Human Activity Recognition through Weighted Finite Automata

Salomón, Sergio; Tîrnăucă, Cristina

doi:10.3390/proceedings2191263

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…, j}, U * ) may be understood as a Gibbs random field (GRF), whose probability distribution is based on exponential functions Eq. (27). Being H j (u j i , {a T l ,u l l = 1, .…”

Section: − →mentioning

confidence: 99%

Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Bordel

2021

ICA

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Activity recognition technologies only present a good performance in controlled conditions, where a limited number of actions are allowed. On the contrary, industrial applications are scenarios with real and uncontrolled conditions where thousands of different activities (such as transporting or manufacturing craft products), with an incredible variability, may be developed. In this context, new and enhanced human activity recognition technologies are needed. Therefore, in this paper, a new activity recognition technology, focused on Industry 4.0 scenarios, is proposed. The proposed mechanism consists of different steps, including a first analysis phase where physical signals are processed using moving averages, filters and signal processing techniques, and an atomic recognition step where Dynamic Time Warping technologies and k-nearest neighbors solutions are integrated; a second phase where activities are modeled using generalized Markov models and context labels are recognized using a multi-layer perceptron; and a third step where activities are recognized using the previously created Markov models and context information, formatted as labels. The proposed solution achieves the best recognition rate of 87% which demonstrates the efficacy of the described method. Compared to the state-of-the-art solutions, an improvement up to 10% is reported.

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“…, j}, U * ) may be understood as a Gibbs random field (GRF), whose probability distribution is based on exponential functions Eq. (27). Being H j (u j i , {a T l ,u l l = 1, .…”

Section: − →mentioning

confidence: 99%

Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Bordel

2021

ICA

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“…In the work [13] This dataset integrated multiple activities of a single inhabitant in a smart lab at the University of Jaén (Spain) with a heterogeneous set of devices with sensors. The most relevant proposals were based on the techniques: bagging classifier [14], finite state machine [15], filtered classifier [16], finite automata and regular expressions [17], naive Bayes classifier [18] and hidden Markov model + definition-based model [19].…”

Section: Related Researchmentioning

confidence: 99%

“…In [17] was presented a method based on the user's behavioral models and activity sensor models in order to build weighted finite automata with regular expressions. So, the location of the inhabitant was obtained for each activity by means of the floor sensor data.…”

Section: Related Researchmentioning

confidence: 99%

UJA Human Activity Recognition multi-occupancy dataset

De-la-Hoz-Franco¹,

Monroy²,

Ariza-Colpas³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Activity Recognition Systems-ARS are proposed to improve the quality of human life. An ARS uses predictive models to identify the activities that individuals are performing in different environments. Under data-driven approaches, these models are trained and tested in experimental environments from datasets that contain data collected from heterogeneous information sources. When several people interact (multi-occupation) in the environment from which data are collected, identifying the activities performed by each individual in a time window is not a trivial task. In addition, there is a lack of datasets generated from different data sources, which allow systems to be evaluated both from an individual and collective perspective. This paper presents the SaMO-UJA dataset, which contains Single and Multi-Occupancy activities collected in the UJAmI (University of Jaén Ambient Intelligence, Spain) Smart Lab. The main contribution of this work is the presentation of a dataset that includes a new generation of sensors as a source of information (acceleration of the inhabitant, intelligent floor for location, proximity and binary-sensors) to provide an excellent tool for addressing multioccupancy in smart environments.

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“…One goal of activity recognition research is to identify physical activities [7][8][9][10][11]. They can be used for human trajectory tracking, recommended system, regions-of-interest detection [12,13], and so on.…”

Section: Related Workmentioning

confidence: 99%

Identifying Human Daily Activity Types with Time-Aware Interactions

Chen

Yao

et al. 2020

Applied Sciences

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Human activities embedded in crowdsourced data, such as social media trajectory, represent individual daily styles and patterns, which are valuable in many applications. However, the accurate identification of human activity types (HATs) from social media is challenging, possibly because interactions between posts and users at different time are overlooked. To fill this gap, we propose a novel model that introduces the interactions hidden in social media and synthesizes Graph Convolutional Network (GCN) for identifying HAT. The model first characterizes interactions among words, posts, dates, and users, and then derives a Time Gated Human Activity Graph Convolutional Network (TG-HAGCN) to predict the HATs of social media trajectory. To examine the proposed model performance, we built a new dataset including interactions between post content, post time, and users from the open Yelp dataset. Experimental results show that exploiting interactions hidden in social media to recognize HATs achieves state-of-the-art performance with high accuracy. The study indicates that interactions among social media promotes ability of machine learning on social media data mining and intelligent applications, and offers a reference solution for how to fuse multi-type heterogeneous data in social media.

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Human Activity Recognition through Weighted Finite Automata

Cited by 8 publications

References 14 publications

Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

UJA Human Activity Recognition multi-occupancy dataset

Identifying Human Daily Activity Types with Time-Aware Interactions

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