Giovanni Diraco

Automatic fall-detection systems, saving time for the arrival of medical assistance, have the potential to reduce the risk of adverse health consequences. Fall-detection technologies are under continuous improvements in terms of both acceptability and performance. Ultra-wideband radar sensing is an interesting technology able to provide rich information in a privacy-preserving way, and thus well acceptable by end-users. In this study, a radar sensor compound of two ultra-wideband monostatic units in two different configurations (i.e., vertical and horizontal baseline) has been investigated in order to provide sensor data from which robust features can be automatically extracted by using deep learning. The achieved results show the potential of the suggested sensor data representation and the superiority of the double-unit vertical-baseline configuration. Indeed, while the horizontal configuration allows to discriminate the body's position around the radar system, the vertical one discriminates the body's height that is more important for fall detection.

The paper proposes new technologies to support independence and engagement in elderly people living alone at home in the framework of a new UE Integrated Project. Aim of the study is the development of a light technological infrastructure to be integrated in the homes of old people at reduced costs, allowing the detection of critical health situations. In particular a reliable fall detector is presented, with focus on the protection and assistance to the elderly in the home environment. The integrated fall detector prototype includes two different sensors: a 3D Time-of-Flight range camera and a wearable MEMS accelerometer. The devices are connected in a networked configuration with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each sensor, an optimized algorithm for fall-detection has been developed and benchmarked on a collected multimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.

A non-invasive system for human posture recognition suitable to be used in several in-home scenarios is proposed and validation results presented. 3D point cloud sequences were acquired by using a time-of-flight sensor in a privacy preserving modality and near real-time processed with a low power embedded PC. To satisfy different application requirements in terms of discrimination capabilities, covered distance range and processing speed, a twofold discrimination approach was investigated in which features were hierarchical arranged from coarse to fine exploiting both topological and volumetric spatial representations. The topological representation encoded the intrinsic topology of the body’s shape in a skeleton-based structure, guarantying invariance to scale, rotations and postural changes, and achieving a high level of detail with a moderate computational cost. In the volumetric representation, on the other hand, postures were described in terms of 3D cylindrical histograms working within a wider range of distances in a faster way and also guarantying good invariance properties. The discrimination capabilities of the approach were evaluated in four different real-home scenarios especially related with ambient assisted living and homecare fields, namely dangerous event detection, anomalous behavior detection, activities recognition, natural human-ambient interaction, and also in terms of invariance to viewpoint changes, representation capabilities and classification performance, achieving promising results. The two approaches exhibited complementary characteristics showing high reliability with classification rates greater than 97% in four application scenarios for which the posture recognition is a fundamental function.

The aging population represents an emerging challenge for healthcare since elderly people frequently suffer from chronic diseases requiring continuous medical care and monitoring. Sensor networks are possible enabling technologies for ambient assisted living solutions helping elderly people to be independent and to feel more secure. This paper presents a multi-sensor system for the detection of people falls in home environment. Two kinds of sensors are used: a wearable wireless accelerometer with onboard fall detection algorithms and a time-of-flight camera. A coordinator node receives data from the two sub-sensory systems with their associated level of confidence and, on the basis of a data fusion logic, it operates the validation and correlation among the two sub-systems delivered data in order to rise overall system performance with respect to each single sensor sub-system. Achieved results show the effectiveness of the suggested multi-sensor approach for improving fall detection service in ambient assisted living contexts.

In this paper an algorithmic framework for posture analysis using a single view 3D TOF camera is presented. The 3D human posture parameters are recovered automatically from range data without the usage of body markers. A topological approach is investigated in order to define descriptors suitable to estimate location of body parts and orientation of body articulations. Two Morse function are exploited, the first one provides an Euclidean distance mapping helpful to deal with body self-occlusions. The second Morse function is based on geodesic distance and provides an extended Discrete Reeb Graph description of the main body parts that are head, torso, arms and legs. Geodesic distance function exhibits the property of invariance under isometric transformations that typically occur when the human body changes its posture. The geodesic map of the body is obtained with a two steps procedure. Firstly, a Delaunay meshing is carried out starting from the depth map provided by the 3D TOF camera; secondly, geodesic distances are computed applying Dijkstra algorithm to previously computed mesh. Moreover, a re-meshing method is proposed in order to deal with self-occlusion problem which occurs in the depth data when a human body is partially occluded by other body segments. Experimental results on both synthetic and real data validate the effectiveness of the proposed approach to classifying four main postures: standing, lying, sitting and bending.

The paper presents an active vision system for human posture recognition, which is an important function of any assisted living system, suitable to be employed in indoor environments. Both hardware and software architectures are defined in order to meet constraints typically imposed by AAL (Ambient Assisted Living) contexts such as compactness, low-power consumption, installation simplicity, privacy preserving and non-intrusiveness. Two different approaches for feature extraction (topological and volumetric) are discussed and the related discrimination capabilities evaluated by using a statistical learning methodology. Experimental results show the soundness of the presented active vision-based solution in order to classify four main human postures (standing, sitting, bent, lying) with an adequate detail level for the specific AAL application.

In recent years several world-wide ambient assisted living (AAL) programs have been activated in order to improve the quality of life of older people, and to strengthen the industrial base through the use of information and communication technologies. An important issue is extending the time that older people can live in their home environment, by increasing their autonomy and helping them to carry out activities of daily living (ADLs). Research in the automatic detection of falls has received a lot of attention, with the object of enhancing safety, emergency response and independence of the elderly, at the same time comparing the social and economic costs related to fall accidents. In this work, an algorithmic framework to detect falls by using a 3D time-of-flight vision technology is presented. The proposed system presented complementary working requirements with respect to traditional worn and non-worn fall-detection devices. The vision system used a state-of-the-art 3D range camera for elderly movement measurement and detection of critical events, such as falls. The depth images provided by the active sensor allowed reliable segmentation and tracking of elderly movements, by using well-established imaging methods. Moreover, the range camera provided 3D metric information in all illumination conditions (even night vision), allowing the overcoming of some typical limitations of passive vision (shadows, camouflage, occlusions, brightness fluctuations, perspective ambiguity). A self-calibration algorithm guarantees different setup mountings of the range camera by non-technical users. A large dataset of simulated fall events and ADLs in real dwellings was collected and the proposed fall-detection system demonstrated high performance in terms of sensitivity and specificity.Copyright © 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

This paper presents an inexpensive framework for 3-D seabed mosaic reconstruction, based on an asynchronous stereo vision system when simplifying motion assumptions are used. In order to achieve a metric reconstruction some knowledge about the scene is recovered by a simple and reliable calibration step. The major issue in calibration come from the asynchronism that complicate the proper frames selection. To overcome this problem a stereo frames selection based on epipolar gap evaluation (EGE) is proposed. Stereo disparity maps are evaluated by using both local and global approaches. To deal with brightness constancy model violation, zero-mean normalized cross-correlation is used as similarity measure in local approach, whereas a histogram equalization is necessary in global approach in order to improve min-cut based algorithms. Experimental results validate the proposed framework, allowing to define 3-D mosaics having visual quality similar to those obtained by using specialized hardware.

This paper presents a hardware and software framework for reliable fall detection in the home environment, with particular focus on the protection and assistance to the elderly. The integrated prototype includes three different sensors: a 3D time-of-flight range camera, a wearable MEMS accelerometer and a microphone. These devices are connected with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each of the three sensors, an optimized algorithm for fall-detection has been developed and benchmarked on a collected mulitimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.

The paper proposes new technologies to support independence and engagement in elderly people living alone at home in the framework of a new UE Integrated Project. Aim of the study is the development of a light technological infrastructure to be integrated in the homes of old people at reduced costs, allowing the detection of critical health situations. In particular a reliable fall detector is presented, with focus on the protection and assistance to the elderly in the home environment. The integrated fall detector prototype includes two different sensors: a 3D Time-of-Flight range camera and a wearable MEMS accelerometer. The devices are connected in a networked configuration with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each sensor, an optimized algorithm for fall-detection has been developed and benchmarked on a collected multimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system efficiency and reliability.

The paper presents an active vision system for the automatic detection of falls and the recognition of several postures for elderly homecare applications. A wall-mounted Time-Of-Flight camera provides accurate measurements of the acquired scene in all illumination conditions, allowing the reliable detection of critical events. Preliminarily, an off-line calibration procedure estimates the external camera parameters automatically without landmarks, calibration patterns or user intervention. The calibration procedure searches for different planes in the scene selecting the one that accomplishes the floor plane constraints. Subsequently, the moving regions are detected in real-time by applying a Bayesian segmentation to the whole 3D points cloud. The distance of the 3D human centroid from the floor plane is evaluated by using the previously defined calibration parameters and the corresponding trend is used as feature in a thresholding-based clustering for fall detection. The fall detection shows high performances in terms of efficiency and reliability on a large real dataset in which almost one half of events are falls acquired in different conditions. The posture recognition is carried out by using both the 3D human centroid distance from the floor plane and the orientation of the body spine estimated by applying a topological approach to the range images. Experimental results on synthetic data validate the correctness of the proposed posture recognition approach.

The chapter presents an automated monitoring system for the detection of dangerous events of elderly people (such as falls) in AAL applications. In order to provide a self-contained technology solution not requiring neither the environment rearrangement, nor the presence of specialized staff, nor a priori information about elderly characteristics/habitude, the focus is placed on the classification of human postures and the detection of related adverse events. The people is detected through a non-wearable device (a TOF camera), overcoming the limitations of the wearable approaches (accelerometers, gyroscopes, etc.) for human monitoring (the devices are prone to be incorrectly worn or forgotten). The system shows high performances in terms of efficiency and reliability on a large real dataset of falls acquired in different conditions. The posture recognition is carried out by using a topological approach on the 3D points cloud. Experimental results validate the soundness of the posture recognition scheme.

This paper presents a multi-sensor system for the detection of people falls in the home environment. Two kinds of devices are used: a MEMS wearable wireless accelerometer with onboard fall detection algorithms and a 3D Time-of-Flight camera. An embedded computing system receives the possible fall alarm data from the two sub-sensory systems and their associated level of confidence. The computing module hosts a data fusion software to operate the validation and correlation among the two subsystems delivered data in order to rise overall system efficiency performance with respect to each single sensor sub-system.

The paper presents an active vision system for the detection of dangerous fall events and the recognition of four main human postures (lie, sit, stand, bend) in Ambient Assisted Living applications. The suggested vision system uses a Time-Of-Flight camera providing accurate 3D measurements of the scene in all illumination conditions. In order to accommodate different installation setups, the system recovers automatically the own 3D position and orientation in the space, according to a floor detection strategy, without human intervention and calibration tools (landmarks, patterns, etc.). The moving people are detected in the 3D points cloud by applying segmentation/tracking methods and metric filtering. The distance of the 3D human centroid from the floor plane is evaluated by using the previously estimated calibration parameters and the corresponding trend is used as feature in a thresholding-based clustering for fall detection. The system shows high performances in terms of efficiency and reliability on a large real dataset of falls acquired in different conditions. The posture recognition is carried out by using both the 3D human centroid distance from the floor plane and the orientation of the body torso estimated by applying a topological approach to the range images. Experimental results on synthetic data validate the soundness of the proposed posture recognition approach.