Combining discriminative spatiotemporal features for daily life activity recognition using wearable motion sensing suit

Vital, Jessica P. M.; Faria, Diego R.; Dias, Gonçalo; Couceiro, Micael S.; Coutinho, Fernanda; Ferreira, Nuno M. F.

doi:10.1007/s10044-016-0558-7

Cited by 22 publications

(19 citation statements)

References 26 publications

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“…This approach is similar to how acceleration data are handled. The authors of [81] used the quaternions and Euler angles representations, along with their velocities, and accelerations of human joints as input data. This also can be seen as a geometrical and parametric representation of the joint poses.…”

Section: Data Representationmentioning

confidence: 99%

“…Mean and variance of the Heel Strike Force, which is computed using dynamics [65] Average Velocity Integral of the acceleration [50] Feature Reduction. The authors of [44][45][46]68,81] deployed Principal Component Analysis (PCA) for reducing the dimensionality of their features. PCA is a holistic method that considers its inputs as points in a high-dimensional space and it finds a lower-dimensional feature space along the highest variance, where classification becomes easier.…”

Section: Gravity Variationmentioning

confidence: 99%

“…The authors of [44] used a NB using the Probability Density Function (PDF) from all of the 19 features, which were previously reduced using PCA; assuming that all the features in the lower space are not mutually correlated. The authors of [81,90] used also a NB assuming that the features are mutually independent. Similarly, refs.…”

Section: Gravity Variationmentioning

confidence: 99%

“…[29,45,46,57,63,64,68,80,90] trained a K-Nearest Neighbor (KNN) based classifier. The authors of [81] used Dynamic Bayesian Mixture Model (DBMM) for combining conditional probability outputs from different classifiers, namely, the NB, SVM and MLP. A weight is assigned to each base classifier, according to a learning process, using an uncertainty measure as a confidence level.…”

Section: Gravity Variationmentioning

confidence: 99%

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Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

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This contribution provides a systematic literature review of Human Activity Recognition for Production and Logistics. An initial list of 1243 publications that complies with predefined Inclusion Criteria was surveyed by three reviewers. Fifty-two publications that comply with the Content Criteria were analysed regarding the observed activities, sensor attachment, utilised datasets, sensor technology and the applied methods of HAR. This review is focused on applications that use marker-based Motion Capturing or Inertial Measurement Units. The analysed methods can be deployed in industrial application of Production and Logistics or transferred from related domains into this field. The findings provide an overview of the specifications of state-of-the-art HAR approaches, statistical pattern recognition and deep architectures and they outline a future road map for further research from a practitioner's perspective.

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Section: Data Representationmentioning

confidence: 99%

Section: Gravity Variationmentioning

confidence: 99%

Section: Gravity Variationmentioning

confidence: 99%

Section: Gravity Variationmentioning

confidence: 99%

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Human Activity Recognition for Production and Logistics—A Systematic Literature Review

et al. 2019

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“…This section brings a brief review of the Dynamic Bayesian Mixture Model (DBMM), which was first proposed by Faria et al [7] for individual activity recognition, also employed in other classification contexts [8], [18], [19], [20], [21]. This background section aims to facilitate the understanding of the next section that will introduce our re-design of the classification model as an extension in order to allow the fusion of multiple set of features with different semantics as multiple mixture and incorporating the learned prior from proximity features.…”

Section: Preliminaries: Classification Backgroundmentioning

confidence: 99%

Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Coppola

Faria

Nunes

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Social activity based on body motion is a key feature for non-verbal and physical behavior defined as function for communicative signal and social interaction between individuals. Social activity recognition is important to study human-human communication and also human-robot interaction. Based on that, this research has threefold goals: (1) recognition of social behavior (e.g. human-human interaction) using a probabilistic approach that merges spatio-temporal features from individual bodies and social features from the relationship between two individuals; (2) learn priors based on physical proximity between individuals during an interaction using proxemics theory to feed a probabilistic ensemble of activity classifiers; and (3) provide a public dataset with RGB-D data of social daily activities including risk situations useful to test approaches for assisted living, since this type of dataset is still missing. Results show that using the proposed approach designed to merge features with different semantics and proximity priors improves the classification performance in terms of precision, recall and accuracy when compared with other approaches that employ alternative strategies.

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Representative‐discriminative dictionary learning algorithm for human action recognition using smartphone sensors

Rajamoney¹,

Ramachandran²

2022

Concurrency and Computation

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SUMMARY With the advancement of mobile computing, understanding, and interpretation of human activities has become increasingly popular as an innovative human computer interaction application over the past few decades. This article presents a new scheme for action recognition based on sparse representation theory using a novel dictionary learning algorithm. This system employs two types of inertial signals from smartphones namely, accelerometer and gyroscope sensory data. Attainment of higher values of classification accuracy depends on the creation of effective dictionaries that completely retain the important features of every action while maintaining the least correlation with the features of other actions. Accordingly, in this research, we propose a new algorithm for learning dictionaries with two levels of dictionary training that aims at learning a compact, representative, and discriminative dictionary for each class. Unlike typical dictionary learning algorithms that aim at the creation of dictionaries that best represents the features of each class, our proposed algorithm incorporates a discriminative criterion that eventually produces better classification results. To validate the proposed framework, all the experiments were performed using three publicly available datasets.

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Combining discriminative spatiotemporal features for daily life activity recognition using wearable motion sensing suit

Cited by 22 publications

References 26 publications

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

Social activity recognition based on probabilistic merging of skeleton features with proximity priors from RGB-D data

Representative‐discriminative dictionary learning algorithm for human action recognition using smartphone sensors

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