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Updated: Mar 22, 2026

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

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A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

Enea Cippitelli¹, Samuele Gasparrini¹, Ennio Gambi¹

¹Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy.

Computational Intelligence and Neuroscience

|April 13, 2016

Summary

This study introduces a new human activity recognition system using skeleton data from RGBD sensors for elderly monitoring in Active and Assisted Living (AAL). The method effectively recognizes AAL-related actions, promoting independent living for seniors.

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Area of Science:

Computer Science
Artificial Intelligence
Biomedical Engineering

Background:

Active and Assisted Living (AAL) tools aim to support elderly individuals living independently.
Human activity recognition (HAR) is crucial for monitoring older adults in home environments.
Cost-effective RGBD sensors offer rich environmental data for HAR applications.

Purpose of the Study:

To propose an HAR algorithm utilizing skeleton data extracted from RGBD sensors.
To develop a system promoting the 'ageing in place' concept for elderly people.
To enhance monitoring capabilities within AAL scenarios.

Main Methods:

Extraction of key poses from RGBD sensor skeleton data to form feature vectors.
Classification using a multiclass Support Vector Machine (SVM).
Key pose computation and association via a clustering algorithm, independent of a learning algorithm.

Main Results:

The proposed approach demonstrated promising results on five public activity recognition datasets.
High efficacy was observed particularly in recognizing actions relevant to AAL.
The system achieved effective human activity recognition without requiring a prior learning phase.

Conclusions:

The developed HAR algorithm shows significant potential for AAL applications.
The method offers a viable solution for monitoring elderly individuals to support independent living.
Further improvements are necessary to fully integrate the solution into real-world AAL scenarios.