Human activity analysis for in-home fall risk assessment

Liciotti, Daniele; Massi, Gionata; Frontoni, Emanuele; Mancini, Adriano; Zingaretti, Primo

doi:10.1109/iccw.2015.7247192

Cited by 13 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The movements of the users are on line recorded using a database, so that their physical activity can be supervised anytime. Furthermore, the RGB-D camera is able to extract the depth images even in dark room, respecting privacy and intimacy with also re-id capabilities [ 42 , 43 , 44 , 45 ]. Observing Figure 15 , the physical architecture of the system consists of a RGB-D sensor installed in a top view configuration.…”

Section: Smart Objectsmentioning

confidence: 99%

HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly

Frontoni

Pollini

Russo

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

The aim of this paper is to present the main results of HDOMO, an Ambient Assisted Living (AAL) project that involved 16 Small and Medium Enterprises (SMEs) and 2 research institutes. The objective of the project was to create an autonomous and automated domestic environment, primarily for elderly people and people with physical and motor disabilities. A known and familiar environment should help users in their daily activities and it should act as a virtual caregiver by calling, if necessary, relief efforts. Substantially, the aim of the project is to simplify the life of people in need of support, while keeping them autonomous in their private environment. From a technical point of view, the project provides the use of different Smart Objects (SOs), able to communicate among each other, in a cloud base infrastructure, and with the assisted users and their caregivers, in a perspective of interoperability and standardization of devices, usability and effectiveness of alarm systems. In the state of the art there are projects that achieve only a few of the elements listed. The HDOMO project aims to achieve all of them in one single project effectively. The experimental trials performed in a real scenario demonstrated the accuracy and efficiency of the system in extracting and processing data in real time to promptly acting, and in providing timely response to the needs of the user by integrating and confirming main alarms with different interoperable smart sensors. The article proposes a new technique to improve the accuracy of the system in detecting alarms using a multi-SO approach with information fusion between different devices, proving that this architecture can provide robust and reliable results on real environments.

show abstract

Section: Smart Objectsmentioning

confidence: 99%

HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly

Frontoni

Pollini

Russo

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main differences with our work lay in: An RGB-D camera in a top view configuration motivated by the enhancement of the applicability of the proposed approach in crowded public environments is employed. The top-view configuration reduces the problem of occlusions and has the advantage of being privacy preserving because a person’s face is not recorded by the camera [ 55 ]. However, this challenging configuration does not allow one to retrieve features related to the front view, which can be highly discriminative for the subject identification.…”

Section: Introductionmentioning

confidence: 99%

“…Third, another possible application of this specific top-view configuration is fall detection and HBA in smart homes, from high-reliability fall detection to occlusion-free HBA at home for elders in AAL environments [ 55 , 63 ].…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

Paolanti

Romeo

Liciotti

et al. 2018

Sensors

Self Cite

View full text Add to dashboard Cite

Person re-identification is an important topic in retail, scene monitoring, human-computer interaction, people counting, ambient assisted living and many other application fields. A dataset for person re-identification TVPR (Top View Person Re-Identification) based on a number of significant features derived from both depth and color images has been previously built. This dataset uses an RGB-D camera in a top-view configuration to extract anthropometric features for the recognition of people in view of the camera, reducing the problem of occlusions while being privacy preserving. In this paper, we introduce a machine learning method for person re-identification using the TVPR dataset. In particular, we propose the combination of multiple k-nearest neighbor classifiers based on different distance functions and feature subsets derived from depth and color images. Moreover, the neighborhood component feature selection is used to learn the depth features’ weighting vector by minimizing the leave-one-out regularized training error. The classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Experimental results show that the proposed methodology outperforms standard supervised classifiers widely used for the re-identification task. This improvement encourages the application of this approach in the retail context in order to improve retail analytics, customer service and shopping space management.

show abstract

“…The algorithm has been evaluated on a dataset recorded from 4 actors performing 20 tests, 10 of which involve the presence of several subjects in the area. The same sensor configuration has been used by Liciotti et al [78]. A background subtraction algorithm based on Gaussian Mixture Model returns a foreground image that contains the people and, possibly, moving objects.…”

Section: A Fall Detection Using Depth Datamentioning

confidence: 99%

Radar and RGB-Depth Sensors for Fall Detection: A Review

et al. 2017

View full text Add to dashboard Cite

Abstract-This paper reviews recent works in the literature on the use of systems based on Radar and RGB-Depth sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users' acceptance and compliance, compared with other sensor technologies such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing.

show abstract

Human activity analysis for in-home fall risk assessment

Cited by 13 publications

References 13 publications

HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly

HDOMO: Smart Sensor Integration for an Active and Independent Longevity of the Elderly

Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

Radar and RGB-Depth Sensors for Fall Detection: A Review

Contact Info

Product

Resources

About