A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

By Enea Cippitelli, Samuele Gasparrini, Ennio Gambi, Susanna Spinsante. 2016

215 citations recorded by the source corpus.

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

A self-organizing neural network architecture for learning human-object interactions (2017, background): “The most typical approaches are those that do not explicitly model the interplay between object recognition and body pose estimation [27, 28, 29].”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “ Saxena [154] Temporal Relational Features Conc Lowlv Hand X Wu and Shao [155] EigenJoints Conc Lowlv Deep X X X X Kerola et al. [156] Spectral Graph Skeletons Conc Lowlv Hand X X X Cippitelli et al. [157] Key Poses BoW Lowlv Dict X X X X 8 3.1. Displacement-Based Representations Features extracted ”
Sensor-based and vision-based human activity recognition: A comprehensive survey (2020, methodology): “The proposed model achieved state-of-the-art results in two benchmark datasets, the KARD and CAD-60 [112] .”
Evaluation of a skeleton-based method for human activity recognition on a large-scale RGB-D dataset (2016, methodology): “Differently from [18], where the coordinate space was centered in the joint of torso, a center-of-mass Jcm is computed considering the average 3D position of the main skeleton, constituted by P joints:”