scholarly journals Smart Fall Detection Framework Using Hybridized Video and Ultrasonic Sensors

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 508
Author(s):  
Feng-Shuo Hsu ◽  
Tang-Chen Chang ◽  
Zi-Jun Su ◽  
Shin-Jhe Huang ◽  
Chien-Chang Chen
Keyword(s):  
Density Functional ◽  
Probability Model ◽  
Three Dimensional ◽  
Fall Detection ◽  
The Elderly ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
Movement Trajectory ◽  
Motion Data ◽  
Comparable Performance

Fall accidents can cause severe impacts on the physical health and the quality of life of those who suffer limb diseases or injuries, the elderly, and their caregivers. Moreover, the later the accident is discovered, the lower the chance of recovery of the injured one. In order to detect accidents earlier, we propose a data-driven human fall detection framework. By combining the sensing mechanism of a commercialized webcam and an ultrasonic sensor array, we develop a probability model for automatic human fall monitoring. The webcam and ultrasonic array respectively collect the transverse and longitudinal time-series signals from a moving subject, and then these signals are assembled as a three-dimensional (3D) movement trajectory map. We also use two different detection-tracking algorithms for recognizing the tracked subjects. The mean height of the subjects is 164.2 ± 12 cm. Based on the data density functional theory (DDFT), we use the 3D motion data to estimate the cluster numbers and their cluster boundaries. We also employ the Gaussian mixture model as the DDFT kernel. Then, we utilize those features to build a probabilistic model of human falling. The model visually exhibits three possible states of human motions: normal motion, transition, and falling. The acceptable detection accuracy and the small model size reveals the feasibility of the proposed hybridized platform. The time from starting the alarm to an actual fall is on average about 0.7 s in our platform. The proposed sensing mechanisms offer 90% accuracy, 90% sensitivity, and 95% precision in the data validation. Then these vital results validate that the proposed framework has comparable performance to the contemporary methods.

scholarly journals Preimpact Fall Detection for Elderly Based on Fractional Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ning Liu ◽  
Dedi Zhang ◽  
Zhong Su ◽  
Tianrun Wang
Keyword(s):  
Social Cost ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Selection Method ◽  
Excellent Performance ◽  
Motion Data ◽  
Algorithm Framework ◽  
Warning Effect ◽  
Mean Filtering

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

scholarly journals An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4192
Author(s):  
Leyuan Liu ◽  
Yibin Hou ◽  
Jian He ◽  
Jonathan Lungu ◽  
Ruihai Dong
Keyword(s):  
Energy Efficient ◽  
Short Term Memory ◽  
Fall Detection ◽  
The Elderly ◽  
Experimental Result ◽  
Sleep Mode ◽  
Detection Accuracy ◽  
Community Based ◽  
Care For The Elderly ◽  
Specificity And Sensitivity

A fall detection module is an important component of community-based care for the elderly to reduce their health risk. It requires the accuracy of detections as well as maintains energy saving. In order to meet the above requirements, a sensing module-integrated energy-efficient sensor was developed which can sense and cache the data of human activity in sleep mode, and an interrupt-driven algorithm is proposed to transmit the data to a server integrated with ZigBee. Secondly, a deep neural network for fall detection (FD-DNN) running on the server is carefully designed to detect falls accurately. FD-DNN, which combines the convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms, was tested on both with online and offline datasets. The experimental result shows that it takes advantage of CNN and LSTM, and achieved 99.17% fall detection accuracy, while its specificity and sensitivity are 99.94% and 94.09%, respectively. Meanwhile, it has the characteristics of low power consumption.

scholarly journals IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5948
Author(s):  
Taekjin Han ◽  
Wonho Kang ◽  
Gyunghyun Choi
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Privacy Preservation ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Ultra Wideband ◽  
Detection Accuracy ◽  
Secondary Damage ◽  
Behavior Types

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

scholarly journals Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Quoc T. Huynh ◽  
Uyen D. Nguyen ◽  
Lucia B. Irazabal ◽  
Nazanin Ghassemian ◽  
Binh Q. Tran
Keyword(s):  
Sensitivity And Specificity ◽  
Human Subjects ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Motion Data ◽  
Time Motion ◽  
High Acceleration ◽  
The Usa ◽  
Velocity Changes

Falling is a common and significant cause of injury in elderly adults (>65 yrs old), often leading to disability and death. In the USA, one in three of the elderly suffers from fall injuries annually. This study’s purpose is to develop, optimize, and assess the efficacy of a falls detection algorithm based upon a wireless, wearable sensor system (WSS) comprised of a 3-axis accelerometer and gyroscope. For this study, the WSS is placed at the chest center to collect real-time motion data of various simulated daily activities (i.e., walking, running, stepping, and falling). Tests were conducted on 36 human subjects with a total of 702 different movements collected in a laboratory setting. Half of the dataset was used for development of the fall detection algorithm including investigations of critical sensor thresholds and the remaining dataset was used for assessment of algorithm sensitivity and specificity. Experimental results show that the algorithm detects falls compared to other daily movements with a sensitivity and specificity of 96.3% and 96.2%, respectively. The addition of gyroscope information enhances sensitivity dramatically from results in the literature as angular velocity changes provide further delineation of a fall event from other activities that may also experience high acceleration peaks.

Fall Detection by Wearable Device Using Support Vector Machine

2014 ◽  
Vol 687-691 ◽  
pp. 1003-1006
Author(s):  
Xian Wei Wang ◽  
Fu Cheng Cao
Keyword(s):  
Support Vector Machine ◽  
Fall Detection ◽  
The Elderly ◽  
Elderly Subjects ◽  
Support Vector ◽  
Detection Accuracy ◽  
False Negatives ◽  
Movement Data ◽  
Elderly Health ◽  
Left And Right

In this study, using simulated falls and activities of daily living (ADL) performed by elderly subjects, the ability to discriminate between falls and ADL was investigated with wearable tri-axial accelerometer sensors, mounted on the chest. The movement data of human body analysis was performed using one-class support vector machine (SVM) to determine the feature of motion types. Experiments to detect falls are performed in four directions: forward, backward, left, and right. The preliminary results show that this method can detect the falls effectively, reduces both false positives and false negatives, while improving fall detection accuracy, and the application can offer a new guarantee for the elderly health.

scholarly journals Application of Image Processing and Three-Dimensional Data Reconstruction Algorithm Based on Traffic Video in Vehicle Component Detection

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Gang Li ◽  
Huansheng Song ◽  
Shuyu Wang ◽  
Jinliang Kong
Keyword(s):  
Background Subtraction ◽  
Three Dimensional ◽  
Vehicle Detection ◽  
Reconstruction Algorithm ◽  
Gaussian Mixture ◽  
Detection Accuracy ◽  
License Plate ◽  
Surveillance Systems ◽  
Subtraction Method ◽  
2D Images

Vehicle detection is one of the important technologies in intelligent video surveillance systems. Owing to the perspective projection imaging principle of cameras, traditional two-dimensional (2D) images usually distort the size and shape of vehicles. In order to solve these problems, the traffic scene calibration and inverse projection construction methods are used to project the three-dimensional (3D) information onto the 2D images. In addition, a vehicle target can be characterized by several components, and thus vehicle detection can be fulfilled based on the combination of these components. The key characteristics of vehicle targets are distinct during a single day; for example, the headlight brightness is more significant at night, while the vehicle taillight and license plate color are much more prominent in the daytime. In this paper, by using the background subtraction method and Gaussian mixture model, we can realize the accurate detection of target lights at night. In the daytime, however, the detection of the license plate and taillight of a vehicle can be fulfilled by exploiting the background subtraction method and the Markov random field, based on the spatial geometry relation between the corresponding components. Further, by utilizing Kalman filters to follow the vehicle tracks, detection accuracy can be further improved. Finally, experiment results demonstrate the effectiveness of the proposed methods.

scholarly journals AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1889
Author(s):  
Francisco Luna-Perejón ◽  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Anton Civit ◽  
Manuel Domínguez-Morales
Keyword(s):  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Life Activities ◽  
Learning Classifier ◽  
The People ◽  
Activity Types ◽  
Remote Monitoring Systems ◽  
Secondary Study

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.

scholarly journals Weak Interactions in Cocrystals of Isoniazid with Glycolic and Mandelic Acids

Crystals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 328
Author(s):  
Raquel Álvarez-Vidaurre ◽  
Alfonso Castiñeiras ◽  
Antonio Frontera ◽  
Isabel García-Santos ◽  
Diego M. Gil ◽  
...  
Keyword(s):  
Density Functional ◽  
Glycolic Acid ◽  
Weak Interactions ◽  
Three Dimensional ◽  
Functional Theory ◽  
Molecular Systems ◽  
X Ray Crystallography ◽  
Hydroxycarboxylic Acids ◽  
Non Covalent Interactions ◽  
Covalent Interactions

This work deals with the preparation of pyridine-3-carbohydrazide (isoniazid, inh) cocrystals with two α-hydroxycarboxylic acids. The interaction of glycolic acid (H2ga) or d,l-mandelic acid (H2ma) resulted in the formation of cocrystals or salts of composition (inh)·(H2ga) (1) and [Hinh]+[Hma]–·(H2ma) (2) when reacted with isoniazid. An N′-(propan-2-ylidene)isonicotinic hydrazide hemihydrate, (pinh)·1/2(H2O) (3), was also prepared by condensation of isoniazid with acetone in the presence of glycolic acid. These prepared compounds were well characterized by elemental analysis, and spectroscopic methods, and their three-dimensional molecular structure was determined by single crystal X-ray crystallography. Hydrogen bonds involving the carboxylic acid occur consistently with the pyridine ring N atom of the isoniazid and its derivatives. The remaining hydrogen-bonding sites on the isoniazid backbone vary based on the steric influences of the derivative group. These are contrasted in each of the molecular systems. Finally, Hirshfeld surface analysis and Density-functional theory (DFT) calculations (including NCIplot and QTAIM analyses) have been performed to further characterize and rationalize the non-covalent interactions.

scholarly journals A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari
Keyword(s):  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Point Of View ◽  
False Alarms ◽  
Battery Life ◽  
Area Network ◽  
Battery Lifetime ◽  
Detection Systems ◽  
Operational Aspects

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.

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