | Wellbeing applications | Activity recognition | Assisted living | ELECTROCHEMISTRY | CHEMISTRY, ANALYTICAL | health monitoring | magnetometer | sensor fusion | CLASSIFICATION | MOBILE-PHONE | wellbeing applications | gyroscope | INSTRUMENTS & INSTRUMENTATION | accelerometer | activity recognition | Humans | Male | Models, Statistical | Walking - classification | Activities of Daily Living - classification | Algorithms | Movement - physiology | Accelerometry - instrumentation | Monitoring, Physiologic - methods | Adult | Monitoring, Physiologic - instrumentation | Accelerometry - methods | Cell Phone | Pattern Recognition, Automated - methods | Smartphones | Accelerometers | Data acquisition | Performance enhancement | Sensors | Gyroscopes | Motion sensors | Recognition
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8. Full Text Eye Movement Analysis for Activity Recognition Using Electrooculography
by Bulling, Andreas and Ward, Jamie A and Gellersen, Hans and Tröster, Gerhard
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 04/2011, Volume 33, Issue 4, pp. 741 - 753
In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an... 
Electrooculography | pattern analysis | feature evaluation and selection | Eyes | signal processing | Ubiquitous computing | Pattern recognition | Video recording | Support vector machines | Wearable computers | Support vector machine classification | Signal processing algorithms | Ear | Acoustic sensors | SYSTEM | TASKS | PERCEPTION | CONTEXT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Eye Movements - physiology | Visual Perception - physiology | Humans | Middle Aged | Adult | Electrooculography - methods | Female | Male | Signal processing | Human-computer interaction | Analysis | Eye movements | Reproduction | Detection | Recognition
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9. Full Text Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition
by Ordóñez, Francisco Javier and Roggen, Daniel
Sensors (Switzerland), ISSN 1424-8220, 01/2016, Volume 16, Issue 1, p. 115
Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that... 
Deep learning | LSTM | Machine learning | Sensor fusion | Neural network | Human activity recognition | Wearable sensors | ELECTROCHEMISTRY | deep learning | CHEMISTRY, ANALYTICAL | INSTRUMENTS & INSTRUMENTATION | human activity recognition | sensor fusion | wearable sensors | machine learning | neural network | Monitoring, Ambulatory - methods | Clothing | Human Activities - classification | Humans | Signal Processing, Computer-Assisted | Machine Learning | Databases, Factual | Neural Networks (Computer)
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10. Full Text A tutorial on human activity recognition using body-worn inertial sensors
by Bulling, Andreas and Blanke, Ulf and Schiele, Bernt
ACM Computing Surveys (CSUR), ISSN 0360-0300, 01/2014, Volume 46, Issue 3, pp. 1 - 33
The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably... 
Activity recognition | on-body inertial sensors | gesture recognition | Activity Recognition Chain (ARC) | On-body inertial sensors | Gesture recognition | Design | Measurement | Standardisation | Algorithms | MOVEMENT | Experimentation | COMPUTER SCIENCE, THEORY & METHODS | ONLINE | Human mechanics | Usage | Sensors | Identification and classification | Human motion | Education | Communities | Pattern recognition | Inertial | Recognition | Best practice
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11. Full Text Human Activity Recognition Process Using 3-D Posture Data
by Gaglio, Salvatore and Re, Giuseppe Lo and Morana, Marco
IEEE Transactions on Human-Machine Systems, ISSN 2168-2291, 10/2015, Volume 45, Issue 5, pp. 586 - 597
In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is... 
Support vector machines | Performance evaluation | Hidden Markov models | kinect | Feature extraction | Cameras | Real-time systems | Human activity recognition | Joints | ACTIONLET ENSEMBLE | SENSORS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | COMPUTER SCIENCE, CYBERNETICS | Datasets | Markov analysis | Human motion | Vector quantization | Image processing | Recall | Mathematical models | Markov models | Human motion body | Recognition | Three dimensional
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12. Full Text Egocentric Daily Activity Recognition via Multitask Clustering
by Yan Yan and Ricci, Elisa and Gaowen Liu and Sebe, Nicu
IEEE Transactions on Image Processing, ISSN 1057-7149, 10/2015, Volume 24, Issue 10, pp. 2984 - 2995
Recognizing human activities from videos is a fundamental research problem in computer vision. Recently, there has been a growing interest in analyzing human... 
Algorithm design and analysis | Visualization | Clustering algorithms | Egocentric Activity Recognition | Multi-task Learning | Cameras | Linear programming | Activity of Daily Living Analysis | Optimization | Videos | multi-task learning | activity of daily living analysis | Egocentric activity recognition | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Activities of Daily Living - classification | Reproducibility of Results | Movement - physiology | Humans | Image Interpretation, Computer-Assisted - methods | Sensitivity and Specificity | Video Recording - methods | Actigraphy - methods | Pattern Recognition, Automated - methods | Photography - methods | Signal processing | Usage | Research | Image processing | Clustering | Algorithms | Tasks | Data sets | Human behavior | Wearable
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