Relevant bibliographies by topics / Pre-impact fall detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Pre-impact fall detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Pre-impact fall detection"

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Nyan, M. N., Francis E. H. Tay, and E. Murugasu. "A wearable system for pre-impact fall detection." Journal of Biomechanics 41, no. 16 (2008): 3475–81. http://dx.doi.org/10.1016/j.jbiomech.2008.08.009.

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Li, Hui Qi, Ding Liang, Yun Kun Ning, Qi Zhang, and Guo Ru Zhao. "Design and Realization of an Early Pre-Impact Fall Alarm System Based on MEMS Inertial Sensing Units." Applied Mechanics and Materials 461 (November 2013): 659–66. http://dx.doi.org/10.4028/www.scientific.net/amm.461.659.

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Falls are the second leading cause of unintentional injury deaths worldwide, so how to prevent falls has become a safety and security problem for elderly people. At present, because the sensing modules of most fall alarm devices generally only integrate the single 3-axis accelerometer, so the measured accuracy of sensing signals is limited. It results in that these devices can only achieve the alarm of post-fall detection but not the early pre-impact fall recognition in real fall applications. Therefore, this paper aimed to develop an early pre-impact fall alarm system based on high-precision inertial sensing units. A multi-modality sensing module embedded fall detection algorithm was developed for early pre-impact fall detection. The module included a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer, which could arouse the information of early pre-impact fall warning by a buzzer and a vibrator. Total 81 times fall experiments from 9 healthy subjects were conducted in simulated fall conditions. By combination of the early warning threshold algorithm, the result shows that the detection sensitivity can achieve 98.61% with a specificity of 98.61%, and the average pre-impact lead time is 300ms. In the future, GPS, GSM electronic modules and wearable protected airbag will be embedded in the system, which will enhance the real-time fall protection and timely immediate aid immensely for the elderly people.
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Lee, Jung Keun, Stephen N. Robinovitch, and Edward J. Park. "Inertial Sensing-Based Pre-Impact Detection of Falls Involving Near-Fall Scenarios." IEEE Transactions on Neural Systems and Rehabilitation Engineering 23, no. 2 (2015): 258–66. http://dx.doi.org/10.1109/tnsre.2014.2357806.

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Lee, Jung Keun. "Study on Vertical Velocity-Based Pre-Impact Fall Detection." Journal of Sensor Science and Technology 23, no. 4 (2014): 251–58. http://dx.doi.org/10.5369/jsst.2014.23.4.251.

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Ahn, Soonjae, Isu Shin, and Youngho Kim. "Pre-impact fall detection using an inertial sensor unit." Journal of Foot and Ankle Research 7, Suppl 1 (2014): A124. http://dx.doi.org/10.1186/1757-1146-7-s1-a124.

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Ahn, Soonjae, Jongman Kim, Bummo Koo, and Youngho Kim. "Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset." Sensors 19, no. 4 (2019): 774. http://dx.doi.org/10.3390/s19040774.

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In this study, pre-impact fall detection algorithms were developed based on data gathered by a custom-made inertial measurement unit (IMU). Four types of simulated falls were performed by 40 healthy subjects (age: 23.4 ± 4.4 years). The IMU recorded acceleration and angular velocity during all activities. Acceleration, angular velocity, and trunk inclination thresholds were set to 0.9 g, 47.3°/s, and 24.7°, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3°/s, and 0.19, respectively, for an algorithm using the triangle feature (TF algorithm). The algorithms were validated by the results of a blind test using four types of simulated falls and six types of activities of daily living (ADL). VA and TF algorithms resulted in lead times of 401 ± 46.9 ms and 427 ± 45.9 ms, respectively. Both algorithms were able to detect falls with 100% accuracy. The performance of the algorithms was evaluated using a public dataset. Both algorithms detected every fall in the SisFall dataset with 100% sensitivity). The VA algorithm had a specificity of 78.3%, and TF algorithm had a specificity of 83.9%. The algorithms had higher specificity when interpreting data from elderly subjects. This study showed that algorithms using angles could more accurately detect falls. Public datasets are needed to improve the accuracy of the algorithms.
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Wang, Lan, Min Peng, and Qingfeng Zhou. "Pre-Impact Fall Detection Based on Multi-Source CNN Ensemble." IEEE Sensors Journal 20, no. 10 (2020): 5442–51. http://dx.doi.org/10.1109/jsen.2020.2970452.

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Nyan, M. N., Francis E. H. Tay, and Matthew Z. E. Mah. "Application of motion analysis system in pre-impact fall detection." Journal of Biomechanics 41, no. 10 (2008): 2297–304. http://dx.doi.org/10.1016/j.jbiomech.2008.03.042.

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Ahn, Soonjae, Isu Shin, Jaesung Ryu, et al. "GS7-8 Application of a pre-impact fall detection using an inertial sensor unit(GS7: Rehabilitation Biomechanics II)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2015.8 (2015): 189. http://dx.doi.org/10.1299/jsmeapbio.2015.8.189.

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Šeketa, Goran, Lovro Pavlaković, Dominik Džaja, Igor Lacković, and Ratko Magjarević. "Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms." Sensors 21, no. 13 (2021): 4335. http://dx.doi.org/10.3390/s21134335.

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Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.
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Dissertations / Theses on the topic "Pre-impact fall detection"

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Carlsson, Tor. "Individualized Motion Monitoring by Wearable Sensor : Pre-impact fall detection using SVM and sensor fusion." Thesis, KTH, Skolan för teknik och hälsa (STH), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-171088.

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Among the elderly, falling represents a major threat to the individual health, and is considered as a major source of morbidity and mortality. In Sweden alone, three elderly are lost each day in accidents related to falling. The elderly who survive the fall are likely to be suffering from decreased quality of life. As the percentage of elderly increase in the population worldwide, the need for preventive methods and tools will grow drastically in order to deal with the increasing health-care costs. This report is the result of a conceptual study where an algorithm for individualized motion monitoring and pre-impact fall detection is developed. The algorithm learns the normal state of the wearer in order to detect anomalous events such as a fall. Furthermore, this report presents the requirements and issues related to the implementation of such a system. The result of the study is presented as a comparison between the individualized system and a more generalized fall detection system. The conclusion is that the presented type of algorithm is capable of learning the user behaviour and is able to detect a fall before the user impacts the ground, with a mean lead time of 301ms.<br>Bland äldre är risken för att drabbas av fallrelaterade skador överhängande, ofta med svåra fysiska skador och psykiska effekter som följd. Med en ökande andel äldre i befolkningsmängden beräknas även samhällets kostnad för vård att stiga. Genom aktiva samt preventiva åtgärder kan graden av personligt lidande och fallre- laterade samhällskostnader reduceras. Denna rapport är resultatet av en konceptuell studie där en algoritm för aktiv, individanpassad falldetektion utvecklats. Algoritmen lär sig användarens normala rörelsemönster och skall därefter särskilja dessa från onormala rörelsemönster. Rapporten beskriver de krav och frågeställningar som är relevanta för utvecklingen av ett sådant system. Vidare presenteras resultatet av studien i form av en jämförelse mellan ett individanpassat och generellt system. Resultatet av studien visar att algoritmen kan lära sig användarens vanliga rörelsemönster och därefer särskilja dessa från ett fall, i medelvärde 301ms innan användaren träffar marken.
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Johansson, Simon. "A machine-learning based approach to pre-impact fall detection with wearable devices : MOTION MONITORING USING SENSOR FUSION AND THE SUPPORT VECTOR MACHINE." Thesis, KTH, Maskinkonstruktion (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170804.

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Falling accidents represents a major threat and is considered as a major source of morbidity and mortality among the elderly. As a consequence of fall related injuries, three people dies in Sweden every day. Additional factors, such as fear of falling further impacts the quality of life for the elderly. Due to the demographic change, which results in an increasing amount of elderly in the population, the costrelated to fall accidents is increasing. In order to the reduce the cost, preventivemethods and tools are believed to be a feasible approach. This report is the resultof a conceptual study that presents the issues related to the development of an individualizedmotion monitoring system applicable to pre-impact fall detection. Thestrategy adopted for fall detection, is to learn the normal behaviour of the user inorder to recognize fall as an anomaly from activities of daily living. The results are based on the comparison between an individualized, and a generalized algorithm.The conclusion is that the suggested algorithm is applicable in pre-impact fall detection systems.<br>Bland äldre människor utgör fallolyckor ett stort hot och anses vara en betydande orsak för all sjuklighet och dödlighet. I Sverige dör tre äldre varje dag till följd av en fallolycka och de årliga kostnaderna förväntas stiga från 15 till 22 miljarder fram till år 2050. Bland dessa kostnader finns direkta sjukvårdskostnader och kostnader relateradetill försämrad livskvalitét. Med demografiska förändringar som en ökande andel äldre i populationen anses aktiva och preventiva åtgärder vara en lämpligmetod för att minska kostnaderna och det personliga lidandet. Denna rapport ärresultatet av en konceptuell studie som behandlar de krav och frågeställningar somställs vid utvecklingen av ett system för aktiv individanpassad fallolycksövervakning. Genom att lära sig det normala rörelsemönstret kan algoritmen urskilja ett fall som en anomali. Resultatet presenteras i form av en jämförelse mellan ett individanpassat och ett generellt system. Slutsatsen är att den presenterande algoritmen kan användas för att lära sig normala rörelsemönster, för att därefter urskilja ett fall innan användaren träffar marken.
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Book chapters on the topic "Pre-impact fall detection"

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Wu, G., and S. Xue. "Automatic Fall Detection Based on Kinematic Characteristics during the Pre-impact Phase of Falls." In IFMBE Proceedings. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14515-5_88.

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Yu, Xiaoqun, Jaehyuk Jang, and Shuping Xiong. "Machine Learning-Based Pre-impact Fall Detection and Injury Prevention for the Elderly with Wearable Inertial Sensors." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80713-9_36.

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Mao, Liyu, Ding Liang, Yunkun Ning, Yingnan Ma, Xing Gao, and Guoru Zhao. "Pre-impact and Impact Detection of Falls Using Built-In Tri-accelerometer of Smartphone." In Health Information Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06269-3_18.

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Conference papers on the topic "Pre-impact fall detection"

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"Pre-impact Fall Detection using Wearable Sensor Unit." In International Conference on Biomedical Electronics and Devices. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004902602070211.

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Li, Shengchao, Hao Xiong, and Xiumin Diao. "Pre-Impact Fall Detection Using 3D Convolutional Neural Network." In 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, 2019. http://dx.doi.org/10.1109/icorr.2019.8779504.

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Xiao, Jinzhuang, Wenyang Ren, Xiaolei Huang, and Hongrui Wang. "A surface electromyography-based pre-impact fall detection method." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623336.

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Leone, Alessandro, Gabriele Rescio, Andrea Caroppo, and Pietro Siciliano. "An EMG-based system for pre-impact fall detection." In 2015 IEEE Sensors. IEEE, 2015. http://dx.doi.org/10.1109/icsens.2015.7370314.

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Zhong, Zhichao, Feiyu Chen, Qian Zhai, et al. "A Real-time Pre-impact Fall Detection and Protection System." In 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2018. http://dx.doi.org/10.1109/aim.2018.8452687.

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Otanasap, Nuth. "Pre-Impact Fall Detection Based on Wearable Device Using Dynamic Threshold Model." In 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE, 2016. http://dx.doi.org/10.1109/pdcat.2016.083.

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Otanasap, Nuth, and Poonpong Boonbrahm. "Pre-impact fall detection system using dynamic threshold and 3D bounding box." In Eighth International Conference on Graphic and Image Processing, edited by Yulin Wang, Tuan D. Pham, Vit Vozenilek, David Zhang, and Yi Xie. SPIE, 2017. http://dx.doi.org/10.1117/12.2266822.

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Su, Yiwen, Daliang Liu, and Yingfeng Wu. "A multi-sensor based pre-impact fall detection system with a hierarchical classifier." In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2016. http://dx.doi.org/10.1109/cisp-bmei.2016.7852995.

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Liang, Shengyun, Tianyue Chu, Dan Lin, Yunkun Ning, Huiqi Li, and Guoru Zhao. "Pre-impact Alarm System for Fall Detection Using MEMS Sensors and HMM-based SVM Classifier." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8513119.

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Otanasap, Nuth, and Poonpong Boonbrahm. "Pre-impact fall detection approach using dynamic threshold based and center of gravity in multiple Kinect viewpoints." In 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2017. http://dx.doi.org/10.1109/jcsse.2017.8025955.

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