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
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
Journal Article
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
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
Journal Article
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
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
Journal Article
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
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
Journal Article
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
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
Journal Article
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
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
Journal Article
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
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
Journal Article
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
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
Journal Article
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)
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)
Journal Article
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
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
Journal Article
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
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
Journal Article
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
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
Journal Article