This study analyzed the risk of falls in the elderly and the safety of the environment in a teaching hospital in Brazil. The Morse Falls Scale was used to stratify the risk of falls in 45 hospitalized elderly individuals, and a checklist was used to analyze the hospital environment. The analysis was based on the chi-square test and multiple regression. The moderate risk of falls was predominant (51.1%). The variable age group (p-value = 0.024) showed statistical evidence of association with the risk of falls. However, the multiple regression analysis showed no difference between the age groups and the risk situation for falls. The hospital wards showed an adequate arrangement of furniture, but some aspects had inadequacies, such as objects in the corridors, non-functional bells in some beds, and inadequacy of the toilet bowls in terms of the recommended height, the absence of non-slip flooring and the support bar in some bathrooms. In conclusion, the moderate risk of falls among the elderly and the adequacy of the hospital environment to technical standards were evident, with the exception of failures in the emergency communication system and sanitary installation.

Fall are significant cause of mortality and disability among older adults globally, affecting individual health and society. Addressing preventable factors, such as physical health and environmental conditions, is essential. This cross-sectional study aims to understand the factors that influence fall prevention behaviors among older adults in semi-urban community. Methods: A total of 440 older adults were randomly selected from six districts in Samutprakarn Province, Thailand. Well-trained data collectors conducted individual interviews with the participants in community-based setting. Results: Multiple logistic regression analysis was conducted, revealing that 96.3% of older adults demonstrated good fall prevention behaviors. The factors significantly associated with these behaviors included being female (OR = 4.39, 95% CI: 1.42-13.52), having good knowledge of fall prevention (OR = 9.19, 95% CI: 2.78-30.38), and perceiving the severity and susceptibility of falls, along with the benefits and barriers to prevention (OR = 11.25, 95% CI: 3.36-37.69). Additionally, sufficient access to health information (OR = 4.94, 95% CI: 1.61-15.16) and strong social support (OR = 4.06, 95% CI: 1.30-12.71) were also significant factors. In conclusion, a high percentage of older adults practiced good fall prevention behaviors, influenced by factors such as gender, knowledge, perception of fall risks, access to health information, and social support. Policy makers should prioritize enhancing health education on fall prevention, improving access to health information, and strengthening social support systems for older adults to further reduce fall-related risks and disabilities.

Fall detection systems play a crucial role in addressing the significant health concern of elderly falls, a leading cause of health deterioration and mortality. As the aging population grows and life expectancy increases, the development of accessible tools becomes vital for predicting and preventing falls, offering a practical and widely applicable solution in contrast to costly and expertise-dependent assessment tools. In contrast, due to the formidable challenges encountered, a comprehensive investigation into the comparative performance of standard ML models within this field still needs to be explored. This paper proposes a standard pipeline for pre-processing, training, and evaluating ML models for fall detection on the SisFall dataset. We conducted extensive experiments to evaluate the performance of various ML models for fall detection. The results validate the efficiency of the deep model in identifying the time windows in which a fall occurred. Among the deep models, the architecture, including a combination of convolutional neural networks and fully connected layers, outperforms the others by macro-averaged Precision, macro-averaged Recall, and macro-averaged F1-Score of 87.03\%, 86.83\%, and 86.93\%, respectively.

Falls are a major public health problem among older adults, therefore predicting the risk of having falls in the near future is of great importance for health and social systems worldwide. Nowadays, prospective fall risk assessment relies on clinical and functional mobility assessment tools, such as the Timed Up and Go (TUG) test. Recently, wearable inertial measurement unit (IMU) sensors measurements have been proposed for fall detection. We hypothesize that the IMU readings captured during TUG test realizations can provide prospective fall risk prediction. In this study, analysis by deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) were used for fall prediction based on features extracted from IMU data acquired during TUG test realizations. Data is obtained from a cohort of 106 older adults wear-ing a wireless inertial sensor with a sampling frequency of 100 Hz while performing the TUG test. Prospective fall incidence was obtained in a six-month follow-up period and used as the ground truth for the supervised training and validations of the deep learning and competing machine learning approaches. Further data collection could lead to potential fall risk biomarker. A repeated hold out cross-validation process using 75 subjects for training and 31 subjects for testing was carried out. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

Introduction: Epilepsy is one of the most common neurological diseases. Epilepsy poses a significant burden on the quality of life of affected individuals and their families. The aim of this study was to determine the psychiatric disorders in epileptic patients. Methods and Materials: This descriptive-analytical study was conducted in 2017 with a simple random sampling method on patients with epilepsy who admitted to the neurology department. Results: Among the 150 examined patients, 88 (58.7%) were female and 63(42%) had epilepsy more than 10 years. The most common psychiatric disorder among epileptic patients was depression (68 patients = 45.3%) and anxiety (65 patients = 43.3%) patients. Conclusion: Most of patients had more than 10 years history of epilepsy. Also, Anxiety and depression were the most common symptoms in epileptic patients. It need to more study to determine the psychiatric disorders in epileptic patients.

Osteosarcopenic obesity (OSO) is described by the simultaneous presence of osteopenia/osteoporosis, sarcopenia, and increased adiposity. Over time, older adults with OSO syndrome might be at greater risk for loss of physical function and bone fractures. Furthermore, a sedentary lifestyle, inadequate nutrition, pharmaceutical drugs and chronic conditions encompass the multifactorial nature of OSO syndrome. Physical activity and a healthy diet play a crucial role in management and treatment of OSO syndrome. Research has shown that even low-intensity physical activity or daily habitual activity can maintain bone mineral density, muscle strength and improve muscle quality, and reduce adiposity. However, older adults with high risk of fall and injuries require tailored exercise intensity. Also, balanced daily intake of vitamin D, calcium and protein is important in prevention and treatment of OSO syndrome in postmenopausal women. Effective measurement of bone mass, muscle mass and strength is required when detecting OSO syndrome and to evaluate the balance, strength and endurance of elder individuals and severity of the condition.

Crowd accident surveys have shown that regardless of the initial triggering factors, pedestrian fall behaviour is the most critical factor causing and aggravating crowd accidents in public traffic areas (PTAs). Application of pedestrian fall behaviour detection methods in PTAs is significant. Once deployed, it would prevent many pedestrians from losing life in crowed traffic areas accidents. However, most existing methods is still focused on medical assistance for the elderly. Therefore, this paper conducted bibliometric and content analyses, combining fall detection-related keywords to retrieve from internationally recognized literature databases and benchmark pedestrian behaviour datasets. Based on the analysis of the state-of-the-art (SOTA) achievements in fall detection methods, the fall detection methods were classified into different categories according to the research approach. Among them, it undertakes a comprehensive analysis of five predominant methods, namely computer vision, Internet of Things, smartphone, kinematic, and wearable device-based methods. Furthermore, the benchmark datasets including fall scenarios were introduced and compared. Finally, it provides a detailed discussion of existing fall detection methods, and possible future directions are identified considering application requirements in PTAs. This overview may help researchers understand the SOTA fall detection methods and devise new methodologies by improving and synthesizing the highlighted issues in PTAs.

Introduction: Fractures in older individuals are often caused by falls, with approximately 90% of hip fractures resulting from falls. The risk of falling increases with age, and while a significant portion of individuals over 65 fall at least once per year, only a small percentage of these falls result in hip fractures. Factors that influence the likelihood of a fracture occurring include the intensity of the fall and the quality of the bone, with lower bone mineral content increasing the risk of fracture. Older women, particularly those in their 70s, are significantly more prone to hip fractures and any type of fracture. Efforts to reduce the likelihood of falls or mitigate the associated trauma are more complex than treating osteoporosis, due in part to a lack of understanding of the causes and contributing factors of falls in older age.Methods: This study analyzed data from patients admitted to Carmel medical center with upper or lower limb fractures between 2017 and 2019 to determine the side of wrist fractures and compare it to patient age in order to examine whether there was a difference in the distribution of sides in distal radius fractures based on age and test the hypothesis that falls are more likely to occur on the left side due to the assumption that dominant hand gross motor skills are better preserved. The study received approval from the Institutional Helsinki Committee and used statistical analysis with a significance level of 0.05. Potential sources of bias include limited availability of reliable data for many patients and the risk of errors in fracture registration or diagnosis, although the small sample size is expected to minimize these biases.Results: In this analysis of patient data from 2019 to 2017, a binomial test found that the probability of breaking the left wrist is significantly greater than the probability of breaking the right wrist (p < 0.05), while a t-test found no difference in the distribution of fractures between the right and left wrists of the distal radius based on age (p = 0.2774). Discussion: The findings of the study are consistent with previous research, and indicate that there is no change in side preference for fractures with age. Conclusion: The probability of breaking the left wrist is approximately 1.5 times greater than the probability of breaking the right wrist.

Falls among older adults pose significant public health challenges associated with severe physical, psychological, and economic consequences. Exercise interventions are crucial for reducing fall risk through diverse biomechanical, physiological, and psychological mechanisms. This integrative review synthesizes evidence from systematic reviews, meta-analyses, randomized controlled trials, and cohort studies published between 2004 and 2024, sourced from databases including PubMed, MEDLINE, EBSCO (EDS), and Google Scholar. Key findings highlight that balance and strength training enhances postural control, gait stability, and joint mobility while promoting neuromuscular coordination. Resistance training is particularly effective in mitigating sarcopenia and improving muscle strength and joint function, aiding quick responses to balance perturbations. Cognitive exercises enhance attention, spatial awareness, and decision-making, reinforcing fall prevention. Psychological benefits include reduced fear of falling, increased confidence, improved mood, support adherence and social engagement, and sustaining physical activity. Holistic models demonstrate the efficacy of integrating physical, cognitive, and social elements for comprehensive fall prevention. The review emphasizes the importance of multidisciplinary strategies and underscores the need for continued cross-disciplinary research to refine evidence-based practices that optimize outcomes for older adults, thereby highlighting the ongoing nature of the research and the potential for further advancements in the field.

The fall armyworm, Spodoptera frugiperda, arrived during the 2016/2017 cropping season in Limpopo, South Africa and it continues to cause devastating damage on maize in this region. The present study investigated knowledge, perception and management of S. frugiperda by smallholder farmers at Dzindi and Tshiombo irrigation schemes in this region. The study was conducted in 16 focus group discussion interviews, 12 at Dzindi and four at Tshiombo irrigation scheme. Although farmers failed to identify S. frugiperda eggs and pupal stages, they positively identified the pest by its larval stage and refer to it by several local names. All farmers relied extensively on insecticides for S. frugiperda control. With the exception of the Dzindi irrigation scheme farmers, who have adopted Bt maize, very few farmers have adopted alternative methods to chemical control such as mechanical control, physical control (crushing egg and handpicking of larvae), biological control, cultural practices (such as Push-Pull Technology), possible indigenous knowledge, or the use of monitoring tools, for the management of S. frugiperda. The study provided valuable information for designing sustainable management practices to control S. frugiperda on maize in smallholder farming communities.

Falls, especially those left unattended, are fatal for the elderly. Several efforts have been made to use the Internet of Things and Machine Learning algorithms to detect falls. All such systems have issues such as (a) sensor placement on the torso and thigh which will be uncomfortable for the elderly. (b) Predictions made on the cloud- the result of the prediction is then sent back to the end device to raise an alert. This is prone to network connectivity and latency issues. We have built an end/device that is wrist-worn and has multiple Inertial Measurement Unit sensors and a heart sensor. We have developed three novel ensemble algorithms (a)Stack(A) (b) Variable weighted Ensemble Voting algorithm-B(VWE(B)), and (c) Variable weighted Ensemble Voting algorithm-C (VWE(C)). Since the ensemble algorithm is run on the end-device built around Qualcomm Snapdragons 820c, we do both feature extraction and selection to reduce data dimensionality. We have used multiple methods such as (a) Identifying features that have maximum impact (b) Principal Component Analysis (PCA) (c) Shapley’s values (d) Cross-Correlation combined with relliefF. We got an accuracy of 97% and specificity of 99%. In this paper, we present the analysis of the system with and without pruning.

A via ferrata (from the German “klettersteig”, hereinafter VF) is a sports route located on forest and mountains vertical rock walls equipped with steps, chains, artificial dams, bridges and other fixed elements and which have a steel cable (safety cable) all the way along allowing users to secure their progress and avoid possible falls [1]. This article aims to analyse the state of the art of the VF sector in Spain, especially in terms of the regulations of obligatory compliance, in addition to defining the basic characteristics of the installations to ensure that these are safe for users, providing a previously non-existent summary of the most important recommendations regardless of the country where they are installed.

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.

Objective: This study aimed to explore potential interventions or preventive measures to reduce the incidence of falls among the elderly in the Madinah region, and to provide recommendations for healthcare professionals, policymakers, and community stakeholders to enhance fall prevention strategies and improve the overall well-being of the elderly population in this area. Methods: The study adopted a cross-sectional study design, and was carried out in the Madinah region using convenient physical assignment among of Saudi and non-Saudi local residents aged 65 years and over. Questionnaire surveys were circulated and demographic characteristics were collected, including participants’ fall experience and health status data over the past year, in order to assess the incidence of falls among the elderly population. This design allows for the collection of data at a single point in time, aiming to evaluate the prevalence, risk factors, and consequences of falls among the target population. Results: A total of 189 participants were recruited to the study, and the data indicated that the prevalence of falls among those aged 65 years and older in the past year was 41.5%. We found that 73% of the participants had experienced falls and post-fall injuries within the past 12 months. There were minimal differences in terms of physical handicapped cases found between those with and without a fall history. Logistic regression analysis revealed a significant link between fall experience and maximum bite force. Conclusion: There is a high prevalence of falls in the elderly population in the Madinah region. Our study reveals that 50.79% of our elderly participants had experienced more than one fall during the preceding year. Some preventable risk factors were identified in an analysis of their daily routines. The impacts of these risk factors have potential consequences for their healthy lives.

Spodoptera frugiperda is a relatively new invasive polyphagous insect pest in Indonesia. So far, S. frugiperda infestation has only been reported in corn, however in other countries S. frugiperda has been known to attack many commercial crops. To date, information on biological parameters of S. frugiperda is limited in Indonesian ecologies. Since host plants are a critical factor for insect life-history and has the potential to be used for pest control strategies, it is important to study the biology and survival of S. frugiperda on different host plants. This research was aimed to study the survival, life cycle, and fecundity of S. frugiperda on different host plants and how it affects pest management. The study was conducted by rearing S. frugiperda on 14 common cultivated host plant species in Indonesia. Survival rate, development time, fecundity, and potential attack rate of S. frugiperda on various tested host plants were analyzed in this study. The findings re-vealed that corn was the main host for S. frugiperda. The ability of S. frugiperda to survive on pa-paya, water spinach, banana, spinach, cucumber, and coco grass indicates that these plants are potential alternate host for S. frugiperda. Long beans, bok choy, choy sum, and beans might be in-dicated as a shelter for S. frugiperda. Meanwhile, inappropriate hosts for S. frugiperda include cabbage, broccoli, and cauliflower due to their low survival rate on these plants. This research implies that these plants have the potential to be used as a hedge, trap, or bunker plant in S. frugi-perda management strategies. However, to prevent a detrimental damage, control methods are needed in integrated manner, including monitoring pest populations, environmental engineering, and conservation of natural enemies.

Background:Peripheral neuropathy (PN) can result in either partial or complete loss of distal sensation resulting in an increased fall risk. Walkasins^® uses a shoe insert to detect the magnitude and direction of sway and sends signals to a leg unit that provides sensory balance cues. The objective of this case report is to describe the long-term influence of the Walkasins^® lower limb sensory neuroprosthesis on balance and gait for an individual with diabetic PN.Case Description:A fifty-one-year-old male with a 3-year history of PN and a 10-year history of type II diabetes mellitus was fitted with Walkasins^® and utilized the shoe inserts 8-10 hours/day for more than 1 year. Although, vibration and tactile thresholds were severely impaired at his 1^st metatarsophalangeal joint and the lateral malleolus bilaterally he could perceive tactile stimuli from the Walkasins^® above the ankles.Outcomes:Following Walkasins^® use, his Activities-specific Balance Confidence Scale (ABC) scores improved from 33% to 80%. His mean Vestibular Activities of Daily Living (VADL) scores decreased from 3.54 to 1. His Functional Gait Assessment (FGA) scores increased from 13/30 to 28/30 and his miniBESTest scores improved from 15/28 to 26/28. Gait speed increased from 0.23 m/sec to 1.5 m/sec. The patient described a decrease in pain and cramping throughout his lower extremities and an increase in function.Discussion:Gait and balance improved with the use of the Walkasins^® and participation in the Neuro Wellness Program. This improvement suggests that the use of sensory substitution devices, such as the Walkasins^®, may replace sensory deficits related to gait and balance dysfunction experienced by patients with PN. Further research is needed to determine if other patients will have a similar response and what the necessary threshold of sensory function is to benefit from use of the Walkasins^®.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

1) Background: Patients with neurological disorders are particularly at risk of falling and requiret functional rehabilitation. HUBER® is a motorised rotating platform that allows simultaneous work on balance, stability and strength with continuous adaptation and immediate feedback. This study aimed to measure the impact of rehabilitation with the HUBER® on the physical performance and quality of life of patients with neurological diseases requiring functional rehabilitation. 2) Methods: 32 subjects were enrolled in CHU Liège CNRF and randomly divided into 2 groups. The first group received classical rehabilitation combined with two training sessions per week on the HUBER® for 8 weeks. The second group received their usual treatment. This randomized control trial has been registered on clinicaltrials.gov under the number NCT04687293. Physical performance and quality of life were evaluated at different time. 3) Results: There was no significant difference between the 2 groups after enrolment. In terms of physical performance, an improvement in SPPB chair stand test (p=0.021) and total SPPB (p=0.003) was observed in the HUBER® group. In terms of quality of life, a higher improvement was observed overall in the HUBER® group (p&lt;0.05 between groups). 4) Conclusion: The HUBER® showed promising results in functional rehabilitation of patients with neurological pathologies, as a complement to classical rehabilitation management by physiotherapy.

Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in the aging population. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLens^TM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older, 12 young) participated in this study. An automated functional mobility assessment app was developed based on the HoloLens platform. Mobility performance was recorded with the headset built-in sensor and validated with reference inertial sensor (Opal, APDM) taped on the headset and lower back. Results indicate vertical kinematic measures by HoloLens was in good agreement with the reference sensor (Normalized RMSE ~ 10%). Additionally, the HoloLens-based test completion time was in perfect agreement with clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users to complete common mobility tests with good measurement accuracy, thus it has great potential to provide objective and efficient sensor-based mobility assessment.

Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, that prevent authors to evenly compare their new proposals. Here, we present a dataset of falls and activities of daily living (ADL) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADL and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADL and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96~\% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where algorithms could be focused on. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages to develop new strategies with this new dataset as benchmark.

Purpose: National Institute for Health and Care Excellence (NICE) has recently published quality standards for assessment of fall risk and preventing further falls. According to the standards, multifactorial fall risk assessments should include: identification of falls history; analysis of gait, balance, mobility and muscle strength, among other factors. Despite being based on subjective analysis or simple timing and not being multifactorial, physiotherapists and physicians quite often use these tests as reference scales to differentiate between lower and higher risk of falling. Instrumented TUG has been recently reported to provide important additional information to the overall score. Objective: To explore a case-based approach of fall risk assessment to identify the most relevant and informative risk factors that in combination could better define a person risk profile. Materials and Methods: A multifactorial assessment of fall risk through questionnaires, standard functional tests, tests instrumented with inertial sensors, and force platforms has been studied within a group aged 55-80 years old. Different fall risk factors and fall risk assessment methods were analyzed in a case-based descriptive study. Results & Discussion: Subjects at higher risk of falling were identified based on their detailed profiles. A set of features were obtained from the instrumented standard tests differing significantly between subjects presenting higher or lower fall risk. Therefore, instrumenting conventional tests with wearables containing inertial sensors and force platforms gives more detailed and quantitative insights. This information can be used to better define and tailor fall prevention exercises and to improve the follow-up of the evolution of the subject.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

A Fall armyworm (FAW) is a major pest of maize and causes huge losses. Chemical control is the commonly used strategy FAW among farmers. Efficacy of baculovirus against FAW has been proven, however, farmers may not afford the products. The use of farmer produced baculovirus mixtures could provide an opportunity for a nature-based solution for FAW at low cost. This study evaluated the potential of virus extracted from FAW larvae treated with a commercial baculovirus (Littovir) for the management of FAW under laboratory and field conditions. In Laboratory, the virus extracted from 25, 50, 75 and 100 FAW larvae caused varied mortality on FAW instars. The highest mortality (45%) on 1st-3rd instars was caused by Littovir followed by virus extract from 100 FAW larvae (36%). Under field conditions, even though virus extracts did not offer adequate protection against the FAW damage, the maize yield was comparable to commercial insecticides treated plots. This study has shown the potential of use of virus extracts for management of FAW. This would offer the farmers a sustainable and affordable option for management of FAW as it would require the farmers to purchase the commercial baculovirus once and collect larvae from treated plots for repeat applications.

During the last few years, several approaches have been proposed to improve early warning systems for reducing rock-fall risk. In this regard, this paper introduces a Deep learning-and (IoT) based Framework for Rock-fall Early Warning, devoted to reducing the rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused the prediction model-based deep learning with a detection model-based Internet of Things. In order to determine the framework’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and the ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, a framework reduced the risk probability from (1.51 ×10-3) to (8.57 ×10-9). Our results indicate the framework’s high prediction accuracy; it also provides a robust decision-making process for delivering early warning and lowering the rock-fall risk probability.

Spodoptera frugiperda is a new invasive and highly polyphagous pest that attacks corn in Indonesia. The availability of abundant plant species allows pests to switch to other host plants to maintain their population. This research aims to examine the development, reproduction, nutritional indices, and life table of S. frugiperda in several plant species. The plants tested were corn, rice, broccoli, oil palm, and baby corn as controls. Ten individual insects were used and repeated five times for each plant species. The test results show that different types of plant feed affect the development time, imago life span, fecundity, and fertility of S. frugiperda. The types of plant feed, that were baby corn fruit and broccoli had higher net reproduction value (R0), intrinsic growth rate (r), gross reproduction rate (GRR), shorter mean generation period (T), and population doubling time (DT) than in corn and rice leaves. On oil palm leaf feed no population parameters can be determined because no larvae developed into adults and had the lowest nutritional indices parameters, so that could not be exploited as a host plant. Also, the nutritional indices of several feed plant species tested provided information that broccoli could be a suitable host when there was no corn in the field.

Spodoptera frugiperda is an invasive pest that has spread in various parts of the world. These pests have the ability to spread and adapt highly to new habitats. Until now, it is not known with certainty the distribution of these invasive pests in Eastern Indonesia, especially Bali and Nusa Tenggara. This study aims to map the spatial distribution and genetic distribution of S. frugiperda which damages maize in the areas of Bali and Nusa Tenggara. This research was conducted using a survey method from May to September 2022 covering the islands of Bali, Lombok, Sumbawa, Sumba, Flores, and Timor. The results showed that S. frugiperda had spread evenly in Bali and Nusa Tenggara. The results of PCR amplification in the COI gene from 9 sample isolates from all research locations showed the similarity of DNA bands leading to the Spodoptera frugiperda species with a banding pattern length of 683 – 697. These results indicated that the distribution of genetic variants of corn caterpillars in Bali, NTB, and NTT was confirmed as S. frugiperda species. However, the isolated gene S. frugiferda, which was shown by the alignment results of the sequences from Lombok, was confirmed as a different strain from strains from Bali, Sumba, Sumbawa, Flores, and Timor. This incident can be seen from the difference in the protein base composition of S. frugiperda from Bali, Sumba, Sumbawa, Timor, and Flores. The results of phylogenetic analysis in this study confirmed 3 clusters of the genetic closeness of S. frugiperda. Cluster-1 comes from the results of the search for specimens of JB FAW and KB FAW from Bali, SB FAW and SB FAW Sorghum from Sumba, SW FAW from Sumbawa, KP FAW from Timor, and FL FAW from Flores. Cluster-2 is an isolate outside of our species. Cluster-3 comes from the search for LT and LS FAW specimens from Lombok. The genetic distance between cluster-1 and cluster-3 is quite far, which is 0.20 mu.

The EXO+ hip protector was developed to minimize the risk of hip fractures in seniors following falls. This study evaluated its usability in a Singapore nursing home from the perspective of staff, focusing on Learnability, Efficiency, and Satisfaction. A mixed-method design was employed, with nursing home residents wearing the EXO+ daily for 8 weeks. Quantitative data on wear time was collected alongside qualitative insights from four repeated focus group discussions with care staff. In total, 15 residents (including 8 residents with cognitive impairments) and 9 staff participated. The analysis revealed six themes and fourteen subthemes related to EXO+’s usability. Residents wore the device for an average of 1.54 hours per day, with those having cognitive impairments showing increased wear times, especially in the mornings and on weekdays. Findings indicated that the EXO+ was learnable and satisfactory for nursing home use, provided there are adequate supporting features. The efficiency of the EXO+ was not fully measured since no falls were reported during the study period. The study offers valuable insights for potential design modifications and workflow adaptations to enhance the implementation of hip protectors in healthcare settings, highlighting facilitators and barriers to support device uptake.

Several herbivores feed on maize in field and storage setups making the development of multiple-insect resistance a critical breeding target. In this study, an association mapping panel of 341 tropical maize lines was evaluated in three field environments for resistance to FAW (fall armyworm) whilst bulked grains were subjected to MW (maize weevil) bioassay, genotyped with Diversity Array Technologies single nucleotide polymorphisms (SNPs) markers. A multi-locus genome-wide association study (GWAS) revealed 62 quantitative trait nucleotides (QTNs) associated with FAW and MW resistance traits on all 10 maize chromosomes, of which, 47 and 31 were discovered at stringent Bonferroni genome-wide significance level of 0.05 and 0.01, respectively, and located within or close to multiple-insect resistance genomic regions (MIRGRs) concerning FAW, SB, and MW. Sixteen QTNs influenced multiple-traits of which six were associated with resistance to both FAW and MW suggesting a pleiotropic genetic control. Functional prioritization of candidate genes (CGs) located within 10-30kb of the QTNs revealed 64 putative GWAS-based CGs (GbCGs) showing evidence of involvement in plant defense mechanisms. Only one GbCG was associated with each of five of the six combined-resistance QTNs, thus, reinforcing the pleiotropy hypothesis. In addition, through In-silico co-functional network inferences, an additional 107 Network-based CGs (NbCGs), biologically connected to the 64 GbCGs, differentially expressed under biotic or abiotic stress were revealed within MIRGRs. The provided multiple-insect resistance physical map should contribute to the development of combined-insect resistance in maize.

The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people use to stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches were not tested with the target population, or are not feasible to be implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We test our approach with the SisFall dataset achieving 99.4% of accuracy. Then, we validate it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.

In this paper, a two-phase model of air shock wave induced by rock-fall was described. The model was made up of the uniform motion phase (velocity was close to 0 m·s^-1) and the acceleration movement phase. The uniform motion phase was determined by experience, meanwhile the acceleration movement phase was derived by the theoretical analysis. A series of experiments were performed to verify the two-phase model and obtained the law of the uniform motion phase. The acceleration movement phase was taking a larger portion when height of rock-fall was higher with the observations. Experimental results of different falling heights showed good agreements with theoretical analysis values. Computational fluid dynamics (CFD) numerical simulation had been carried out to study the variation velocity with different falling height. As a result of this, the two-phase model could accurately and convenient estimating the velocity of air shock wave induced by rock-fall. The two-phase model could provide a reference and basis for estimating the air shock waves' velocity and designing the protective measures.

The fall armyworm (FAW), Spodoptera frugiperda, a major pest in maize production, was assessed for its temporal and spatial distribution in maize fields during both the off and rainy seasons of 2021 and 2022. In this study, 30 and 50 maize plants were randomly sampled using a "W" pattern during the dry and rainy seasons, respectively. Larval density, infestation rates, and damage severity were monitored over time. Taylor's power law and the mean crowding aggregation index were applied to evaluate the dispersion patterns of the larvae. The results indicate a higher infestation rate and larval density in agroecological zone AZE 8 (fisheries zone of South Benin) compared to AZE 6 (ferralitic soils zone) during the off-season. In the rainy season, while the percentage of damaged plants was higher in AZE 8, no significant differences in larval density between the two zones were observed. The dispersion analysis revealed moderate aggregation (aggregation index = 1.25) with a basic colony of 2.08 larvae, reflecting the aggregation oviposition behavior of FAW. This study provides valuable monitoring data on the FAW's distribution, offering insights for further research on population dynamics and developing predictive models for integrated pest management strategies.

The distribution of zakat, which is still carried out directly through mosques or zakat amil bodies, can hinder the payment process due to inaccurate calculations and difficulties in finding zakat amil institutions. In the digital era, information technology is very important in the management of zakat, alms, and infaq. Therefore, a zakat information management system was created. The e-Zakat program, developed using the waterfall method, plays an important role in five main aspects of zakat management: muzaki and mustahiq data collection, zakat collection, zakat distribution, mustahiq development, and reporting. The use of information technology in this program is expected to increase efficiency, accountability, transparency, as well as the reach of zakat and muzaki involvement.

Splenic rupture and hematoma are significant complications that can arise in patients with non-Hodgkin lymphoma (NHL). Understanding these associations is essential for optimal patient management and patients' enhancing outcomes. Histopathological and immunohistochemistry analyses are crucial in diagnosing NHL and assessing splenic involvement. A judicial autopsy was requested by the Prosecutor's Office for a malpractice claim due to a fall in the hospital. In the Emergency Department, a 72-year-old man fell from the gurney, reporting a wound to his forehead. No other symptoms were reported. A face and brain CT scan showed no abnormalities. Nine days after discharge, the patient presented with abdominal pain. Abdominal CT revealed splenic rupture and hemoperitoneum. The patient underwent open splenectomy, but the patient showed signs of hemodynamic shock and subsequently died. The evidence emerging from the autopsy allowed us to diagnose a Mantle Cell non-Hodgkin lymphoma with spleen involvement, previously unknown. Histopathological and immunohistochemical analyses were performed to assess splenic rupture's diagnosis and time estimation. The findings strongly suggest that the splenic rupture was associated with the patient's fall and the pre-existing malignancy. This case highlights the importance of considering underlying hematological malignancies when investigating delayed splenic rupture. Immunohistochemical study of spleen samples allowed the assessment of the timing of splenic hematoma and rupture, leading to establishing a causal relationship with trauma.

An accurate, economical, and reliable device for detecting falls in persons ambulating with the assistance of an orthopedic walker is crucially important for the elderly and patients with limited mobility. Existing wearable devices such as wristbands are not designed for walker users, and patients may not wear them at all times. This research proposes a novel idea of attaching an internet-of-things (IoT) device with an inertial measurement unit (IMU) sensor directly to an orthopedic walker to perform real-time fall detection as well as activity logging. A dataset is collected and labeled for walker users in four activities, including idle, motion, step, and fall. Classic machine learning algorithms are evaluated using the dataset by comparing their classification performance. Deep learning with convolutional neural network (CNN) is also explored. Furthermore, the hardware prototype is designed by integrating a low-power microcontroller for onboard machine learning, an IMU sensor, a rechargeable battery, and Bluetooth wireless connectivity. The research results show the promise of improved safety and well-being of walker users.

A chronic activated pro-inflammatory cytokine network (“inflamm-aging”) may amplify the neurodegenerative effects of a fall induced brain trauma in geriatric subjects. Our research aimed to evaluate how a trained machine learning algorithm may predict recent antecedent falls based only on specific serologic cytokines network analysis and how the consequences of these falls can be substantiated on standard head MRIs. All 279 subjects included in our study were selected from the ADNI1 dataset and all had a mild cognitive impairment diagnostic at the ADNI1 study baseline. A “train group” was built and included 14 subjects with a history of a recent, simple, standing-level fall. These were carefully matched with 14 similar subjects without any antecedent trauma. The “test group” included 251 subjects, all without any history of recent fall. The machine learning algorithm (classic C4.5 decision tree) was trained to detect a pattern of variation in 23 clinically relevant cytokines in relation with an antecedent fall. Changes in five cytokines (matrix metalloproteinase-7, eotaxin-1, interleukin-3, interleukin-8 and matrix metalloproteinase-9) were used for fall prediction in the “test” group. Once trained, the algorithm predicted a recent fall in 119 cases from the test group. The mean brain ventricular volume that was significantly different between fall/non-fall subgroups (41645.5±10337.2 vs 27127.3±6749.4 mm3, p=0.005) remained significant in the test group, after prediction between (41544.24±17343.4 vs 34553.5±10543.2 mm3, p=0.042). The hippocampus mean volume was also significantly different between in the test group (6297.3±1080.1 vs 6745.9±1123.7, p=0.0015). A significant brain ventricular difference was observed in the “65<y.o.” subgroup (p=0.04). If confirmed by larger prospective studies, our findings may increase the precision of the neuro-cognitive assessments in geriatric subjects.

Chronic obstructive pulmonary disease (COPD) is increasingly being recognized as a systemic disease rather than a mere disorder of the lungs. Central (respiratory) and peripheral (limb) muscle weakness are among the main pronounced systemic effects of COPD. While the disease primarily affects the lower limb muscles and contributes to gait impairment, COPD is also associated with an increasing risk of falls in patients (COPDp). Previous studies have reported higher rates of falls among COPDp (1.17 to 1.20 falls/person-year), amounting to four times higher than an age-matched healthy group. Potential fall risk factors include muscle weakness, impaired daily activities, cognitive dysfunction, and gait and balance impairment. Although COPDp often manifest many of these risk factors, there remains a gap in literature regarding falls during walking in this population. This study aimed to 1. analyze the literature to identify the risk factors of falling in COPDp, and 2. investigate the underlying mechanisms by which these risk factors can lead to increased prevalence of falling. The results suggest that in addition to the known risk factors of falling, low back pain and mental fatigue should also be considered as relevant risk factors in the treatment process of these patients. Moreover, respiratory problems, which are common in this population, have demonstrated pronounced effects on energy expenditure, gait, and other types of activities of daily living (ADLs), leading to reduced intensity, disrupted coordination of the trunk-pelvic structure with the lower limbs during gait, and altered motor control performance due to activation of muscles in an inefficient synergic manner. These problems potentially lead to the increased vulnerability of these patients to external disturbances and higher incidence risk of falls and injuries. Cognitive problems, which are typically due to reduced oxygen received by the brain, as well as general inflammation caused by COPD, also play a significant role in gait disruption and balance. Future research is warranted to determine the prevalence of falls in COPDp by examining the response of these patients to Medio-Lateral (ML) and Anterior-Posterior (AP) disturbances during gait in association with traditional and recommended fall risk factors.

Postural instability (PI) in Parkinson’s disease (PD) exposes patients to increased risk of falls (RF). Although dopaminergic therapy and deep brain stimulation (DBS) improve motor performance in advanced PD patients, their effects on PI and RF are not clear. PI and RF were assessed by a stabilometric platform in 6 advanced PD patients. Patients were evaluated on and off dopaminergic medication and four DBS conditions: bilateral-DBS, DBS of the more- or less-affected side and DBS-off. Dopaminergic medication alone worsened PI and RF, DBS alone worsened RF, and no medication/DBS combination produced an improvement in postural control with respect to the OFF-medication/DBS-off condition. When ON-medication, PI and RF significantly improved after turning the DBS on, regardless of medication condition. Among DBS conditions, bilateral-DBS provided the maximal improvement of PI and RF when ON medication, and minimal worsening of PI and RF when OFF medication, whereas patients performed worse when in most-affected side DBS condition. These results can help in developing the best therapeutic strategy for postural disorders in patients with advanced PD.

Older adults in low-and middle-income countries experience a disproportionate burden of non-communicable diseases (NCDs). Unintentional injuries are among the major NCDs, and falls are the second leading cause of these injuries and deaths worldwide, including in Thailand. We aimed to culturally adapt the CDC’s Stopping Elderly Accidents, Deaths, and Injuries (STEADI) for Thai older adults and explore the feasibility, appropriateness, and acceptability of using STEADI in primary care via trained community health workers (CHWs) and care managers (CMs). STEADI takes a coordinated care approach that consists of three steps: screening, assessing, and intervening. In Step a, CHWs screened fall risk in 20 community-dwelling older adults using three key questions and found that all of them had fall risk, then CHWs screened with a Stay Independent questionnaire (range 0-14) and found that 100% have high fall risk (total scores 9.7± 2.4). In Step b, CMs assessed balance, vision, footwear, postural hypotension, medications, and CHWs assessed home hazards. They found that 50% had poor balance, 70% took 4+ medications,75% fell on the walkway, and 70% had no bathroom modifications. In Step c, individual participants received fall prevention interventions to mitigate their specific fall risk factors. CHWs and CMs indicated high acceptability (19.20±.1.31 of 20 total), appropriateness (18.80± 1.79 of 20 total), and feasibility (18.60±1.67 of 20 total) of the Thai-STEADI intervention. Our study showed that the community-based multifactorial Thai-STEADI delivered by CHWs and CMs is feasible and acceptable to prevent falls in older adults with limited access to health care.

The measurement of the grounding resistance of protection grids in large installations as well as grounding electrodes in urban areas is addressed in this article. The resistance value is obtained using a three-pin array by measuring the fall-of-potential on the ground surface. The resistance measured by this method is corrected using the so-called correction factor that adjusts the measured resistance value to its real value. The proposed measurement method obtains correct values of the grounding resistance even when the auxiliary and potential electrodes of the tree-pin array are close to the electrode to be measured. Thus, it can be applied to large electrodes as well as electrodes in urban areas. Several simulated examples are used to illustrate the method, and some real cases with field measurements are presented for a final validation of the method.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADL.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as – the lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

In 2004, first absolute gravity (AG) measurements were performed on the mountain tops of Mt. Zugspitze (2 sites) and Mt. Wank (1 site), and at the Wank foot (1 site). Wank (summit height 1780 m) and Zugspitze (2960 m) are about 20 km apart from each other and belong geologically to different parts of the Northern Limestone Alps. Bridging a time span of 15 years, the deduced gravity variations for Zugspitze are in the order of 0.30 μm/s² with a standard uncertainty of 0.04 μm/s². The Wank stations (foot and top) show no significant gravity variation. The vertical stability of Wank summit is also confirmed by results of continuous GNSS recordings. Because an Alpine mountain uplift of 1 or 2 mm/yr cannot explain the obtained gravity decline at Zugspitze, the dominating geophysical contributions are assumed to be due to the diminishing glaciers in the vicinity. The modelled gravity trend caused by glacier retreat between epochs 1999 and 2018 amounts to -0.012 μm/s²/yr at both Zugspitze AG sites. This explains more than half of the observed gravity decrease. Long-term variations on inter-annual and climate-relevant decadal scale will be investigated in the future using as a supplement superconducting gravimetry (installed in 2019) and GNSS equipment (since 2018).