What Times What Equals 19

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What Times What Equals 19

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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-. .org Google 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: 1 February 2019 / Revised: 9 April 2019 / Accepted: 13 April 2019 / Published: 28 April 2019

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Stroke is a major health problem worldwide, especially in the elderly. A reliable fall detection system minimizes the negative consequences of falls. One of the key challenges and issues reported in the literature is the difficulty of making fair comparisons between fall detection systems and machine learning techniques for detection. In this paper, we present UP-collapse detection data. The data set includes raw and performance data collected from 17 healthy young subjects, 11 of whom were in no activity, three trials per session. The dataset summarizes more than 850 GB of data from wearable sensors, environmental sensors, and vision devices. Two empirical cases are presented. The goal of our database is to help the human performance recognition and machine learning research communities fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning communities.

According to the World Health Organization, falls are the second leading cause of unintentional injuries and deaths worldwide. Falls can also lead to functional dependence in the elderly. “Approximately 28-35% of people aged 65 and over will fall each year, rising to 32-42% over 70” [1]. The incidence of falls varies by country and is rare in developed countries [2]. In Mexico, 33.5% of older adults over 60 fell at least once a year before the interview [3].

The prevalence of falls increases worldwide with age and is indeed an important health problem. Falls often require emergency treatment, as they account for 20% to 30% of minor to severe injuries. A fall detection system will alert you to minimize these consequences. Early detection of the adverse consequences of falls can improve the time needed for patient treatment [4]. Patients sometimes lie on the floor and collapse can cause additional medical and psychological problems if undetected. When monitoring becomes more realistic in everyday situations, participants tend to forget the exact information of the fall. This recall problem is particularly acute in elderly or disabled participants [5]. A fall detection system can help determine the exact time of a fall.

Three main methods for fall detection systems have been reported in the literature [6], depending on whether the data is obtained through wearable sensors, environmental sensors, or visual devices. Igual et al. [4] Fall detection devices are classified into two broad categories: context-aware systems and wearable devices. Situational awareness systems use sensors embedded in the environment to consider all systems such as infrared, pavement, radar, microphone and pressure sensors as well as vision-based devices. Cameras, motion capture devices, and Kinect are also considered content notification systems. Wearable sensors with accelerometers and gresoscopes are often used in fall detectors. Recently, sensors embedded in smartphones, smartwatches, and other wearable devices have become popular fall detection systems due to the low cost and widespread adoption of these devices worldwide. Other authors such as Mubasher et al. [6], which are divided into three categories: wearable device-based, environmental sensor-based, and vision-based. A recent review of fall detection systems and similar [7] analyzed the advantages and limitations of such approaches in detail, and proposed an additional novel multifunctional system that includes various combinations of wearable, visual, and environmental sensors. Among the key challenges and issues reported by many authors are privacy issues, limitations of uptake and operational tools, and difficulties in comparisons between technologies. This last problem stems from the lack of public databases, especially those that record actual declines in adults.

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Due to the small number and high number of real-life cases, it is difficult to collect data on unexpected falls. Many databases for fall detection have been simulated in the laboratory. Khan et al. [8] highlight the problem of a large imbalance in the actual fall figures: if all people in kindergarten fell an average of 2.6 times a year, the last recorded figure in one year would be 31.55 million per person. Have normal activities, 2.6 good. will fall. Therefore, even if simulated fall data cannot truly reproduce falls, creating a database that collects data from volunteers who simulate different falls still appears to be the best option for evaluating fall detection systems. Several surveys [8, 9] report a lack of reference frames and a lack of publicly available data sets for fall detection. These facts, in addition to the lack of real data, prevent the validation and comparison of systems and methods.

To solve the above-mentioned problem, we proposed an open multidimensional dataset for fall detection. UP-fall survey data were collected using 17 healthy young subjects using a variety of methods including wearable sensors, environmental sensors, and vision devices. The volunteers performed six activities of daily living and handled five different falls, three trials per session. We use five wearable sensors to collect accelerometer, gristoscope, and ambient light data. In addition, we received data from an electroencephalographic (EEG) headset, six infrared sensors, and two cameras. This dataset contains a collection of raw and active data from wearable sensors, environmental sensors, and vision devices summarizing more than 850 GB of data.

The goal of our database is to help the human performance recognition and machine learning research communities fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning communities. We know that these falls are simulated by healthy young adults, some differences from the actual fall in adults can be detected for safety reasons. However, this dataset can be used to deliver predictive learning experiences to older or disabled adults.

The rest of the paper is organized as follows: first, an overview of the fall detection dataset is presented in Section 2, second, our UP-fall detection dataset is described in Section 3, and we describe the experiments in Section 4. We describe two cases. Results are presented in Section 5, and we discuss our results in Section 6 and Section 7, respectively.

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There are many fall detection systems reported in the literature, and therefore few data sets. In this section, we provide an overview of fall detection data. We considered a database based on sensors such as wearable or environmental sensors. A vision-based database that includes regular or depth camera or motion capture data is a multi-database that integrates sensors and/or cameras. There are some important public datasets that identify human activity, such as SCUT-NAA [10], which were excluded from this review because they do not include falls.

The most cited data for sensors based on fall detection are reported in [11, 12, 13, 14, 15] and summarized in Table 1.

The DLR (German Aerospace Center) dataset [11] is a collection of waist-mounted Internal Measurement Unit (IMU) data of 16 people (6 women and 5 men) aged 23 to 50 years. They consider seven activities (walking, running, standing, sitting, falling, and falling). The types of falls did not differ.

The MobiFall Fall Detection Database [12] was developed by the Bioinformatics and Health Laboratory of the Technical Education Institute of Crete. They captured data generated from internal sensors (3D accelerometers and gristoscopes) on smartphones placed in trouser pockets. There were 24 subjects of all ages, seventeen males and seven females

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