Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.
Wearable devices for fall detection have received attention in academia and industry, because falls are very dangerous, especially for elderly people, and if immediate aid is not provided, it may result in death. However, some predictive devices are not easily worn by elderly people. In this work, a huge dataset, including 2520 tests, is employed to determine the best sensor placement location on the body and to reduce the number of sensor nodes for device ergonomics. During the tests, the volunteer’s movements are recorded with six groups of sensors each with a triaxial (accelerometer, gyroscope and magnetometer) sensor, which is placed tightly on different parts of the body with special straps: head, chest, waist, right-wrist, right-thigh and right-ankle. The accuracy of individual sensor groups with their location is investigated with six machine learning techniques, namely the k-nearest neighbor (k-NN) classifier, Bayesian decision making (BDM), support vector machines (SVM), least squares method (LSM), dynamic time warping (DTW) and artificial neural networks (ANNs). Each technique is applied to single, double, triple, quadruple, quintuple and sextuple sensor configurations. These configurations create 63 different combinations, and for six machine learning techniques, a total of 63 × 6 = 378 combinations is investigated. As a result, the waist region is found to be the most suitable location for sensor placement on the body with 99.96% fall detection sensitivity by using the k-NN classifier, whereas the best sensitivity achieved by the wrist sensor is 97.37%, despite this location being highly preferred for today’s wearable applications.
Laser-induced thermotherapy (LITT) has been widely studied since it is a minimally invasive technique for focal destruction of liver tumors without side effects. However, there are still some concerns about the treatment and monitoring thermal effects during operation. Although real-time imaging modalities are available, like magnetic resonance imaging, they are not cost-effective and not applicable to all conditions. This paper presents artificial neural network (ANN) modeling of laser-induced thermal damages on ex vivo liver tissue. In this work three laser sources, i.e. a diode pumped laser with a wavelength of 980 nm, a 1070 nm yttrium lithium fluoride fiber laser, and a 1940 nm thulium fiber laser, were used in order to thermally damage tissues by applying the laser until coagulation observed. The diameter and depth of coagulation were empirically measured and used to train the ANN model by finding the correlation between laser parameters (application time, power, penetration depth, wavelength, and spot size) and thermal damages. Thermal damage can be determined by observing coagulation diameter and coagulation depth. The prediction ability and accuracy of the trained ANN model were tested by comparing the actual and simulated results. Our result showed that the ANN successfully predicts LITT damage in terms of coagulation diameter and coagulation depth, with a very high accuracy. The trained ANN model was compared with two mathematical models. In terms of performance, the ANN is a very useful and practical tool for determining LITT damage.
Abstract. Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.
The ability to pattern highly conductive features on paper substrates is critically important for applications in radio frequency identification (RFID) tags, displays, sensors, printed electronics, and diagnostics. Ink-jet printing particle-free reactive silver inks is an additive, material efficient and versatile strategy for fabrication of highly conductive patterns; however, the intrinsic wetting properties of cellulose based papers are not suitable to serve as substrates for this process. This study reports one-step and practical modification of the surface of paper substrates using industrially available materials. The paper substrates were dip-coated with films of hydrocarbon and fluorocarbon based polymeric resins. Ink-jet printing particle-free reactive silver inks on the modified paper substrates followed by fast thermal annealing resulted in highly conductive patterns. The coatings improved the conductivity of the patterns and reduced the number of printing layers required to obtain conductivity. We finally demonstrated fabrication of a printed RFID tag on the coated paper substrates operating at the frequency range of 865-870 MHz. Cellulose (2019) 26:3503-3512 https://doi.org/10.1007/s10570-019-02326-y( 0123456789().,-volV) ( 01234567 89().,-volV) Graphical abstract Hydrophobic resin Dip-coating Ag Ink 1 µm 20 µm
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.