Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.

/pdf/challenges-issues-and-trends-in-fall-detection-systems-2s75b5dfqs.pdf

Challenges, issues and trends in fall detection systems

This paper presents a novel fall detection system based on the Kinect sensor. The system runs in real-time and is capable of detecting walking falls accurately and robustly without taking into account any false positive activities (i.e. lying on the floor). Velocity and inactivity calculations are performed to decide whether a fall has occurred. The key novelty of our approach is measuring the velocity based on the contraction or expansion of the width, height and depth of the 3D bounding box. By explicitly using the 3D bounding box, our algorithm requires no pre-knowledge of the scene (i.e. floor), as the set of detected actions are adequate to complete the process of fall detection.

Fall detection system using Kinect's infrared sensor

Energy demand has been increasing worldwide and the building sector represents a large percentage of global energy consumption. Therefore, promoting energy efficiency in buildings is essential. Among all building services, Heating, Ventilation and Air Conditioning (HVAC) systems are significantly responsible for building energy use. In HVAC, ventilation is the key issue for providing suitable Indoor Air Quality (IAQ), while it is also responsible for energy consumption in buildings. Thus, improving ventilation systems plays an important role not only in fostering energy efficiency in buildings, but also in providing better indoor climate for the occupants and decreasing the possibility of health issues in consequence. In the last decades, many energy-efficient ventilation methods are developed by researchers to mitigate energy consumption in buildings. This paper reviews scientific research and reports, as well as building regulations and standards, which evaluated, investigated and reported the development of energy-efficient methods for ventilation in buildings. Besides energy-efficient methods such as natural and hybrid ventilation strategies, occupants’ behaviours regarding ventilation, can also affect the energy demand in buildings. Therefore, the influence of occupants’ behaviour on the energy use and the correlation between ventilation and the occupants’ health and productivity were also considered. The review showed that ventilation is interrelated with many factors such as indoor and outdoor conditions, building characteristics, building application as well as users’ behaviour. Thus, it is concluded that many factors must be taken into account for designing energy-efficient and healthy ventilation systems. Moreover, it should be mentioned that utilizing hybrid ventilation in buildings integrated with suitable control strategies, to adjust between mechanical and natural ventilation, leads to considerable energy savings while an appropriate IAQ is maintained.

Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review

Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

DistancePPG: Robust non-contact vital signs monitoring using a camera

Falls and fall-related injuries are major incidents, especially for elderly people, which often mark the onset of major deterioration of health. More than one-third of home-dwelling people aged 65 or above and two-thirds of those in residential care fall once or more each year. Reliable fall detection, as well as prevention, is an important research topic for monitoring elderly living alone in residential or hospital units. The aim of this study is to review the existing fall detection systems and some of the key research challenges faced by the research community in this field. We categorize the existing platforms into two groups: wearable and ambient devices; the classification methods are divided into rule-based and machine learning techniques. The relative merit and potential drawbacks are discussed, and we also outline some of the outstanding research challenges that emerging new platforms need to address.

/pdf/automatic-fall-monitoring-a-review-5jihhisvq7.pdf

Automatic Fall Monitoring: A Review

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.

An active vision system for fall detection and posture recognition in elderly healthcare

A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications.

Detecting falls with 3D range camera in ambient assisted living applications: A preliminary study

People occupancy detection and profiling with 3D depth sensors for building energy management

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.

Giovanni Diraco

Papers

An active vision system for fall detection and posture recognition in elderly healthcare

A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications.

Detecting falls with 3D range camera in ambient assisted living applications: A preliminary study

People occupancy detection and profiling with 3D depth sensors for building energy management

A hardware-software framework for high-reliability people fall detection