Next-generation fall detection: harnessing human pose estimation and transformer technology

Review question/objective The qualitative and quantitative objective of this review is to scope the extant knowledge about family involvement in decision making for people with dementia living in residential aged care. More specifically, the review question/s are: •Who are the decision makers for people with dementia living in residential care? •What is the experience of decision making for family members in the residential care setting? •What are the barriers or facilitators to decision making by families? •What is the impact of decision making processes on family members? •What is the impact of collaborative decision making with family on the person with dementia? • What processes or strategies do family decision-makers use? Inclusion criteria Types of participants This review will consider studies that include people with dementia who are living in residential aged care, their families and care staff. For the purpose of this review, the definition of “dementia” is not restricted to any specific diagnosis, stage or degree of severity. There is no age limit on participants included, provided they are living in residential aged care. Care staff may include any and all types of staff involved in providing care to people with dementia in residential aged care, including but not limited to nursing staff, medical staff and direct care staff. Types of intervention(s)/phenomena of interest This review will consider both quantitative and qualitative studies that investigate involvement of family members in decision making for people with dementia in a residential aged care setting. For this systematic review, decision making for people with dementia in a residential aged care setting refers to decisions relating to the physical, psychosocial, spiritual and emotional domains of care, for example decisions relating to advance care planning, decisions regarding clinical care, decisions regarding end-of-life care, and decisions regarding leisure and lifestyle. Types of outcomes This review will consider studies that investigate or discuss who the decision makers are in care for people with dementia and how decisions are made, what processes assist families in decision making and what are the barriers or facilitators to collaborative decision making by families, as well as the impact of decision making processes on family members and the impact of collaborative decision making with family on the person with dementia. The review will explicitly exclude studies that are solely concerned with decisions about whether to enter residential aged care or decisions regarding financial issues; however where this data forms part of a larger study, the study may be included but this data will not be extracted.

Falls are the main cause of injury in the elderly people. Therefore, fall detection system in the elderly is an essential technology for aging society. This project was designed to develop the fall detection system based on wearable sensors and Internet of Things for the real-time fall detection, data storage, calculation, and notification. The raw data, kinematics signal from the daily living of elder, including three-dimensional accelerometer and gyroscope are provided by GY-85, which have been sent bidots cloud via NodeMCU-ESP32. Further, the NodeMCU-ESP32 avail of the raw data to specify the fall detection algorithm. Moreover, the NodeMCU-ESP32 is designed to achieve the seamless communication between the fall detection device and system to acquire and process the data anywhere anytime as long as there is wifi connection. Consequently, the Ubidots cloud is designed to perform the calculation based on support vector machine (SVM) theory. It also sends the fall detection result via LINE notification (chat application) to caregivers. The tests of the proposed system are performed based on activities of daily living and the directions that fall can occur. The results of the testing show that the proposed system can provide high accuracy and reliability in fall detection.

Falling can have fatal consequences for elderly people especially if the fallen person is unable to call for help due to loss of consciousness or any injury. Automatic fall detection systems can assist through prompt fall alarms and by minimizing the fear of falling when living independently at home. Existing vision-based fall detection systems lack generalization to unseen environments due to challenges such as variations in physical appearances, different camera viewpoints, occlusions, and background clutter. In this paper, we explore ways to overcome the above challenges and present Single Shot Human Fall Detector (SSHFD), a deep learning based framework for automatic fall detection from a single image. This is achieved through two key innovations. First, we present a human pose based fall representation which is invariant to appearance characteristics. Second, we present neural network models for 3d pose estimation and fall recognition which are resilient to missing joints due to occluded body parts. Experiments on public fall datasets show that our framework successfully transfers knowledge of 3d pose estimation and fall recognition learnt purely from synthetic data to unseen real-world data, showcasing its generalization capability for accurate fall detection in real-world scenarios.

Elderly falls are a growing phenomenon observed within the world. According to World Health Organization (WHO), it is the second leading cause of unintentional or accidental deaths among the elderly. Thus, the need for research regarding the development of fall detection systems is imperative. Researchers have utilized various approaches to develop fall detection systems, significant number of which have employed Machine Leaning (ML) algorithms for fall detection. In this study, we evaluated the efficiency of six ML algorithms on a public fall detection dataset. A robust deep neural network for fall detection (FD-DNN) is identified to be the current state-of-the-art, it detects falls by using a self-built sensor that consumes low power. By evaluating the efficiency of six machine learning algorithms on a publicly available joint fall detection dataset, the accuracy of the fall detection was increased from 99.17% to 99.88% by using the K-nearest Neighbor indicating that common machine learning algorithms can achieve identical or higher accuracy rendering the complex and expensive deep neural network-based fall detection systems inefficient.KeywordsFall detectionElderly fall detectionMachine learning algorithmsWearable fall detection systemSisFall datasetMobiFall datasetK-nearest neighborsKNNSupport vector machinesSVMDecision treeRandom forestLogistic regressionNaïve Bayes

Human fall detection is a critical research area focused on developing methods and systems that can automatically detect and recognize falls, particularly among the elderly and individuals with disabilities. Falls are a major cause of injuries and deaths among these populations, and timely intervention can reduce the severity of consequences. This article presents a comprehensive review of fall detection systems, emphasizing the use of cutting-edge technologies such as deep learning, sensor fusion, and machine learning. The research explores a variety of methodologies and strategies employed in fall detection systems, including the integration of wearable sensors, smartphones, and cameras. By examining various fall detection techniques and their experimental results, the article highlights the effectiveness of these systems in identifying and classifying falls. The study also addresses the challenges and limitations associated with fall detection systems, emphasizing the need for ongoing research and advancements. In summary, this research contributes to the development of advanced fall detection systems, demonstrating their potential to improve the quality of life for the elderly, alleviate healthcare burdens, and provide reliable solutions for fall detection and classification.

When elderly patients are transferred from a residential to an acute care setting, important information regarding their health care can be lost. Over the past years, the concept of advance care planning has also been given a more prominent place in the care for the elderly. However it remains a challenge to communicate the results achieved by this process when patients are referred to another health care setting. Developing a sound method for transferring information is a key element in the transitional care for the elderly patient. In collaboration with the residential and acute care settings in Leuven, Flemish Brabant, Belgium this study aimed to develop a validated, standardized transfer-sheet. After a literature search a topic list was generated to be used as the basis for a Delphi-procedure in which 16 experts from both the acute and the residential care settings participated. The transfer-sheet was then evaluated for content validity by an expert-panel (n = 9) from the acute and residential care settings. Face validity was assessed by two nurses and two doctors, randomly selected from the above settings. All 44 subthemes in the transfer-sheet showed excellent content validity. The scale content validity universal agreement (S CVIUA) for the entire transfer-sheet was 0.68. The average scale content validity (S CVIAve) was 0.96. After a second and final Delphi-round a final transfer-sheet was constructed consisting of 8 themes and 50 sub-themes. Based on these results standardized transfer-sheet was developed and validated.

Elderly are the world’s largest growing population, categorized over the age of 60 to 65 years. They are the ones who prone to fall due to their old age and low self-efficacy, thus making them vulnerable to different accidents. Even doing daily activities can also expose the elderly to a fall incident. As a result, it has gained the attention of many researchers in conducting studies related to the elderly daily health care, especially in relation to the fall detection system. This paper aims to provide a systematic review on the classification of fall detection systems for the elderly. This systematic review is designed based on the existing and extensive literature review on fall detection systems guided by the prisma statement (preferred reporting items for systematic reviews and meta-analyses) review method. Based on this systematic review, four overarching themes that provide in-depth information on fall detection to detect fall events have been identified; classification of fall detection, basis development, type of sensor and detection technique. In a nutshell, the fall detection approach has successfully provided an alternative health care services for elderly who choose to live independently. Therefore, it is important to continue to develop a fall detection system that integrates with technology in order to provide a safe living environment for elderly, and for children, it can offer as an alternative for monitoring systems.

The machine learning revolution is redesigning modern healthcare, and with the growth of the elderly population, fall detection has become an important research topic in healthcare. This paper surveys advances in machine learning-based fall detection technologies and reviews sensor-based, image processing-based, and wearable sensor-based fall detection systems and applications. In addition, this paper proposes a self-learning posture analysis and eye status-based fall detection system to solve the issue of mis-detections in fall detection systems, which have not been addressed in past works. Furthermore, this work proposes an image-feature-separation system that can use image processing with a low risk of privacy disclosure. Moreover, this work establishes a dataset, which includes 36 non-fall/fall cases comprising 25,200 images that can be used not only for this research but also in related studies. Experimental results show that this system can detect a fall with high accuracy and solve mis-detections in machine learning-based fall detection systems.

Objectives To present the best available evidence related to the identification and management of medication incidents (errors) associated with the prescribing, dispensing and administration of medicines in the older person in the acute, subacute, and residential care settings. Questions The specific review questions to be addressed are: • What types of medication incidents (errors) have been identified in the acute, subacute and residential care settings? • What causes of medication incidents (errors) have been identified in the acute, subacute and residential care settings? • What strategies/interventions are most effective in reducing the incidence of medication incidents (errors) in the acute, subacute and residential care settings? Criteria for considering studies for this review Types of participants Those people who are involved in the prescribing, dispensing or administering of medication to the older person (aged 65 years and older) in the acute, sub acute or residential care settings will be included in the review, namely: • Registered nurses • Enrolled nurses (or equivalent eg. LPN) • Pharmacists • Physicians/medical practitioners (or equivalents) • Personal care attendants/ancillary staff (or equivalent) Types of interventions All studies reviewing the following will be included in the review: 1. Identification of different types of medication incidents (errors)in the acute, sub-acute and residential settings 2. a. preventable • incidents (errors) in prescribing of medication (dose, route, time, patient, legibility, known allergies) • incidents (errors) in dispensing of medication (packaging, labelling) • incidents (errors) in method of administration of a drug (drug, dose, route, time, patient, known allergies) b. non-preventable 3. Strategies to prevent medication incidents (errors) in the acute, sub-acute and residential settings Types of outcome measures The outcome measures of interest to be considered will include: • Identification of the causes of medication incident (error) • Strategies that reduce the number and type of medication incident (error)

COVID‐19 pandemic experiences of older adults with dementia in community and residential care settings: Results from a US national survey

Current evidence about the effects of hygiene and emollients in maintaining skin integrity in older people in residential and hospital settings is inadequate. We cannot draw conclusions regarding frequency of skin damage or side effects due to very low-quality evidence. Low-quality evidence suggests that in residential care settings for older people, certain types of hygiene and emollient interventions (two different standardised skin care regimens; moisturising soap bar; combinations of water soak, oil soak, and lotion) may be more effective in terms of clinical score of dryness when compared with no intervention or standard care. Studies were small and generally lacked methodological rigour, and information on effect sizes and precision was absent. More clinical trials are needed to guide practice; future studies should use a standard approach to measuring treatment effects and should include patient-reported outcomes, such as comfort and acceptability.

Falls among older people remain a very important public healthcare issue. Every year over 11 million falls are registered in the U.S. alone. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The final decision of a fall event is taken based on the location of the user. To the best of our knowledge, this is the first fall detection system which works on an independent smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By analyzing real data of activities of daily life of elderly people, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account the residual movement and the location of the user to increase the fall detection accuracy. By testing with real data we have a fall detection system ready to be deployed on the market.

This scoping review will identify and map the available literature on virtual care for chronic wound management in residential aged care settings. Chronic wounds are an underacknowledged public health issue with substantial health and economic costs. These wounds, which do not heal in a timely manner, are common in residential aged care settings. While evidence-based wound care is essential for better outcomes, access is challenging in residential aged care settings. Emerging virtual care interventions have improved access to evidence-based wound care; however, little is known about the current use of such interventions in residential aged care settings. This review will include adults with chronic wounds living in residential aged care across diverse geographic locations. The review will consider virtual care for chronic wounds, with a particular focus on intervention type and frequency, as well as barriers and enablers of use. The proposed review will be conducted in accordance with the JBI methodology for scoping reviews and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). MEDLINE (Ovid), CINAHL (EBSCOhost), Embase (Ovid), AgeLine, IEEE Xplore, and ACM digital library will be searched for relevant literature, with no limitations on language or date of publication. The World Health Organization's International Clinical Trials Registry Platform (WHO ICTRP) and the US National Institutes of Health Ongoing Trials Register will be searched for gray literature. Title, abstract, and full-text screening will be conducted by 2 reviewers independently. Data will be extracted using a reviewer-developed tool, and a narrative summary will accompany the tabulated results. OSF https://osf.io/j97bt.

Elderly suffers from injuries or disabilities through falls every year. With a high likelihood of falls causing serious injury or death, falling can be extremely dangerous, especially when the victim is home-alone and is unable to seek timely medical assistance. Our fall detection systems aims to solve this problem by automatically detecting falls and notify healthcare services or the victim’s caregivers so as to provide help. In this paper, development of a fall detection system based on Kinect sensor is introduced. Current fall detection algorithms were surveyed and we developed a novel posture recognition algorithm to improve the specificity of the system. Data obtained through trial testing with human subjects showed a 26.5% increase in fall detection compared to control algorithms. With our novel detection algorithm, the system conducted in a simulated ward scenario can achieve up to 90% fall detection rate.KeywordsFall DetectionKinectPosture Recognition

Elder abuse is a global human rights issue, particularly in residential care settings where there is limited understanding of older people's experiences of this phenomenon. This scoping review aims to map and describe the existing literature on this phenomenon. Using Arksey and O'Malley's scoping review methodology, further developed by Levac etal. and the Joanna Briggs Institute, a systematic search of six databases was conducted to identify relevant studies published from inception to January 2023. Studies were included if they focused on older people's experience of abuse within residential care settings. A comprehensive data extraction process was employed to identify key themes from the included studies. The initial search yielded a total of 3701 articles of which eight met the inclusion criteria. The findings revealed a range of abusive experiences encountered by older people living in residential care settings, including psychological, physical, financial, neglect and sexual abuse. The common attributes of residents vulnerable to abuse were evident throughout each of the studies. Finally, the theme of organizational neglect was apparent through the absence of effective safeguarding measures in the included studies. The findings revealed the insufficient implementation of safeguarding measures increases the risk of abuse among residents. This scoping review highlights the limited research on the experiences of older people who have encountered elder abuse in residential care settings. The findings stress the need for further research exploring the complex interplay of factors contributing to elder abuse within aged care settings. The insights gained from this scoping review can inform the development of comprehensive safeguarding strategies to prevent and address elder abuse in residential care settings, promoting the well-being and safety of older people. This project is a scoping review of the literature; therefore, no patient or public contribution was deemed necessary. Most research on elder abuse has been conducted in the community rather than in Residential Care Settings where there is a limited understanding of older people's experiences of abuse within the published literature. The scoping review highlights the presence of inadequate safeguarding strategies in Residential Care Settings within the reviewed studies, providing recommendations for developing and improving safeguarding measures in Residential Care Settings to prevent abuse, neglect, and harm in the future.