Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Fall prevention intervention technologies: A conceptual framework and survey of the state of the art

In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge.The Royal Society, grant Ref: RG13082

INBED: A Highly Specialized System for Bed-Exit-Detection and Fall Prevention on a Geriatric Ward

OBJECTIVE:In geriatric institutions, the risk of falling of patients is very high and frequently leads to fractures of the femoral neck, which can result in serious consequences and medical costs. With regard to the current numbers of elderly people, the need for smart solutions for the prevention of falls in clinical environments as well as in everyday life has been evolving. METHODS:Hence, in this paper, we present the Inexpensive Node for bed-exit Detection (INBED), a comprehensive, favourable signaling system for bed-exit detection and fall prevention, to support the clinical efforts in terms of fall reduction. The tough requirements for such a system in clinical environments were gathered in close cooperation with geriatricians. RESULTS:The conceptional efforts led to a multi-component system with a core wearable device, attached to the patients, to detect several types of movements such as rising, restlessness and-in the worst case-falling. Occurring events are forwarded to the nursing staff immediately by using a modular, self-organizing and dependable wireless infrastructure. Both, the hardware and software of the entire INBED system as well as the particular design process are discussed in detail. Moreover, a trail test of the system is presented. CONCLUSIONS:The INBED system can help to relieve the nursing staff significantly while the personal freedom of movement and the privacy of patients is increased compared to similar systems

Development of a guided wave EMAT online inspection system for Al/Al-Sn/Al/steel and CuSn/steel bimetal strip bond quality control used in the automotive industry

Cold roll bonded (CRB) Al/Al-Sn/Al/steel and sintered CuSnNi/steel bimetal strips are used in the automotive industry for the manufacture of engine bearings, bushes and thrust washers. Any defects such as delamination or porosity that occur in bimetal strips during manufacturing can cause problems at downstream production steps and if they remain undetected, could result in components failing in the field, which is a significant business risk.;One way to reduce this business risk is to install a final inspection system on a continuous production line as the strip passes a fixed inspection point. In process control this could alert the operators to reject defective material and correct process parameters when the defect occurs. As this system requires 100% volumetric inspection, installing it has its challenges due to the harsh manufacturing environment in which the strip moves at up to 20 m/min in the processing lines at room temperature.;A literature review and feasibility study on different non-destructive testing (NDT) techniques to inspect bond quality of CRBed Al/Al-Sn/Al/steel bimetal strips was conducted to assess technologies that could be developed for serial inspection. Guided waves generated using Electromagnetic Acoustic Transducers (EMATs) was identified as best suited for this application. Since this technology was not available off-the-shelf, significant research and experimental work was carried out to develop an automated prototype system.;The system was successfully installed at a strip processing line and demonstrated the online bond inspection capability for Al/Al-Sn/Al/steel and CuSnNi/steel bimetal strips, which is the main achievement of this EngD project. For CuSnNi/steel strips, causes of defects and preventative control measures were studied and examined. Industrialisation of the inspection system will significantly reduce the company business risk and improve bond quality of bimetal strips.Cold roll bonded (CRB) Al/Al-Sn/Al/steel and sintered CuSnNi/steel bimetal strips are used in the automotive industry for the manufacture of engine bearings, bushes and thrust washers. Any defects such as delamination or porosity that occur in bimetal strips during manufacturing can cause problems at downstream production steps and if they remain undetected, could result in components failing in the field, which is a significant business risk.;One way to reduce this business risk is to install a final inspection system on a continuous production line as the strip passes a fixed inspection point. In process control this could alert the operators to reject defective material and correct process parameters when the defect occurs. As this system requires 100% volumetric inspection, installing it has its challenges due to the harsh manufacturing environment in which the strip moves at up to 20 m/min in the processing lines at room temperature.;A literature review and feasibility study on different non-destructive testing (NDT) techniques to inspect bond quality of CRBed Al/Al-Sn/Al/steel bimetal strips was conducted to assess technologies that could be developed for serial inspection. Guided waves generated using Electromagnetic Acoustic Transducers (EMATs) was identified as best suited for this application. Since this technology was not available off-the-shelf, significant research and experimental work was carried out to develop an automated prototype system.;The system was successfully installed at a strip processing line and demonstrated the online bond inspection capability for Al/Al-Sn/Al/steel and CuSnNi/steel bimetal strips, which is the main achievement of this EngD project. For CuSnNi/steel strips, causes of defects and preventative control measures were studied and examined. Industrialisation of the inspection system will significantly reduce the company business risk and improve bond quality of bimetal strips

Technology-assisted healthcare: exploring the use of mobile 3D visualisation technology to augment home-based fall prevention assessments

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonFalls often cause devastating injuries which precipitate hospital and long-term care admission and result in an increased burden on health care services. Fall prevention interventions are used to overcome fall risk factors in an ageing population. There is an increasing need for technology-assisted interventions to reduce health care costs, whilst also lessening the burden that an ageing population increasingly has on health care services. Research efforts have been spent on reducing intrinsic fall risk factors (i.e. functional ability deficits and balance impairments) in the older adult population through the use of technology-assisted interventions, but relatively little effort has been expended on extrinsic risk factors (i.e. unsuitable environmental conditions and lack of assistive equipment use), considering the drive for healthcare outside of the clinical setting into the patients’ home. In the field of occupational therapy, the extrinsic fall-risk assessment process (EFAP) is a prominent preventive intervention used to promote independent living and alleviate fall risk factors via the provision of assistive equipment prescribed for use by patients in their home environment. Currently, paper-based forms with measurement guidance presented in the form of 2D diagrams are used in the EFAP. These indicate the precise points and dimensions on a furniture item that must be measured as part of an assessment for equipment. However, this process involves challenges, such as inappropriate equipment prescribed due to inaccurate measurements being taken and recorded from the misinterpretation of the measurement guidance. This is largely due to the poor visual representation of guidance that is provided by existing paper-based forms, resulting in high levels of equipment abandonment by patients. Consequently, there is a need to overcome the challenges mentioned above by augmenting the limitations of the paper-based approach to visualise measurement guidance for equipment. To this end, this thesis proposes the use of 3D visualisation technology in the form of a novel mobile 3D application (Guidetomeasure) to visualise guidance in a well-perceived manner and support stakeholders with equipment prescriptions. To ensure that the artefact is a viable improvement over its 2D predecessor, it was designed, developed and empirically evaluated with patients and clinicians alike through conducting five user-centred design and experimental studies. A mixed-method analysis was undertaken to establish the design, effectiveness, efficiency and usability of the proposed artefact, compared with conventional approaches used for data collection and equipment prescription. The research findings show that both patients and clinicians suggest that 3D visualisation is a promising development of an alternative tool that contains functionality to overcome existing issues faced in the EFAP. Overall, this research makes a conceptual contribution (secondary) to the research domain and a software artefact (primary) that significantly improves practice, resulting in implications and recommendations for the wider healthcare provision (primary).The Engineering and Physical Sciences Research Council (EPSRC)

Automated Mixed Traffic Vehicle (AMTV) technology and safety study

Technology and safety related to the implementation of an Automated Mixed Traffic Vehicle (AMTV) system are discussed. System concepts and technology status were reviewed and areas where further development is needed are identified. Failure and hazard modes were also analyzed and methods for prevention were suggested. The results presented are intended as a guide for further efforts in AMTV system design and technology development for both near term and long term applications. The AMTV systems discussed include a low speed system, and a hybrid system consisting of low speed sections and high speed sections operating in a semi-guideway. The safety analysis identified hazards that may arise in a properly functioning AMTV system, as well as hardware failure modes. Safety related failure modes were emphasized. A risk assessment was performed in order to create a priority order and significant hazards and failure modes were summarized. Corrective measures were proposed for each hazard

A deep learning approach towards railway safety risk assessment

Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks

Autonomous Pedestrian Detection in Transit Buses

This project created a proof of concept for an automated pedestrian detection and avoidance system designed for transit buses. The system detects objects up to 12 meters away, calculates the distance from the system using a solid-state LIDAR, and determines if that object is human by passive infrared. This triggers a visual and sound warning. A Xilinx Zynq-SoC utilizing programmable logic and an ARM-based processing system drive data fusion, and an external power unit makes it configurable for transit-buses

Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach