Relevant bibliographies by topics / Fall

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fall'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Fall"

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Cave, Rachel. "When whale fall falls." New Scientist 199, no. 2668 (2008): 21. http://dx.doi.org/10.1016/s0262-4079(08)61989-2.

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KALYANAKRISHNAN, SHIVARAM, and AMBARISH GOSWAMI. "LEARNING TO PREDICT HUMANOID FALL." International Journal of Humanoid Robotics 08, no. 02 (2011): 245–73. http://dx.doi.org/10.1142/s0219843611002496.

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Falls are undesirable in humanoid robots, but also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of fall prediction: to predict if the balance controller of a robot can prevent a fall from the robot's current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. It is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and exhibits complex dynamics. We contribute a novel approach to engineer fall data such that existing supervised learning methods can be exploited to achieve reliable prediction. Our method provides parameters to control the tradeoff between the false positive rate and the lead time. Several combinations of parameters yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned solutions are decision lists with typical depths of 5–10, in a 16-dimensional feature space. Experiments are carried out in simulation on an ASIMO-like robot.
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Wright, Stephen. "One falls, we all fall." Nursing Standard 24, no. 47 (2010): 26–27. http://dx.doi.org/10.7748/ns.24.47.26.s31.

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Stückemann, Wolfgang. "Editorial. Vom Fall zur Falle." Fachanwalt Arbeitsrecht 22, no. 11 (2018): 353. http://dx.doi.org/10.1515/fa-2018-221101.

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Davis, Kathy. "Trips, falls and fall injuries." Primary Health Care 24, no. 7 (2014): 16. http://dx.doi.org/10.7748/phc.24.7.16.s24.

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Zhang, Duo, Xusheng Zhang, Shengjie Li, et al. "LT-Fall." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (2022): 1–24. http://dx.doi.org/10.1145/3580835.

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Falls are the leading cause of fatal injuries to elders in modern society, which has motivated researchers to propose various fall detection technologies. We observe that most of the existing fall detection solutions are diverging from the purpose of fall detection: timely alarming the family members, medical staff or first responders to save the life of the human with severe injury caused by fall. Instead, they focus on detecting the behavior of human falls, which does not necessarily mean a human is in real danger. The real critical situation is when a human cannot get up without assistance and is thus lying on the ground after the fall because of losing consciousness or becoming incapacitated due to severe injury. In this paper, we define a life-threatening fall as a behavior that involves a falling down followed by a long-lie of humans on the ground, and for the first time point out that a fall detection system should focus on detecting life-threatening falls instead of detecting any random falls. Accordingly, we design and implement LT-Fall, a mmWave-based life-threatening fall detection and alarming system. LT-Fall detects and reports both fall and fall-like behaviors in the first stage and then identifies life-threatening falls by continuously monitoring the human status after fall in the second stage. We propose a joint spatio-temporal localization technique to detect and locate the micro-motions of the human, which solves the challenge of mmWave's insufficient spatial resolution when the human is static, i.e., lying on the ground. Extensive evaluation on 15 volunteers demonstrates that compared to the state-of-the-art work (92% precision and 94% recall), LT-Fall achieves zero false alarms as well as a precision of 100% and a recall of 98.8%.
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Kalyanakrishnan, Shivaram, and Ambarish Goswami. "Predicting Falls of a Humanoid Robot through Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (2010): 1793–98. http://dx.doi.org/10.1609/aaai.v24i2.18815.

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Although falls are undesirable in humanoid robots, they are also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of fall prediction, i.e., to predict if a robot's balance controller can prevent a fall from the current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. Hence, it is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and therefore exhibits complex dynamics. Today effective supervised learning tools are available for finding patterns in high-dimensional data. Our paper contributes a novel approach to engineer fall data such that a supervised learning method can be exploited to achieve reliable prediction. Specifically, we introduce parameters to control the tradeoff between the false positive rate and lead time. Several parameter combinations yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned predictors are decision lists with typical depths of 5-10, in a 16-dimensional feature space. Experiments are carried out in simulation on an Asimo-like robot.
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Habib Perez, Olinda D., Samantha Martin, Katherine Chan, Hardeep Singh, Karen K. Yoshida, and Kristin E. Musselman. "A qualitative photo-elicitation study exploring the impact of falls and fall risk on individuals with subacute spinal cord injury." PLOS ONE 17, no. 6 (2022): e0269660. http://dx.doi.org/10.1371/journal.pone.0269660.

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Background Individuals living with chronic spinal cord injury or disease (SCI/D) are at an increased risk of falling. However, little is known about the impact of falls and fall risk in the subacute phase of SCI/D, despite this being a time when fall prevention initiatives are delivered. Hence, we explored the impact of falls and fall risk in individuals with subacute SCI/D as they transitioned from inpatient rehabilitation to community living. Methods This qualitative photo-elicitation study used an inductive thematic analysis. Eight individuals (7 male) undergoing inpatient rehabilitation at a Canadian tertiary rehabilitation hospital due to a new SCI/D participated. Six months following discharge, photo-elicitation interviewing was used to understand the impact of falls and fall risk. Over 7–14 days, participants completed a photo-assignment that involved taking photographs in response to questions, such as what increases/decreases your likelihood of falling? A semi-structured interview followed, in which participants described their photographs and discussed their experiences with falls, fall risk and fall prevention training. Results Four themes were identified. 1) Risk factors and strategies identified through lived experience. Participants discovered their fall risk factors and fall prevention strategies through “trial and error”. 2) Influences on the individual’s perception of their fall risk. Prior experience with falls, including falls experienced by themselves as well as friends and family, influenced their perception of fall risk. 3) Experiencing life differently due to increased fall risk. A high fall risk reduced participation, increased negative emotions and decreased independence and quality of life. 4) Falls training in rehabilitation can be improved. Prior experiences with falls training varied; however, participants expressed a desire for comprehensive and individualized training. Conclusion Although participants’ experiences with falls and fall prevention varied, falls and the risk of falling can have a significant impact on the first year of living with a SCI/D.
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Dinh, Anh, Yang Shi, Daniel Teng, et al. "A Fall and Near-Fall Assessment and Evaluation System." Open Biomedical Engineering Journal 3, no. 1 (2009): 1–7. http://dx.doi.org/10.2174/1874120700903010001.

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The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection.
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Fahimnia, Somayeh, Hadi Mirhedayati Roudsari, John Doucette, and Armin Shahrokni. "Falls in Older Patients with Cancer Undergoing Surgery: Prevalence and Association with Geriatric Syndromes and Levels of Disability Assessed in Preoperative Evaluation." Current Gerontology and Geriatrics Research 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/5713285.

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Falls are common among older adults. However, not much is known about the prevalence of falls among older patients with cancer. In 2015, older patients with cancer referred to Geriatrics service for preoperative evaluation were assessed for fall history, basic and instrumental activities of daily living (ADL and IADL), KPS, and use of assistive device. Of 806 patients, 215 (26.7%) patients reported fall. Incidence of last fall inside and outside home was 54.4% and 45.5%, respectively. Among patients with no falls, 33.6% had KPS ≤ 80 compared to 59.6% with one-time fall and 60.7% with multiple falls (p<0.001). Among IADL, 8.5% of patients with no falls were unable to do shopping compared with 14.7% in one-time fall and 18.8% in multiple fallers (p<0.001). In ADL items, the percentage of patients who were limited a lot in walking outside was 10.7% in no falls, 20.2% in one-time fall, and 27.1% in multiple fallers groups (p<0.001). Only 17.8% of patients with no falls were using canes while 27.7% of patients with one-time fall and 38.8% with multiple falls were using canes (p<0.001). Falls are prevalent among older patients with cancer. Fall history and number of falls are associated with functional status.
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Dissertations / Theses on the topic "Fall"

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Campbell, Baili Denise. "Fall Safety Bundle." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2797.

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The Centers for Medicare and Medicaid Services (CMS) report thousands of falls in hospitals each year. The CMS does not reimburse hospitals for fall related injuries, costing the hospital system organization for which this DNP project was designed millions of dollars each year. Framed within the Iowa model of evidence-based practice and using a team approach, the purpose of this project was to develop an evidence-based (EB) fall safety bundle for use by nursing staff and a curriculum to educate staff on prevention strategies. The components of the EB fall bundle kit were approved by the stakeholder committee. Evaluation of the curriculum and the pretest/posttest items was completed by three content experts. The curriculum was evaluated related to the objectives using a 'met' (2) and a 'not met' (1) response. All responses were 'met' for an average score of 2 showing the content met the objectives. Validation of the pretest/post items was conducted using a 10-item, Likert scale, ranging from 1- 'is not relevant' to 4- 'is highly relevant'. The content validation index was 1.0, showing that the test items met the objectives and content of the course. Recommendations included providing a consistent methodology to disseminate the fall safety bundle and educational curriculum across the entire healthcare system as well as adding the fall safety bundle tool kit to the hospital's intranet page for ease of access for all staff. Social change will be achieved by facilitating prevention of fall related injuries and avoiding the financial impact on the facility.
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Belcher, Janet Maxine. "Quality Initiative to Reduce Falls in an Acute Care Setting." ScholarWorks, 2020. https://scholarworks.waldenu.edu/dissertations/7599.

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Falls are the most frequently reported incidents among hospitalized patients in the United States with at least 4 falls per 1,000 patient days occurring annually. Falls are related to high rates of mortality and morbidity and high hospital costs. The purpose of this project was to evaluate a fall prevention quality initiative to reduce falls in an acute care facility by educating staff on an evidence-based fall prevention protocol. The project sought to explore whether implementation of an evidence-based fall prevention initiative in educating nurses would affect the nurses’ professional knowledge and the number of patient fall incidences in the cardiac care unit. The theoretical framework supporting this project was Neuman’s systems theory. The Iowa model was used to guide this evidence-based project. An educational session was implemented to increase nurses’ awareness of fall prevention practices. Two sets of data were collected: the pretest and posttest results, and the number of falls on the unit. A total of 21 unit nurses participated in the pretest; 18 (86.0%) completed the posttest. The mean score on the pretest was 81.62%; the mean score was 85.89% for the posttest with a mean difference of 4.27%. A paired sample t-test revealed no statistically significant differences in scores after education. This project has implications for social change by supporting patient safety, decreased hospital stays, and reduced health care expenses to patients and health care organizations.
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Delotelle, Amanda J. "Fall." Virtual Press, 2005. http://liblink.bsu.edu/uhtbin/catkey/1327291.

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The creative project involved writing and producing a short movie and an accompanying paper that described all facets of the scriptwriting and movie-making process. The movie, entitled "Fall," focuses on date rape and lying. The movie was based on research into date rape and Posttraumatic Stress Disorder. The movie chronicles a lonely young woman who has problems fitting in at her University. She can't seem to make friends and she feels inadequate among the other students. She wants attention and she gets it when she lies to a friend about being raped. As word of her "rape" spreads, people come together to console her. However, the lie eventually overwhelms her.<br>Department of Telecommunications
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Vonderhaar-Picard, Vanessa. "Identification of High Fall Risk Patients in Acute Rehab." Mount St. Joseph University Dept. of Nursing / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=msjdn1557840767904783.

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Tuvemo, Johnson Susanna. "Falls and fall prevention in community-dwelling older adults." Doctoral thesis, Uppsala universitet, Institutionen för neurovetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-333507.

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Falls are the primary cause of injuries among older adults, and accidents that result from falls can lead to personal suffering and extensive societal burdens. The overall aims of this thesis were to explore and describe falls and fall prevention strategies in community-dwelling older adults and to evaluate a fall prevention home exercise program, the Otago Exercise Program (OEP), with or without motivational interviewing (MI). Methods: Qualitative and quantitative research methods were uses. The designs were as follows: a cross-sectional, descriptive and comparative study (study I); a descriptive feasibility investigation (study II) and a randomized controlled trial (RCT) with two interventions, the OEP and OEP+MI, as well as a control group, with a 12-month follow-up (study III); and a prospective and descriptive study (study IV). The four studies comprised community-dwelling individuals aged 75 years or older. Study I included 262 individuals and studies II-IV had 175 participants who needed walking aids or home support. Study II also included 12 physical therapists.  Data collection was performed via self-reported questionnaires, fall calendars, exercise diaries, physical performance tests and a semi-structured questionnaire.  Results and conclusions: Suggested actions to prevent falls significantly differed between high and low active older adults (study I). Support for self-directed behavioral strategies could be important for preventing falls in older adults who have low physical activity levels. The study protocol for the RCT had acceptable feasibility (study II), and only minor changes of the protocol were needed. There were no benefits for OEP or OEP+MI with personal support implemented nine times over the 12-month period. However, all groups maintained physical functioning and activity (study III). To increase physical functioning and reduce falls in this sub-group of older adults, more frequent personal support and/or an alternative delivery format may be required for efficient intensity and challenge in home exercises. Over 12 months, falls and fall-related injuries in the RCT sample were the most common when moving around within the home and transitioning from sitting to standing (study IV). Special attention to these activities might be important for preventing falls in community-dwelling older adults who need walking aids or home support.
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SUBBURAMAN, RAJESH. "Fall Prediction and Controlled Fall for Humanoid Robots." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/939791.

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Humanoids which resemble humans in their body structure and degrees of freedom are anticipated to work like them within infrastructures and environments constructed for humans. In such scenarios, even humans who have exceptional manipulation, balancing, and locomotion skills are vulnerable to fall, humanoids being their approximate imitators are no exception to this. Furthermore, their high center of gravity position in relation to their small support polygon makes them more prone to fall, unlike other robots such as quadrupeds. The consequences of these falls are so devastating that it can instantly annihilate both the robot and its surroundings. This has become one of the major stumbling blocks which humanoids have to overcome to operate in real environments. As a result, in this thesis, we have strived to address the imminent fall over of humanoids by developing different control techniques. The fall over problem as such can be divided into three subissues: fall prediction, controlled fall, and its recovery. In the presented work, the first two issues have been addressed, and they are presented in three parts. First, we define what is fall over for humanoids, different sources for it to happen, the effect fall over has both on the robot and to its surroundings, and how to deal with them. Following which, we give a brief introduction to the overall system which includes both the hardware and software components which have been used throughout the work for varied purposes. Second, the first sub-issue is addressed by proposing a generic method to predict the falling over of humanoid robots in a reliable, robust, and agile manner across various terrains, and also amidst arbitrary disturbances. The aforementioned characteristics are strived to attain by proposing a prediction principle inspired by the human balance sensory systems. Accordingly, the fusion of multiple sensors such as inertial measurement unit and gyroscope (IMU), foot pressure sensor (FPS), joint encoders, and stereo vision sensor, which are equivalent to the human’s vestibular, proprioception, and vision systems are considered. We first define a set of feature-based fall indicator variables (FIVs) from the different sensors, and the thresholds for those FIVs are extracted analytically for four major disturbance scenarios. Further, an online threshold interpolation technique and an impulse adaptive counter limit are proposed to manage more generic disturbances. For the generalized prediction process, both the instantaneous and cumulative sum of each FIVs are normalized, and a suitable value is set as the critical limit to predict the fall over. To determine the best combination and the usefulness of multiple sensors, the prediction performance is evaluated on four different types of terrains, in three unique combinations: first, each feature individually with their respective FIVs; second, an intuitive performance based (PF); and finally, Kalman filter based (KF) techniques, which involve the usage of multiple features. For PF and KF techniques, prediction performance evaluations are carried out with and without adding noise. Overall, it is reported that KF performs better than PF and individual sensor features under different conditions. Also, the method’s ability to predict fall overs during the robot’s simple dynamic motion is also tested and verified through simulations. Experimental verification of the proposed prediction method on flat and uneven terrains was carried out with the WALK-MAN humanoid robot. Finally, in reference to the second sub-issue, i.e., the controlled fall, we propose two novel fall control techniques based on energy concepts, which can be applied online to mitigate the impact forces incurred during the falling over of humanoids. Both the techniques are inspired by the break-fall motions, in particular, Ukemi motion practiced by martial arts people. The first technique reduces the total energy using a nonlinear control tool, called energy shaping (ES) and further distributes the reduced energy over multiple contacts by means of energy distribution polygons (EDP). We also include an effective orientation control to safeguard the end-effectors in the event of ground impacts. The performance of the proposed method is numerically evaluated by dynamic simulations under the sudden falling over scenario of the humanoid robot for both lateral and sagittal falls. The effectiveness of the proposed ES and EDP concepts are verified by diverse comparative simulations regarding total energy, distribution, and impact forces. Following the first technique, we proposed another controller to generate an online rolling over motion based on the hypothesis that multi-contact motions can reduce the impact forces even further. To generate efficient rolling motion, critical parameters are defined by the insights drawn from a study on rolling, which are contact positions and attack angles. In addition, energy-injection velocity is proposed as an auxiliary rolling parameter to ensure sequential multiple contacts in rolling. An online rolling controller is synthesized to compute the optimal values of the rolling parameters. The first two parameters are to construct a polyhedron, by selecting suitable contacts around the humanoid’s body. This polyhedron distributes the energy gradually across multiple contacts, thus called energy distribution polyhedron. The last parameter is to inject some additional energy into the system during the fall, to overcome energy drought and tip over successive contacts. The proposed controller, incorporated with energy injection, minimization, and distribution techniques result in a rolling like motion and significantly reduces the impact forces, and it is verified in numerical experiments with a segmented planar robot and a full humanoid model.
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Hall, Courtney D. "Fall Risk." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/560.

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Bidgood, Lee. "Fall Celebration." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/3253.

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Alnes, Jacqueline. "Fall Risk." PDXScholar, 2015. https://pdxscholar.library.pdx.edu/open_access_etds/2310.

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This work of nonfiction examines the ways in which an unexpected and devastating health condition at the age of eighteen influences a person's identity and perception of self. The narrative is based largely around running, and the narrator explores what it means to live in a body that might fail.
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Martin, Rosemary. "The Impact of Engagement Strategies on the Reduction of Patient Falls." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4462.

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Despite the availability of many fall prevention measures, many patients fall in U.S. hospitals each year. Experts view patient fall rates as the measure that can be most affected by a nurse-led, evidence-based intervention. The purpose of this quality improvement project was to implement and evaluate the impact of patient engagement strategies on patient compliance to fall prevention education and the reduction of falls. The quality improvement framework used for this project was the Iowa Model. Interventions for this project included patient engagement strategies including the teach-back (TB) method and video-based fall prevention education paired with the project site's existing fall prevention program. A prospective quantitative design was used to answer the practice-focused question of whether the implementation of a falls protocol incorporating patient engagement strategies improves patient compliance with the fall prevention plan of care and reduces patient falls. A total of 58 patients were included in this project, conducted from July to October 2017. The results showed a 75% reduction in the fall rate compared to the same three month period in 2016. This finding suggests that reinforcement of oral and written instruction through video education follow-up and the use of the TB method to assess patient understanding are effective measures to reduce patient falls and increase patient compliance to the fall prevention plan of care. These patient engagement strategies can be replicated by nurses in similar acute care settings. Adoption of such evidence-based changes in nursing practice may improve patient safety and decrease harm in hospital settings as implications for positive social change.
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Books on the topic "Fall"

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ill, Halpern Shari, ed. Fall leaves fall! Scholastic Press, 2000.

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Pillau, Helmut. Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4.

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Roca, Núria. Fall. Barron's Educational Series, 2004.

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Amoroso, Cynthia. Fall. Child's World, 2010.

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McAdam, Colin. Fall. Hamish Hamilton Canada, 2009.

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Busby, Ailie. Fall. Child's Play-International, 2015.

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Kalkman, Lora. Fall. Publications International, 2000.

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Amoroso, Cynthia. Fall. Child's World, 2010.

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Demarest, Chris L. Fall. Harcourt, 1996.

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Rius, María. Fall. Barron's Educational Series, 1998.

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Book chapters on the topic "Fall"

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Bartl, Reiner, and Christoph Bartl. "Falls and Fall Prevention." In Bone Disorders. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29182-6_38.

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Bartl, Reiner, and Christoph Bartl. "Falls and Fall Prevention." In The Osteoporosis Manual. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00731-7_33.

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Pillau, Helmut. "Abschiebehaft." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_1.

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Pillau, Helmut. "Das gute Referat." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_10.

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Pillau, Helmut. "Zieleinlauf." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_100.

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Pillau, Helmut. "Das Rad wird immer wieder neu erfunden." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_11.

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Pillau, Helmut. "„Dear Doosie“." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_12.

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Pillau, Helmut. "Dem Teufel die Seele verkaufen." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_13.

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Pillau, Helmut. "Dereismus." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_14.

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Pillau, Helmut. "Der Hofnarr." In Von Fall zu Fall… Steinkopff, 2004. http://dx.doi.org/10.1007/978-3-662-13380-4_15.

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Conference papers on the topic "Fall"

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Cartocci, Nicholas, Antonios E. Gkikakis, Fabio Pera, Maria Teresa Settino, Darwin G. Caldwell, and Jesús Ortiz. "Fall-KAN: Fall impact time estimation Kolmogorov-Arnold Network." In 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2024. https://doi.org/10.1109/iceccme62383.2024.10796549.

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Toche, James, and Tyler Boulom. "Enhancing Fall Detection Accuracy in Diverse Home Environments using an Adafruit Microcontroller-Based Wearable Device." In 10th International Conference on Computer Science, Engineering and Information Technology. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141906.

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The problem addressed in this project is the variability in fall detection accuracy due to different room layouts in homes. Traditional fall detection systems often fail to account for diverse environments, leading to false positives or missed detections. To solve this, the project integrates an Adafruit microcontroller with an accelerometer to monitor movement and detect falls, using a threshold-based algorithm for accuracy. The key components include the microcontroller, battery, and casing, which allow for wearable, real-time monitoring. Challenges included ensuring consistent performance across various room setups. These were addressed through controlled experiments simulating different living room and kitchen layouts. Results demonstrated that the device could accurately detect falls in various environments, with minimal false positives. This solution offers a reliable and adaptable fall detection system, making it highly useful for elderly individuals or those at risk of falls, ultimately enhancing safety in diverse home environments.
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Ozkan, Birce. "Fall." In the 2015 ACM International Joint Conference. ACM Press, 2015. http://dx.doi.org/10.1145/2800835.2801675.

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Jee, Youngju, Eunjoo Lee, Eunsi Jang, Hyunhwa Hong, and Kyoungran An. "Relations among Fall Efficacy, Fear of fall, Fall Knowledge, and Fall Prevention Behavior in the Elderly." In 10th International Workshop Series Convergence Works. Global Vision School Publication, 2016. http://dx.doi.org/10.21742/asehl.2016.9.11.

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Forrest, K. Y. Z., and J. M. Cali. "Associated human factors for falls and fall-related injuries." In SAFE 2009. WIT Press, 2009. http://dx.doi.org/10.2495/safe090341.

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Weihao Qu, Feng Lin, Aosen Wang, and Wenyao Xu. "Evaluation of a low-complexity fall detection algorithm on wearable sensor towards falls and fall-alike activities." In 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2015. http://dx.doi.org/10.1109/spmb.2015.7405427.

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in Older Persons, Risk. "Influence of Footwear on Fall." In Applied Human Factors and Ergonomics Conference. AHFE International, 2020. http://dx.doi.org/10.54941/ahfe100348.

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Abstract:
It has been shown that several gait parameters are predictive for fall risk in older persons. There is evidence that footwear can influence foot position and gait patterns, especially in older persons. We carried out experiments to study the effects of type of footwear on gait patterns in persons aged 60+. We found an association between the type of footwear and certain gait parameters that are predictive for falls. In conclusion footwear can increase fall risk. Footwear for older people should be designed in such a way that risk of falls is minimized.
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"VTC2005-Fall." In VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005. IEEE, 2005. http://dx.doi.org/10.1109/vetecf.2005.1558158.

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Chakraborty, Tusher, Akshay Uttama Nambi, Ranveer Chandra, et al. "Fall-curve." In SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems. ACM, 2018. http://dx.doi.org/10.1145/3274783.3274853.

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Hardie, Peter. "UpDown Fall." In ACM SIGGRAPH 2006 Art gallery. ACM Press, 2006. http://dx.doi.org/10.1145/1178977.1179024.

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Reports on the topic "Fall"

1

Alnes, Jacqueline. Fall Risk. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2307.

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Reinhart, Carmen, and Vincent Reinhart. After the Fall. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w16334.

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Thomas, James. Fall Protection Introduction. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2372649.

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Farnell, Michael. Fall SULI Report. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2481549.

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Chochoms, Michael. Fall Protection Introduction, #33462. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1259639.

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Bentley, Jennifer A., and Mariah Schmitt. 2017 Fall Dairy Tour. Iowa State University, 2018. http://dx.doi.org/10.31274/ans_air-180814-393.

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Remedes, Tyler Joseph. Fall 2019 Dissertation Update. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1570611.

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Remedes, Tyler Joseph, Scott D. Ramsey, Joseph H. Schmidt, and James Baciak. Fall 2019 Dissertation Update. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1574744.

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Kippen, Karen. Physics Flash Fall 2020. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1679990.

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Kippen, Karen. Physics Flash Fall 2023. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2007336.

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