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Detecting and Preventing Falls Using a Hybrid Technology System: A Review
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The main cause of fatal accidents in the construction sector are falls from height (FFH) and the inappropriate use of a harness is commonly associated with these fatalities. Traditional methods, such as onsite inspections, safety communication, or safety training, are not enough to mitigate accidents caused by FFH associated with a poor management in the use of a harness. Although some technological solutions for the automated monitoring of workers could improve safety conditions, their use is not frequent due to the particularities of construction sites: complexity, dynamic environments, outdoor workplaces, etc. Then, the integration of expert knowledge with technology is a key issue. Fuzzy logic systems (FLS) and Internet of Things (IoT) present many potential benefits, such as real-time decisions being made based on FLS and data from sensors. In the current research, the development and test of an IoT system integrated with the Java Fuzzy Markup Language Library for FLS, to support experts’ decision making in FFH, is proposed. The proposal was checked in four construction scenarios based on working conditions with different levels of risk of FFH and obtained promising results.
Fall risk assessment and fall detection are crucial for the prevention of adverse and long-term health outcomes. Wearable sensor systems have been used to assess fall risk and detect falls while providing additional meaningful information regarding gait characteristics. Commonly used wearable systems for this purpose are inertial measurement units (IMUs), which acquire data from accelerometers and gyroscopes. IMUs can be placed at various locations on the body to acquire motion data that can be further analyzed and interpreted. Insole-based devices are wearable systems that were also developed for fall risk assessment and fall detection. Insole-based systems are placed beneath the sole of the foot and typically obtain plantar pressure distribution data. Fall-related parameters have been investigated using inertial sensor-based and insole-based devices include, but are not limited to, center of pressure trajectory, postural stability, plantar pressure distribution and gait characteristics such as cadence, step length, single/double support ratio and stance/swing phase duration. The acquired data from inertial and insole-based systems can undergo various analysis techniques to provide meaningful information regarding an individual's fall risk or fall status. By assessing the merits and limitations of existing systems, future wearable sensors can be improved to allow for more accurate and convenient fall risk assessment. This article reviews inertial sensor-based and insole-based wearable devices that were developed for applications related to falls. This review identifies key points including spatiotemporal parameters, biomechanical gait parameters, physical activities and data analysis methods pertaining to recently developed systems, current challenges, and future perspectives.
Falls can have serious consequences for people, leading to restrictions in mobility or, in the worst case, to traumatic-based cases of death. To provide rapid assistance, a portable fall detection system has been developed that is capable of detecting fall situations and, if necessary, alerting emergency services without any user interaction. The prototype is designed to facilitate reliable fall detection and to classify several fall types and human activities. This solution represents a life-saving service for every person that
will significantly improve assistance in the case of fall events, which are a part of daily life. Additionally, this approach facilitates independent system operation, since the system does not depend on sensor or network units located within a building structure. This article also introduces fall analysis. To guarantee functional safety, a hazard analysis method named system-theoretic accident model and processes (STAMP) is applied.
Nationally, falls from height (FFH) are a significant threat to the construction fields and are one of the leading causes of a fatal accident to construction workers. Since the construction industry is carried out in hazardous environments, accidents occurring at various severity rates, leading to minor, severe and fatal injuries. Meanwhile, the majority of accidents are caused by a variety of significant causes and uncertain actions or unsafe conditions. The recognition effect of falls from height accidents at construction sites is the focus of this research. Therefore, this paper revealed the major effect of an accident due to a fall from height from past researchers. The reported cases of accidents were investigated by the Malaysian Department for Occupational Safety and Health (DOSH) were reviewed. The finding of this paper indicates a time loss of project execution due to accident investigation was the major effect of the accident due to fall from height at construction sites and cost implication for hiring a new worker, training for a new employee and compensation for injury or settlement of death claims. Discoveries of this paper will enhance the construction industry to improve the performance and regulation of all construction projects in terms of safety.
The risk of falls in older adults restrict their social life and independent living. The assisted living devices help older adults to live independently in their home, giving a psychological boost, and releasing the burden on the caregiver and the healthcare providers. A robust and accurate fall detection system is essential to provide immediate help and to reduce the severe post-fall consequences, and the associated medical care cost significantly. This review aims to provide a comprehensive technical insight into the existing fall detection system, to classify various approaches and the challenges encountered during implementation. The fall detectors are broadly classified into three categories, namely wearable, ambiance-based, and hybrid sensing detectors, which are further explored by the sensor technology. This review provides a comprehensive overview of each competing sensor technology ranging from an accelerometer, pressure sensor, and radar to camera-based and their infusion into a complete fall detection system. It outlines the strength and limitations of different sensor fall detection systems in terms of feature extraction, classification, performance, and experimental dataset. The user adaptability, installation complexity, and power requirement of the systems are the main areas, which are not addressed adequately in the literature. In the end, the review provides a basic framework in deciding the technology for a specific scenario or location according to the prerequisites for the deployment.
Background:
wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening.
Objective:
To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies.
Methods:
A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application.
Results:
We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application.
Conclusion:
This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.
Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems.
The increase in elderly population especially in the developed countries and the number of elderly people living alone can result in increased healthcare costs which can cause a huge burden on the society. With fall being one of the biggest risk among the elderly population resulting in serious injuries, if not treated quickly. The advancements in technology, over the years, resulted in an increase in the research of different fall detection systems. Fall detection systems can be grouped into the following categories: camera-based, ambient sensors, and wearable sensors. The detection algorithm and the sensors used can affect the accuracy of the system. The detection algorithm used can either be a decision tree or machine learning algorithms. In this paper, we study the different fall detection systems and the problems associated with these systems. The fall detection model which most recent studies implements will be analysed. From the study, it is found that personalized models are the key, for creating an accurate model and not limiting users to specific activities to perform.
Fall accidents constitute a crucial type of accident in the construction industry. This study investigates fall accidents that occurred in the United States between 1997 and 2012. Using the 20,997 construction industry accidents recorded in the Occupational Safety and Health Administration (OSHA) database, this study examines the frequency and trend of fall accidents. Additionally, by using data from 9,141 fall accidents, this study investigates various dimensions of fall accidents, such as fall height, fall location, and fall protection, and types of industry and projects where fall accidents occurred. The analyses and subsequent findings are discussed as follows: First, the percentage of fall accidents from four major accident types (fall, struck by, caught in or between, and electrocution) has been increased substantially. Second, in terms of project type, residential housing projects experienced a higher portion of fall accidents. Third, more than 80% of fall accidents occurred from a height of less than 9.1 m (30 ft), and only 11% of fall accident victims were properly equipped with fall protection. These findings serve to alert safety agencies of the need to diagnose the current state of fall accidents and to revise the policies and regulations to reduce these figures.
In this paper a fall detection system is presented that automatically detects the fall of a person and their location using an array of ultrasonic wave transducers connected to a field-programmable gate array (FPGA) processor. Experimental results are provided on a prototype deployment installed at an assisted living community. The system can provide a cost-effective and intelligent method to help caregivers detect a fall quickly so that patients are treated in a timely manner. In addition to room monitoring and local alert functions, the system incorporates a personal computer and wireless connection to enable remote monitoring of patient's activity and health status.
Every year, more than 37 million falls that require medical attention occur. The elderly suffer the greatest number of fatal falls. Therefore, automatic fall detection for elderly is one of the most important health-care applications as it enables timely medical intervention. The fall detection problem has extensively been studied over the last decade. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable algorithms with feasible computational cost is still an open research challenge. In this paper, a low-cost highly-accurate machine learning-based fall detection algorithm is proposed. Particularly, a novel online feature extraction method that efficiently employs the time characteristics of falls is proposed. In addition, a novel design of a machine learning-based system is proposed to achieve the best accuracy/numerical complexity trade-off. The low computational cost of the proposed algorithm not only enables to embed it in a wearable sensor but also makes the power requirements quite low and hence enhances the autonomy of the wearable device where the need for battery recharge/replace is minimized. Experimental results on a large open dataset show that the accuracy of the proposed algorithm exceeds 99:9% with a computational cost of less than 500 floating point operations per second.
The construction process is considered a very risky endeavor because of the high frequency of work-related
injuries and fatalities. The collection and analysis of safety data is an important element in measurement and
improvement strategy development. The adoption of wearable technology has the potential for a result-oriented data collection and analysis approach to providing real-time information to construction personnel. The objective of this paper is to provide a comprehensive review of the applications of wearable technology for personalized construction safety monitoring. The characteristics of wearable devices and safety metrics thought to
be capable of predicting safety performance and management practices are identified and analyzed. The review
indicates that the existing wearable technologies applied in other industrial sectors can be used to monitor and measure a wide variety of safety performance metrics within the construction industry. Benefits of individual wearable sensors or systems can be integrated based on their attributes for multi-parameter monitoring of safety performance.
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.
Falls are a major health problem for the frail community dwelling old people. For more than two decades, falls have been extensively investigated by medical institutions to mitigate their impact (e.g. lack of independence, fear of falling, etc.) and minimize their consequences (e.g. cost of hospitalization, etc.). However, the problem of elderly falling does not only concern health-professionals but has also drawn the interest of the scientific community. In fact, falls have been the object of many research studies and the purpose of many commercial products from academia and industry. These studies have tackled the problem using fall detection approaches exhausting a variety of sensing methods. Lately, researcher has shifted their efforts to fall prevention where falls might be spotted before they even happen. Despite their restriction to clinical studies, early-fall prediction systems have started to emerge. At the same time, current reviews in this field lack a common ground classification. In this context, the main contribution of this article is to give a comprehensive overview on elderly falls and to propose a generic classification of fall-related systems based on their sensor deployment. An extensive research scheme from fall detection to fall prevention systems have been also conducted based on this common ground classification. Data processing techniques in both fall detection and fall prevention tracks are also highlighted. The objective of this work is to deliver medical technologists in the field of public health a good position regarding fall-related systems.
This research aims to clarify the arguments in the body of knowledge on IT use in fall prevention among the elderly, synthesize ideas to assist in the delivery of healthcare to prevent falls in older people and further add to the available body of knowledge. An extensive literature search was carried out and the information retrieved from the literature was synthesised into paragraphs using themes to structure the types of information technology used for falls prevention. The different modalities of IT used in falls prevention at the different places of care for each category were explored and inferences were drawn from the structured themes which summarized the major findings. The research found that there is potential ground for a wider use of the forms of IT used in falls prevention in the elderly in various settings and outlined the factors involved in this usage. With further refinements in larger studies, many of these forms of IT would be better explored and acceptance is likely guaranteed provided they are accessible and affordable. The need for IT use in fall prevention in the elderly is unavoidable with the trend in technology and the associated convenience. More work is needed to further define the effects of IT in falls prevention using larger prospective studies that will be more generalizable.