Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer

Noor, Mohd Halim Mohd; Salčić, Zoran; Wang, Kevin I‐Kai

doi:10.1016/j.pmcj.2016.09.009

Cited by 111 publications

(48 citation statements)

References 49 publications

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“…The activities of interest consist of three static activities (i.e., sitting, standing, lying) and three dynamic activities (i.e., walking, going upstairs, going downstairs). The sampling rate is 50 Hz and a sliding window of 2.56 s with 50% overlap between two adjacent segments is used to divide the streaming sensor data into segments [36]. That is, each window contains 128 sensor readings.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Towards Human Activity Recognition: A Hierarchical Feature Selection Framework

Wang

Chen

et al. 2018

Sensors

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The inherent complexity of human physical activities makes it difficult to accurately recognize activities with wearable sensors. To this end, this paper proposes a hierarchical activity recognition framework and two different feature selection methods to improve the recognition performance. Specifically, according to the characteristics of human activities, predefined activities of interest are organized into a hierarchical tree structure, where each internal node represents different groups of activities and each leaf node represents a specific activity label. Then, the proposed feature selection methods are appropriately integrated to optimize the feature space of each node. Finally, we train corresponding classifiers to distinguish different activity groups and to classify a new unseen sample into one of the leaf-nodes in a top-down fashion to predict its activity label. To evaluate the performance of the proposed framework and feature selection methods, we conduct extensive comparative experiments on publicly available datasets and analyze the model complexity. Experimental results show that the proposed method reduces the dimensionality of original feature space and contributes to enhancement of the overall recognition accuracy. In addition, for feature selection, returning multiple activity-specific feature subsets generally outperforms the case of returning a common subset of features for all activities.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

Towards Human Activity Recognition: A Hierarchical Feature Selection Framework

Wang

Chen

et al. 2018

Sensors

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“…Working this way, turns out to be simpler than to perform data annotation by, for instance, relying on a fixed size sliding windows method. In our case, activities, such as "running", do not have fixed time duration, which makes the applicability of fixed sliding windows methods not well suitable [13].…”

Section: Activity Analysismentioning

confidence: 97%

Activity recognition in a physical interactive robogame

Oliveira

Orrù

Nascimento

et al. 2017

2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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Abstract-In this paper, we investigate the possibility of human physical activity recognition in a robot game scenario. Being able to recognize types of activity is essential to enable robot behavior adaptation to support player engagement. Also, the introduction of this recognition system will allow for development of better models for prediction, planning and problem solving in PIRGs that can foster human-robot interaction. The experiments reported on this paper were performed on data collected from real in-game activity, where a human player faces a mobile robot. We use a custom single tri-axial accelerometer module attached to the player's chest in order to capture motion information. The main characteristic of our approach is the extraction of features from patterns found on the motion variance rather than on raw data. Furthermore, we allow for the recognition of unconstrained motion given that we do not ask the players to perform target activities before hand: all detectable activities are derived from the free player motion during the game itself. To the best of our knowledge, this is the first paper to consider activity recognition in a physical interactive robogame.

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“…Given the sparse distribution in time of sporadic actions or gestures, adaptive segmentation techniques have been shown to offer better performance (Noor et al, 2017). Within adaptive segmentation techniques, Piecewise Linear Representations (PLRs) are well-known techniques (Keogh et al, 2004;Lovrić et al, 2014).…”

Section: Time Series Segmentationmentioning

confidence: 99%

“…Besides PLRs, various customised segmentation approaches have been proposed for spotting sporadic gestures or actions from continuous inertial data streams. Noor et al (2017) used an extendable Gaussian Probability Function-based window. Parate et al (2014) employed a segmentation approach based on a re-adjustable resting position and a distance peak detector from the most current resting position.…”

Section: Time Series Segmentationmentioning

confidence: 99%

“…An adaptive segmentation technique to spot a set of four transitional activities (sit-to-stand, stand-to-sit, sit-to-lie and lie-to-sit) was developed by Noor et al (2017) using data from a waist-worn accelerometer. First, a set of thirteen features was used on windows of fixed length to determine whether the different windows contained a transitional, a dynamic or a static activity.…”

Section: Gesture Spotting and Recognitionmentioning

confidence: 99%

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Eating and drinking gesture spotting and recognition using a novel adaptive segmentation technique and a gesture discrepancy measure

Anderez

Lotfi

Pourabdollah

2020

Expert Systems with Applications

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Despite the increasing developments on human activity recognition using wearable technology, there are still many open challenges in spotting and recognising sporadic gestures. As opposed to activities, which exhibit continuous behaviour, the difficulty of spotting gestures lies in their rather sparse nature. This paper proposes a novel solution to spot and recognise a set of similar eating and drinking gestures from continuous inertial data streams. First, potential segments containing an eating or a drinking gesture are found using a Crossings-based Adaptive Segmentation Technique (CAST). Second, further to the long-established range of features employed in previous human activities recognition research work, a gesture discrepancy measure is proposed to improve the classification performance of the system. At the final step, a range of state-of-the-art classification models is employed for evaluation. Various conclusions can be drawn from the results obtained. First, given the 100% recall achieved at the segmentation step, the CAST can be considered a reliable segmentation technique for spotting drinking and eating gestures which may be employed in future gesture spotting work. Second, the addition of gesture discrepancy as a feature descriptor consistently improves the classification performance of the system. Third, the reliability of the food and drink intake monitoring approach proposed in this work finds support on the out-performance of previous similar

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Adaptive sliding window segmentation for physical activity recognition using a single tri-axial accelerometer

Cited by 111 publications

References 49 publications

Towards Human Activity Recognition: A Hierarchical Feature Selection Framework

Towards Human Activity Recognition: A Hierarchical Feature Selection Framework

Activity recognition in a physical interactive robogame

Eating and drinking gesture spotting and recognition using a novel adaptive segmentation technique and a gesture discrepancy measure

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