A tutorial on human activity recognition using body-worn inertial sensors

By Andreas Bulling, Ulf Blanke, Bernt Schiele. 2014

1,647 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Deep Learning for Sensor-based Human Activity Recognition (2020, background): “vious works adopted machine learning methods in human activity recognition [79]. They highly rely on feature extraction techniques including time-frequency transformation [62], statistical approaches [21] and symbolic representation [88]. However, the features extracted are carefully engineered and ”
Transition-Aware Human Activity Recognition Using Smartphones (2016, background): “A second issue considers that the activities carried out by people are, in real-life situations, more than the ones learned by any HAR system [31].”
Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition (2015, methodology): “• Means and variance (MV) [Bulling et al., 2014] Same as the proposed CNN method, the sliding window strategy is used to generate a set of r × D matrix-level instances first.”
A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition (2020, background): “The first is insufficient labeled observations [2].”
Privacy and Utility Preserving Sensor-Data Transformations (2019, methodology): “To evaluate the RAE, we use four benchmark datasets of gesture and activity recognition including at least 10 diﬀerent labels: Opportunity [39], Skoda [40], Hand-Gesture [41], and Utwente [42].”
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition (2016, background): “Sliding window approaches are commonly used for static and periodic activities, while sporadic activities lend themselves to template matching approaches or hidden Markov modelling [8,11].”
Learning Human Identity From Motion Patterns (2015, background): “In [12] and [7], exhaustive overviews of preprocessing techniques and manual feature extraction from accelerometer data for activity recognition are given.”
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables (2017, methodology): “The majority of related work in HAR utilises (variants of) the well established sliding-window, frame-based analysis approach [Bulling et al. 2014].”
Deep Learning for Sensor-based Activity Recognition: A Survey (2017, background): “HAR aims to understand human behaviors which enable the computing systems to proactively assist users based on their requirement (Bulling et al., 2014).”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “A typical ARC for HAR is composed of five stages: (i) data collection, (ii) pre-processing, (iii) segmentation, (iv) feature extraction and (v) classification.”
Using Inertial Sensors for Position and Orientation Estimation (2017, neutral): “A separate but related field of research is that of activity recognition using inertial sensors, where the aim is to determine the type of motion that is performed by a person wearing or carrying a device containing inertial sensors, see [19, 4] for an overview.”
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes (2021, background): “Not only Area Under the Curve (AUC) indicates the overall efficiency of classifiers, but it also describes the likelihood that positive cases selected will be ranked higher than negative cases [34].”
Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0 (2018, methodology): “There are two evaluation schemes for the activity recognition model, which are a person-dependent method and a person-independent, leave-one-out method [17].”
A survey of depth and inertial sensor fusion for human action recognition (2015, neutral): “A tutorial was presented in [8] covering a comprehensive discussion of designing and evaluating activity recognition systems using body-worn inertial sensors.”