標(biāo)題: Titlebook: Challenges and Trends in Multimodal Fall Detection for Healthcare; Hiram Ponce,Lourdes Martínez-Villase?or,Ernesto Mo Book 2020 Springer N [打印本頁(yè)] 作者: Carter 時(shí)間: 2025-3-21 18:24
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書(shū)目名稱(chēng)Challenges and Trends in Multimodal Fall Detection for Healthcare讀者反饋
書(shū)目名稱(chēng)Challenges and Trends in Multimodal Fall Detection for Healthcare讀者反饋學(xué)科排名
作者: 嫻熟 時(shí)間: 2025-3-21 23:39 作者: magenta 時(shí)間: 2025-3-22 00:40 作者: Onerous 時(shí)間: 2025-3-22 07:52
Draft Agrarian Programme, July 1895features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accur作者: 可用 時(shí)間: 2025-3-22 08:57
Patient Relationship Managementelderly people’s quality of life, as they can read and analyze through sensors their facial expressions, voice, among others. Gamification in older people may motivate elderly people to socialize with their peers through social interaction and by doing activities as exercising. Thus, this chapter pr作者: 傳授知識(shí) 時(shí)間: 2025-3-22 14:10 作者: 傳授知識(shí) 時(shí)間: 2025-3-22 18:34 作者: 機(jī)械 時(shí)間: 2025-3-23 00:09 作者: Stress-Fracture 時(shí)間: 2025-3-23 05:15
Wearable Sensors Data-Fusion and Machine-Learning Method for Fall Detection and Activity Recognitionhe 3 most similar users as the ones used for the test. The internal evaluation on the 9 users showed that with this optimized configuration the method achieves 98% accuracy. During the final evaluation for the challenge, our method achieved the highest results (82.5% F1-score, and 98% accuracy) and 作者: CRUDE 時(shí)間: 2025-3-23 07:08 作者: Volatile-Oils 時(shí)間: 2025-3-23 10:42 作者: 商業(yè)上 時(shí)間: 2025-3-23 17:44
A Novel Approach for Human Fall Detection and Fall Risk Assessmentnces of fall such as high fall risk or low fall risk level. Thus, making the system adaptable to the physical condition of the user. The proposed system also performs a fall injury assessment after the fall event to alert for appropriate assistance and includes a fall risk assessment tool which can 作者: 碎片 時(shí)間: 2025-3-23 21:06 作者: 指耕作 時(shí)間: 2025-3-24 02:16
Studies in Systems, Decision and Controlhttp://image.papertrans.cn/c/image/223434.jpg作者: genuine 時(shí)間: 2025-3-24 05:23
https://doi.org/10.1007/978-1-4613-2793-6ment of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate be作者: 察覺(jué) 時(shí)間: 2025-3-24 07:52 作者: guardianship 時(shí)間: 2025-3-24 10:57 作者: 注入 時(shí)間: 2025-3-24 15:36 作者: 灰姑娘 時(shí)間: 2025-3-24 19:31 作者: 口訣 時(shí)間: 2025-3-25 01:48 作者: Rebate 時(shí)間: 2025-3-25 04:17 作者: THROB 時(shí)間: 2025-3-25 10:32 作者: agonist 時(shí)間: 2025-3-25 14:49 作者: 熱心 時(shí)間: 2025-3-25 17:45
https://doi.org/10.1007/978-3-8350-9117-7t role, because fall is the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. The three basic approaches used to develop fall detection systems include some sort of wearable, ambient or non-invasive based devices. Most of such syste作者: insincerity 時(shí)間: 2025-3-25 21:09
Challenges and Trends in Multimodal Fall Detection for Healthcare978-3-030-38748-8Series ISSN 2198-4182 Series E-ISSN 2198-4190 作者: 小木槌 時(shí)間: 2025-3-26 01:22
https://doi.org/10.1007/978-3-030-38748-8Fall Detection; Fall Classification; Human Fall Detection; Fall Detection data Set; Intelligent Real-Tim作者: Muscularis 時(shí)間: 2025-3-26 07:44
978-3-030-38750-1Springer Nature Switzerland AG 2020作者: 說(shuō)笑 時(shí)間: 2025-3-26 09:12
D. B. Hoyt,W. G. Junger,A. N. Ozkans located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30 and 38.50%, respectively.作者: SLAY 時(shí)間: 2025-3-26 14:24
Psycho- und soziosomatische Konzeptedentifying falls obtained results that surpassed the other models submitted to the test. They were successful in extracting various information from a highly sophisticated and incredibly dimensional dataset to help professionals from various areas expand their investigations in the field of falling people.作者: Accrue 時(shí)間: 2025-3-26 16:52
Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Tes located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30 and 38.50%, respectively.作者: Germinate 時(shí)間: 2025-3-26 22:09 作者: Recessive 時(shí)間: 2025-3-27 04:00
Book 2020 fall detection systems, as well as for those interested in solving challenging, signal recognition, vision and machine learning problems. Potential applications include health care, robotics, sports, human–machine interaction, among others..作者: HAVOC 時(shí)間: 2025-3-27 07:25
2198-4182 of sensor technologies, artificial intelligence, machine leThis book focuses on novel implementations of sensor technologies, artificial intelligence, machine learning, computer vision and statistics for automated, human fall recognition systems and related topics using data fusion..It includes the作者: garrulous 時(shí)間: 2025-3-27 12:13
Book 2020ated, human fall recognition systems and related topics using data fusion..It includes theory and coding implementations to help readers quickly grasp the concepts and to highlight the applicability of this technology. For convenience, it is divided into two parts. The first part reviews the state o作者: 增長(zhǎng) 時(shí)間: 2025-3-27 13:41 作者: 令人作嘔 時(shí)間: 2025-3-27 19:16
The Agrarian Question and Socialismlearning model from the first place scored . in .-score, outperforming the baseline of .. After analyzing the implementations from the participants, we summarized the insights and trends of fall classification.作者: predict 時(shí)間: 2025-3-27 23:51 作者: hemophilia 時(shí)間: 2025-3-28 04:16
Open Source Implementation for Fall Classification and Fall Detection Systems UP-Fall Detection dataset. This implementation comprises a set of open codes stored in a GitHub repository for full access and provides a tutorial for using the codes and a concise example for their application.作者: 賠償 時(shí)間: 2025-3-28 09:04 作者: 考古學(xué) 時(shí)間: 2025-3-28 14:03
Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniqp human fall detection systems include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. This study reviewed the techniques and approaches employed to device systems to detect unintentional falls and classified them based on the approaches employed and sensors used.作者: 悄悄移動(dòng) 時(shí)間: 2025-3-28 15:07
Open Source Implementation for Fall Classification and Fall Detection Systemsment of fall detection and fall classification systems. These systems can help to improve the time in which a person receives help after a fall occurs. Many of the simulated falls datasets consider different types of fall however, very few fall detection systems actually identify and discriminate be作者: Bone-Scan 時(shí)間: 2025-3-28 22:48
Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Tedirectly, to society’s productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we present and evaluate several bidirectional long short-term memory (Bi-LSTM) models using a data set provided by the Challenge UP competition. The main goa作者: 飾帶 時(shí)間: 2025-3-28 22:59 作者: Highbrow 時(shí)間: 2025-3-29 07:02 作者: 羊欄 時(shí)間: 2025-3-29 09:00
Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning te作者: canvass 時(shí)間: 2025-3-29 12:33
Approaching Fall Classification Using the UP-Fall Detection Dataset: Analysis and Results from an Inence on Neural Networks (IJCNN 2019). This competition lies on the fall classification problem, and it aims to classify eleven human activities (i.e. five types of falls and six simple daily activities) using the joint information from different wearables, ambient sensors and video recordings, store
