Two-person Activity Recognition using Skeleton Data

International Journal of Simulation--Systems, Science & Technology, 2013

Human activity recognition is an important ability in an y system that supports human in performing their daily activities. However, current supervised approach in human activity recognition is difficult to be deployed in the natural human living environment where labeled observations are scarce. In this paper, we demonstrate the use of K-means clustering and simple template models to achieve human activity detection and recognition in an unsupervised manner. The features used are extracted from the skeleton data obtained from an inexpensive RGBD (RGB-Depth) sensor. Our result s show an average detection performance of 80.4% precision and 83.8% recall. The availability of an unsupervised approach in human activity recognition will make possible the wide adoption of human activity recognition in the natural human living environment.

8TH BRUNEI INTERNATIONAL CONFERENCE ON ENGINEERING AND TECHNOLOGY 2021

Recognition of human activities is one of the most significant fields in the area of machine intelligence research, with its main objective of distinguishing various human activities. Accurate human posture extraction from images is a crucial step towards this objective. In this paper, laboratory dataset consisting of 2D and 3D images of subjects performing seventeen different activities have been collected, and then used to construct 2D and 3D human postures. Eight different classification models are subsequently used to classify the different activities. Subsequently, it has been shown that Random Forest classification model gives the best performance, in terms of accuracy. The findings also demonstrate that both 2D and 3D postures are capable of achieving significant accuracy score, with very high average accuracies of 99.18% and 99.82%, respectively, using the Random Forest classification model.

IEEE Transactions on Human-Machine Systems, 2014

In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, Support Vector Machines, Hidden Markov Models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatio-temporal evolution of known postures. Experiments were performed on KARD, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution outperforms four relevant works based on RGB-D image fusion, hierarchical Maximum Entropy Markov Model, Markov Random Fields and Eigenjoints respectively. The performance we achieved, i.e., precision/recall of 77.3% and 76.7%, and the ability to recognize the activities in real-time, show promise for applied use.

Sensors

The Assisted Living Environments Research Area–AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems—ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, s...

Soft Computing

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers-support vector machines (SVMs), K-nearest neighbour and random forest-is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset-the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities.

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

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.

Pattern Recognition Letters, 2020

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International Journal of Ad Hoc and Ubiquitous Computing, 2019

The evaluation of a patient's functional ability to perform daily living activities is an essential part of nursing and a powerful predictor of a patient's morbidity, especially for the elderly. In this article, we describe the use of a machine learning approach to address the task of recognising activity in a smart home. We evaluate our approach by comparing it to a Markov statistical approach and using several performance measures over three datasets. We show how our model achieves significantly better recognition performance on certain data sets and with different representations and discretisation methods with an accuracy measurement that exceeds 92% and accuracy of 68%. The experiments also show a significant improvement in the learning time which does not exceed one second in the totality of the experiments reducing the complexity of the approach.

Capabilities of domestic service robots could be further improved, if the robot is equipped with an ability to recognize activities performed by humans in its sensory range. For example in a simple scenario a floor cleaning robot can vacuum the kitchen floor after recognizing human activity “cooking in the kitchen”. Most of the complex human activities can be sub divided into simple activities which can later used for recognize complex activities. Activities like “take meditation” can be sub divided into simple activities like “opening pill container” and “drinking water”. However, even recognizing simple activities are highly challenging due to the similarities between some inter activities and dissimilarities of intra activities which are performed by different people, body poses and orientations. Even a sim- ple human activity like “drinking water” can be performed while the subject is in different body poses like sitting, standing or walking. Therefore building machine learning techniques to recognize human activities with such complexities is non trivial. To address this issue, we propose a human activity recognition technique that uses 3D skeleton features produced by a depth camera. The algorithm incor- porates importance weights for skeleton 3D joints according to the activity being performed. This allows the algorithm to ignore the confusing or irrelevant features while relying on informative features. Later these joints were ensembled together to train Dynamic Bayesian Networks (DBN), which is then used to infer human activi- ties based on likelihoods. The proposed activity recognition technique is tested on a publicly available dataset and UTS experiments with overall accuracies of 85% and 90%

Sensors, 2021

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt Univer...