Enea Cippitelli

A Depth-Based Fall Detection System Using a Kinect® Sensor

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

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.

Kinect as a Tool for Gait Analysis: Validation of a Real-Time Joint Extraction Algorithm Working in Side View

The Microsoft Kinect sensor has gained attention as a tool for gait analysis for several years. Despite the many advantages the sensor provides, however, the lack of a native capability to extract joints from the side view of a human body still limits the adoption of the device to a number of relevant applications. This paper presents an algorithm to locate and estimate the trajectories of up to six joints extracted from the side depth view of a human body captured by the Kinect device. The algorithm is then applied to extract data that can be exploited to provide an objective score for the “Get Up and Go Test”, which is typically adopted for gait analysis in rehabilitation fields. Starting from the depth-data stream provided by the Microsoft Kinect sensor, the proposed algorithm relies on anthropometric models only, to locate and identify the positions of the joints. Differently from machine learning approaches, this solution avoids complex computations, which usually require significant resources. The reliability of the information about the joint position output by the algorithm is evaluated by comparison to a marker-based system. Tests show that the trajectories extracted by the proposed algorithm adhere to the reference curves better than the ones obtained from the skeleton generated by the native applications provided within the Microsoft Kinect (Microsoft Corporation, Redmond, WA, USA, 2013) and OpenNI (OpenNI organization, Tel Aviv, Israel, 2013) Software Development Kits.

Proposal and Experimental Evaluation of Fall Detection Solution Based on Wearable and Depth Data Fusion

Fall injury issues represent a serious problem for elderly in our society. These people want to live in their home as long as possible and technology can improve their security and independence. In this work we study the joint use of a camera based system and wearable devices, in the so called data fusion approach, to design a fall detection solution. The synchronization issues between the heterogeneous data provided by the devices are properly treated, and three different fall detection algorithms are implemented. Experimental results are also provided, to compare the proposed solutions.

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

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) 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 The 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.

Multimodal Interaction in a Elderly-Friendly Smart Home: A Case Study

This paper discusses different and multimodal user-system interfaces proposed in the framework of a smart home designed to support the independent living of elderly and frail users. It is shown how different technologies and solutions may be complemented and integrated to provide effective interaction both for routine activities of daily living and anomalous situations.

Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices

Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects’ normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject’s lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.

Comparison of RGB-D Mapping Solutions for Application to Food Intake Monitoring

Food intake behaviours are strictly correlated to health, especially for elderly people. Dietary habits monitoring is one of the most challenging activity for researchers in AAL scenario. RGB-D sensors, such as Kinect, provide multiple useful data to perform behavioural analysis in an unobtrusive way. Unfortunately, when using the Kinect sensor, depth and RGB data are not available with the same point of view, and a mapping algorithm is required in order to associate a 3D point to the same pixel in both the RGB and depth frames. In this paper, some techniques for RGB-D mapping of Kinect sensor data are compared, and a proposed implementation is described. Some experimental results in specific conditions are finally provided.

Low complexity head tracking on portable android devices for real time message composition

For the people who are totally or partially unable to move or control their limbs and cannot rely on verbal communication, it is very important to obtain an interface capable of interpreting their limited voluntary movements, in order to allow communications with friends, relatives and care providers, or to send commands to a system. This paper presents a real time software application for disabled subjects, suffering from both motor and speech impairments, that provides message composition and speech synthesis functionalities based on face detection and head tracking. The proposed application runs on portable devices equipped with Android Operating System, and relies upon the O.S.’s native computer vision primitives, without resorting to any external software library. This way, the available camera sensors are exploited, and the device computational requirements accomplished. Experimental results show the effectiveness of the application in recognizing the user’s movements, and the reliability of the message composition and speech synthesis functionalities.

Quality of Kinect Depth Information for Passive Posture Monitoring

The availability of a low cost device incorporating RGB and depth sensors, such as the Microsoft Kinect device, has enabled a plethora of applications and solutions aiming at automatic monitoring of gait and posture, in order to assess the subject’s behavior and possibly prevent future health problems. This paper discusses the quality of the information provided by the depth sensor the Kinect device is equipped with, in order to assess the precision of such information, in different operational contexts, and properly identify the performance that may be reasonably expected from the adoption of such device, in the field of passive monitoring of gait and posture. Some techniques borrowed from the image processing field are also suggested, in order to improve the depth information in specific conditions, by means of a low complexity post-processing of the raw depth data.