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

Prior Knowledge Based Motion Model Representation

This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points' trajectories. Peaks and valleys of these trajectories are used to detect key frames-frames where both legs are in contact with the floor. Secondly, motion models associated with each joint are locally tuned by using those key frames. Differently than previous approaches, this tuning process is not performed at every frame, reducing CPU time. In addition, the movement's frequency is defined by the elapsed time between two consecutive key frames, which allows handling walking displacement at different speed. Experimental results with different video sequences are presented

Computer vision based posture estimation and fall detection.

Falls are a major health problem, especially in the elderly population. Increasing fall events demands a high quality of service and dedicated medical treatment which is an economic burden. Serious injuries due to fall can cost lives in the absence of immediate care and support. There- fore, a monitoring system that can accurately detect fall events and generate instant alerts for immediate care is extremely necessary. To address this problem, this research aims to develop a computer vision-based fall detection system. This study proposes fall detection in three stages: (A) Detection of human silhouette and recognition of the pose, (B) Detection of the human as three regions for different postures including fall and (C) Recognise fall and non-fall using locations of human body regions as distinguishing features. The first stages of work comprise human silhouette detection and identification of activities in the form of different poses. Identifying a pose is important to understand a fall event where a change of pose defines its characteristics. A fall event comprises of sequential change of poses and ends up in a lying pose. Initial pose during a fall can be standing, sitting or bending but the final pose is usually a lying pose. It would, therefore, be beneficial if lying pose is recognised more accurately than other normal activities such as standing, sitting, bending or crawling to address a fall. Hence in the first stage, Background Subtraction (BS) is used to detect human silhouette. After background subtraction, the foreground images were used in a Convolutional Neural Network (CNN) to recognise different poses. The RGB and the Depth images were captured from a Kinect Sensor. The fusion of RGB and Depth images were explored for feeding to a convolutional neural net- work. Depth together with RGB complimented each other to overcome their weakness respectively and proved to be a significant strategy. The classification was performed using CNN to recognise different activities with 81% accuracy on validation. The other challenge in fall detection is the tracking of a person during a fall. Background Subtraction is not sufficient to track a fallen person especially when there are lighting and viewpoint variations in the environment and present of another object like furniture, a pet or even another person. Furthermore, tracking be- comes tougher during the fall in comparison to normal activities like walking or sitting because the rate of change pose is higher during a fall. To overcome this, the idea is to locate the regions in the body in every frame and consider it as a stable tracking strategy. The location of the body parts provides crucial information to distinguish falls from the other normal activities as the person is detected all the time during these activities. Hence the second stage of this research consists of posture detection using the pose estimation technique. This research proposes to use CNN based pose estimation using simplified human postures. The available joints are grouped according to three regions: Head, Torso and Leg and then finally fed to the CNN model with just three inputs instead of several available joints. This strategy added stability in pose detection and proved to be more effective against complex poses observed during a fall. To train the CNN model, transfer learning technique was used. The model was able to achieve 96.7% accuracy in detecting the three regions on different human postures on the publicly available dataset. A system which considers all the lying poses as falls can also generate a higher false alarm. Lying on bed or sofa can easily generate a fall alarm if they are recognised as falls. Hence, it is important to recognise actual fall by considering a sequence of frames that defines a fall and not just the lying pose. In the third and final stage, this study proposes Long Short-Term Memory (LSTM) recurrent networks-based fall detection. The proposed LSTM model uses the detected three region’s location as input features. LSTM is capable of using contextual information from the sequential input patterns. Therefore, the LSTM model was fed with location features of different postures in a sequence for training. The model was able to learn fall patterns and distinguish them from other activities with 88.33% accuracy. Furthermore, the precision of the fall class was 1.0. This is highly desirable in the case of fall detection as there is no false alarm and this means that the cost incurred in calling medical support for a false alarm can be completely avoided

Human Action Recognition with RGB-D Sensors

Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset

2.5D multi-view gait recognition based on point cloud registration

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM

Human Pose Estimation from Monocular Images : a Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problema into several modules: feature extraction and description, human body models, and modelin methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used

Novel methods for posture-based human action recognition and activity anomaly detection

PhD ThesisArti cial Intelligence (AI) for Human Action Recognition (HAR) and Human Activity Anomaly Detection (HAAD) is an active and exciting research eld. Video-based HAR aims to classify human actions and video-based HAAD aims to detect abnormal human activities within data. However, a human is an extremely complex subject and a non-rigid object in the video, which provides great challenges for Computer Vision and Signal Processing. Relevant applications elds are surveillance and public monitoring, assisted living, robotics, human-to-robot interaction, prosthetics, gaming, video captioning, and sports analysis. The focus of this thesis is on the posture-related HAR and HAAD. The aim is to design computationally-e cient, machine and deep learning-based HAR and HAAD methods which can run in multiple humans monitoring scenarios. This thesis rstly contributes two novel 3D Histogram of Oriented Gradient (3D-HOG) driven frameworks for silhouette-based HAR. The 3D-HOG state-of-the-art limitations, e.g. unweighted local body areas based processing and unstable performance over di erent training rounds, are addressed. The proposed methods achieve more accurate results than the baseline, outperforming the state-of-the-art. Experiments are conducted on publicly available datasets, alongside newly recorded data. This thesis also contributes a new algorithm for human poses-based HAR. In particular, the proposed human poses-based HAR is among the rst, few, simultaneous attempts which have been conducted at the time. The proposed HAR algorithm, named ActionXPose, is based on Convolutional Neural Networks and Long Short-Term Memory. It turns out to be more reliable and computationally advantageous when compared to human silhouette-based approaches. The ActionXPose's exibility also allows crossdatasets processing and more robustness to occlusions scenarios. Extensive evaluation on publicly available datasets demonstrates the e cacy of ActionXPose over the state-of-the-art. Moreover, newly recorded data, i.e. Intelligent Sensing Lab Dataset (ISLD), is also contributed and exploited to further test ActionXPose in real-world, non-cooperative scenarios. The last set of contributions in this thesis regards pose-driven, combined HAR and HAAD algorithms. Motivated by ActionXPose achievements, this thesis contributes a new algorithm to simultaneously extract deep-learningbased features from human-poses, RGB Region of Interests (ROIs) and detected objects positions. The proposed method outperforms the stateof- the-art in both HAR and HAAD. The HAR performance is extensively tested on publicly available datasets, including the contributed ISLD dataset. Moreover, to compensate for the lack of data in the eld, this thesis also contributes three new datasets for human-posture and objects-positions related HAAD, i.e. BMbD, M-BMdD and JBMOPbD datasets