What Times What Equals 98

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What Times What Equals 98

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Precession Of The Equinoxes

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By Lourdes Martínez-Villaseñor Lourdes Martínez-Villaseñor Scilit Preprints.org Google Scholar * , Hiram Ponce Hiram Ponce Scilit Preprints.org Google Scholar * , Jorge Brieva Jorge Brieva Scilit Preprints.org Google Scholar , Ernesto Moya-Albor Preprint. Scholar , José Núñez-Martínez José Núñez-Martínez Scilit Preprints.org Google Scholar and Carlos Peñafort-Asturiano Carlos Peñafort-Asturiano Scilit Preprints.org Google Scholar

Received: 01.02.2019 / Modified: 9.04.2019 / Accepted: 13.04.2019 / Published: 28.04.2019

Law Of Cosines

Falls, especially among the elderly, are a major health problem worldwide. Reliable fall detection systems can mitigate the negative consequences of a fall. Important challenges and issues described in the literature include the difficulty of fairly comparing fall detection systems and machine learning techniques for detection. In this paper, we introduce the UP-Fall Detection Dataset. The dataset contains raw files and feature sets obtained from 17 healthy young people with no impairments who performed 11 activities and falls in three trials. The database also compiles more than 850 GB of information from wearable sensors, environmental sensors and camera devices. Two experimental use cases were demonstrated. Our dataset aims to help the human action detection and machine learning research communities fairly compare their fall detection solutions. It also provides many experimental opportunities for the signal detection, vision and machine learning community.

According to the World Health Organization (WHO), falls are the second leading cause of unintentional injury and death in the world. Falls also often lead to functional dependence in the elderly. “About 28-35% of people aged 65 and over fall each year, rising to 32-42% among people over 70” [1]. The incidence of falls varies from country to country and is less common in developed countries [2]. In Mexico, 33.5% of the elderly over 60 had experienced at least one fall in the year before the interview [3].

The prevalence of falls increases with age worldwide and is actually considered a serious health problem. Falls often require immediate medical attention as they result in 20-30% of minor to severe injuries [1] or even death. Fall detection systems warn in the event of a fall and mitigate its consequences. The negative consequences of falls can be reduced by real-time fall detection, which reduces the time it takes for a patient to seek medical care [4]. If falls are not detected quickly, patients sometimes end up lying on the floor, causing other health and psychological problems. If falls are observed less frequently in real subjects, participants tend to forget accurate fall data. This issue of memories is more critical, especially for elderly or disabled participants [5]. Fall detection systems can help determine the actual time of the fall.

Three main approaches to fall detection systems [6] have been identified in the literature, depending on whether the data is collected by wearable sensors, ambient sensors, or vision devices. Igual et al. [4] classified fall sensors into two broad approaches: contextual systems and wearable devices. Contextual systems consider all systems that use sensors in the environment, including environmental sensors such as infrared, floor, radar, microphone, and pressure sensors, as well as vision-based devices. Cameras, motion sensors and Kinect are also considered contextual systems. Fall sensors often use wearable sensors with accelerometers and gyroscopes. Recently, sensors embedded in smartphones, smartwatches, and other portable devices have gained popularity in fall detection systems due to the high affordability and global adoption of these devices. Other authors such as Mubashir et al. [6] divided fall detection approaches into three categories: wearable, environmental sensor-based, and vision-based. These reviews of fall detection systems and recent studies such as [7] provide a detailed analysis of the advantages and limitations of these approaches and other emerging multimodal systems that incorporate various combinations of wearable, vision, and ambient sensors. Important challenges and issues cited by most authors include privacy concerns, disruptive and operational devices, and difficulty in comparing techniques. This last problem is due to the lack of public databases, especially those that record actual falls among seniors.

Bayes’ Theorem: What It Is, Formula, And Examples

Due to the low frequency and variety of falls in real life, the collection of real unexpected fall data sets is difficult. Most fall detection datasets are simulated under laboratory conditions. Khan et al. [8] described the problem of a huge imbalance in actual fall data: if all people in kindergarten fell on average 2.6 times per year, the data set recorded in one year would have 31.55 million normal activities per person and 2.6 falls. Therefore, even if the data from simulated falls cannot accurately reproduce a fall in reality, the creation of datasets collecting data from volunteers simulating different falls still seems to be the best option for evaluating a fall detection system. Many studies [8, 9] reported that there is no reference framework and few publicly available datasets for fall detection. In addition to almost no access to actual data, these facts hinder the validation and comparison of systems and methods.

We present a publicly available multimodal fall detection dataset to address the above problem. The UP-Fall Detection dataset was collected on 17 healthy young individuals without any impairment using different methods, namely wearable sensors, ambient sensors and vision devices. The volunteers performed six daily activities and simulated five different types of falls, three trials each. We use five wearable sensors to collect accelerometer, gyroscope and ambient light data. In addition, we received data from one electroencephalograph (EEG) headset, six infrared sensors, and two cameras. This dataset contains raw and feature sets that aggregate over 850 GB of information from wearable sensors, environmental sensors, and camera devices.

Our dataset aims to help the human action detection and machine learning research communities fairly compare their fall detection solutions. It also provides many experimental opportunities for the signal detection, vision and machine learning community. We are aware that because the falls were simulated by young, healthy adults without disabilities for safety reasons, older people may experience some differences from actual falls. However, this data set can be used to inform learning experiments to predict elderly or disabled adults.

The rest of the paper is organized as follows: first, Section 2 provides an overview of fall detection datasets. Second, our UP fall detection dataset is described in Section 3. In Section 4, we explain two experimental use cases. Experiments and results are presented in Section 5. We discuss our results and present conclusions in Sections 6 and 7, respectively.

S&p 500 Automated Trading Using Machine Learning

Many fall detection systems have been described in the literature, and therefore very few datasets are publicly available. In this section, we provide an overview of fall detection datasets. We considered sensor-based databases that include wearable or ambient sensors; vision-based databases, including conventional or depth cameras or motion capture data; multimodal databases containing a combination of sensors and/or cameras. There are some important publicly available datasets for human activity detection, such as SCUT-NAA [10], which are excluded from this review because they do not include falls.

The most cited sensor datasets based on fall detection are listed in [11, 12, 13, 14, 15] and summarized in Table 1.

The German Aerospace Center (DLR) dataset [11] is a single belt inertial measurement unit (IMU) dataset consisting of 16 individuals (6 females and 5 males) aged between 23 and 50 years. They consider seven activities (walking, running, standing, sitting, lying down, falling and jumping). Fall types were not distinguished.

The MobiFall fall detection dataset [12] was developed by the Biomedical Informatics and eHealth Laboratory of the Technological Education Institute of Crete. They captured data generated by inertial sensors (a 3D accelerometer and a gyroscope) on a smartphone placed in a trouser pocket. 24 subjects, seventeen men and seven women with a range of ages

Gone But Not Forgotten

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