Multiwindow Fusion for Wearable Activity Recognition

@inproceedings{Baos2015MultiwindowFF,
  title={Multiwindow Fusion for Wearable Activity Recognition},
  author={Oresti Ba{\~n}os and Juan Manuel G{\'a}lvez and Miguel Damas and A. Guill{\'e}n and Luis Javier Herrera and H{\'e}ctor Pomares and Ignacio Rojas and Claudia Villalonga and Choong Seon Hong and Sungyoung Lee},
  booktitle={IWANN},
  year={2015}
}
The recognition of human activity has been extensively investigated in the last decades. Typically, wearable sensors are used to register body motion signals that are analyzed by following a set of signal processing and machine learning steps to recognize the activity performed by the user. One of the most important steps refers to the signal segmentation, which is mainly performed through windowing approaches. In fact, it has been proved that the choice of window size directly conditions the… Expand
Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows
TLDR
The results show that the recognition performance can be increased up to 15% when using the fusion of equally-sized subwindows compared to using a classical single window. Expand
Human Activity Recognition Based on Wearable Sensor Using Hierarchical Deep LSTM Networks
TLDR
A novel structure named hierarchical deep LSTM (H-LSTM) based on long short-term memory is proposed based on preprocessed sensor data and then, the feature will be selected and extracted by time–frequency-domain method. Expand
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges
TLDR
The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition, and categorise the studies into generative, discriminative and hybrid methods. Expand
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
TLDR
The focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. Expand
Segmentation Study of Aged Gait Based on FFT
TLDR
The human action sequence segmentation method has been proposed based on the motion characteristics and can effectively segment the daily actions of the aged, extract valid action sequence, and is particularly effective in the segmentation of regular actions. Expand
Markov Dynamic Subsequence Ensemble for Energy-Efficient Activity Recognition
TLDR
This work formalizes a dynamic subsequence selection problem that minimizes the computational cost while persevering a high recognition accuracy, and derives an upper bound of the expected ensemble size, so that the energy consumption caused by the computations of the proposed method is guaranteed. Expand
Automated ergonomic risk monitoring using body-mounted sensors and machine learning
TLDR
A methodology is introduced to unobtrusively evaluate the ergonomic risk levels caused by overexertion by collecting time-stamped motion data from body-mounted smartphones, automatically detecting workers’ activities through a classification framework, and estimating activity duration and frequency information. Expand
Adaptive data processing for real-time nutrition monitoring
TLDR
This paper proposes a novel scheme for segmentation of sparse sensor data using an adaptive window size approach, and shows a reduction in processing overhead of 68% compared to the baseline with fixed window sizes. Expand
Adaptive data processing for real-time nutrition monitoring
TLDR
This paper proposes a novel scheme for segmentation of sparse sensor data using an adaptive window size approach, and shows a reduction in processing overhead of 68% compared to the baseline with fixed window sizes. Expand
DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería
TLDR
This work presents the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System), where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Expand
...
1
2
...

References

SHOWING 1-10 OF 30 REFERENCES
Human activity recognition based on a sensor weighting hierarchical classifier
TLDR
This paper presents a fusion classification methodology which takes into account the potential of the individual decisions yielded at both activity and sensor classification levels, and systematically outperforms the results obtained by traditional multiclass models which otherwise may require a high-dimensional feature space to acquire a similar performance. Expand
Window Size Impact in Human Activity Recognition
TLDR
An extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design is presented. Expand
Designing a Robust Activity Recognition Framework for Health and Exergaming Using Wearable Sensors
TLDR
A new robust stochastic approximation framework for enhanced classification of intensity-independent activity recognition of data where the class labels exhibit large variability, the data are of high dimensionality, and clustering algorithms are necessary is proposed. Expand
A tutorial on human activity recognition using body-worn inertial sensors
TLDR
This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition using on-body inertial sensors and describes the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. Expand
Feature Selection and Activity Recognition from Wearable Sensors
TLDR
In this experiment, data was from 17 daily life examples from male and female subjects, and Interestingly, acceleration mean values from the necklace were selected as important features. Expand
Daily living activity recognition based on statistical feature quality group selection
TLDR
Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one-feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist. Expand
Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition
TLDR
While classical recognition models show little tolerance to sensor displacement, the proposed method is proven to have notable capabilities to assimilate the changes introduced in the sensor position due to self-placement and provides considerable improvements for large misplacements. Expand
Activity recognition using a single accelerometer placed at the wrist or ankle.
TLDR
A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set, and could be implemented in real time on mobile devices with only 4-s latency. Expand
Activity classification in persons with stroke based on frequency features.
TLDR
Nine k-nearest neighbor cross-validated classifiers were developed using frequency-features derived from shank-mounted IMUs on the less-affected and affected limbs of subjects with stroke to develop classification models capable of identifying activities performed within large datasets. Expand
Multi-sensor Fusion Based on Asymmetric Decision Weighting for Robust Activity Recognition
TLDR
A novel model devised to cope with the effects introduced by sensor technological anomalies is presented, which builds on the knowledge gained from multi-sensor configurations, through asymmetrically weighting the decisions provided at both activity and sensor levels. Expand
...
1
2
3
...