Detect Fall Incidents Research Articles

Comparative Analysis of Machine Learning Algorithms for Classification of Environmental Sounds and Fall Detection

In recent years, number of elderly people in population has been increased because of the rapid advancements in the medical field, which make it necessary to take care of old people. Accidental fall incidents are life-threatening and can lead to the death of a person if first aid is not given to the injured person. Immediate response and medical assistance are necessary in case of accidental fall incidents to elderly people. The research community explored various fall detection systems to early detect fall incidents, however, still there exist numerous limitations of the systems such as using expensive sensors, wearable sensors that are hard to wear all the time, camera violates the privacy of person, and computational complexity. In order to address the above-mentioned limitations of the existing systems, we proposed a novel set of integrated features that consist of melcepstral coefficients, gammatone cepstral coefficients, and spectral skewness. We employed a decision tree for the classification performance of both binary problems and multi-class problems. We obtained an accuracy of 91.39%, precision of 96.19%, recall of 91.81%, and F1-score of 93.95%. Moreover, we compared our method with existing state-of-the-art methods and the results of our method are higher than other methods. Experimental results demonstrate that our method is reliable for use in medical centers, nursing houses, old houses, and health care provisions.

Risk Factors Preventing Immediate Fall Detection: A Study Using Zero-Inflated Negative Binomial Regression

PurposeFalls are the most common accidents in healthcare facilities, and timely intervention can have a positive effect on the hazards and trauma experienced by patients after a fall. This study determined the factors affecting the time taken to detect a fall. MethodsA total of 3,470 cases of falls reported through the Korea Patient Safety Reporting and Learning System were included in the analysis. A zero-inflated negative binomial regression method was used for this retrospective secondary data analysis study. ResultsThere were 537 patients whose falls were not detected immediately; the count model was used to predict risk factors that delayed fall detection. Women aged 60–69 years—compared to those below 60 years and an evening nursing shift, compared to a day shift—were identified as significant factors. The fall detection time of about 2,933 patients was zero; therefore, the logit model was applied to predict a patient's possibility of belonging to the group whose fall was detected immediately. Comparisons of tertiary hospitals with general hospitals and hospitals, of the evening shift with the day shift, and of the day shift with the night shift indicated significant influencing factors. ConclusionsThese findings can assist nurses in recognizing patient and hospital characteristics related to delayed fall detection. Strategies to improve patient safety in healthcare facilities that focus on patient characteristics such as age can be recommended. Furthermore, nurse staffing requires improvement to detect fall incidents immediately.

Automatic fall detection using region-based convolutional neural network

The common classifiers usually used to detect fall incidents depend on building and maintaining complex feature extraction for accurate machine learning tasks. However, these efforts have not succeeded in determining an ideal classifier or feature extraction for fall detection. In this research, we address the feature extraction problem along with the choice of the most appropriate classifier by using deep learning where the most prominent features are learned over the numerous layers of the network. More specifically, a general framework that relies on a faster region-based convolutional neural network was designed and developped to recognize the fall incidents. In particular, we designed three custom architectures and exploited transfer learning by using pre-trained networks such as the VGG-16 and AlexNet to overcome the fall detection challenge. The performance of the proposed networks showed the advantage of the pre-trained networks, where VGG-16 scored highest in those measures followed by AlexNet, the custom networks showed impressive results that were close to the pre-trained networks.

Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network

Automatic fall detection using radar aids in better assisted living and smarter health care. In this brief, a novel time series-based method for detecting fall incidents in human daily activities is proposed. A time series in the slow-time is obtained by summing all the range bins corresponding to fast-time of the ultra wideband radar return signals. This time series is used as input to the proposed deep convolutional neural network for automatic feature extraction. In contrast to other existing methods, the proposed fall detection method relies on multi-level feature learning directly from the radar time series signals. In particular, the proposed method utilizes a deep convolutional neural network for automating feature extraction as well as global maximum pooling technique for enhancing model discriminability. The performance of the proposed method is compared with that of the state-of-the-art, such as recurrent neural network, multi-layer perceptron, and dynamic time warping techniques. The results demonstrate that the proposed fall detection method outperforms the other methods in terms of higher accuracy, precision, sensitivity, and specificity values.

IMPLEMENTATION OF SMART MONITORING SYSTEM WITH FALL DECTECTOR FOR ELDERLY USING IOT TECHNOLOGY

Current global statistics shows that increasing number of elderly people live alone. Considering this unavoidable situation, a smart IoT system that can ease young family members to monitor their elderly family member from anywhere has been proposed. In this paper, the system uses a low-cost single board computer, named Raspberry Pi, with embedded webcam to perform 24 hours monitoring is demonstrated. A fall incident can be detected by a captured video that will be processed using an image processing technique. This fall detection is done by several basic activities; separating moving objects from the background, calculating the parameters for these areas and finally, fall detection itself. The fall detector is essential for elderly person monitoring since most of them suffer from chronic diseases and thus need more attention from their young family members. The system can also send notification to the user using social media application when detecting fall incidents in the monitoring area. Video captured by the system will be stored in cloud server, so that it can be used for any incident investigation in the future. By using the system, incidents such as death of elderly family members can be avoided by notifying fall incidents to family members that might be away from home.

Combined curvelets and hidden Markov models for human fall detection

Fall events detection is one of the most crucial issues in the health care of elderly people. This paper proposes an innovative approach for reliably detecting fall incidents based on human silhouette shape variation in vision monitoring. This mission is achieved by: (i) introducing the curvelet transform and area ratios for identifying human postures in images; (ii) reducing the feature vector dimension using differential evolution technique; (iii) identifying postures by a support vector machine, and (iv) adapting a hidden Markov model for classifying video sequences into non-fall and fall events. Experimental results are obtained on several "Fall Detection" datasets. For evaluation, several assessment measures are computed. These evaluation measures demonstrate the effectiveness of the proposed methodology when compared to some state-of-the-art approaches.

Social-aware Event Handling within the FallRisk Project.

With the uprise of the Internet of Things, wearables and smartphones are moving to the foreground. Ambient Assisted Living solutions are, for example, created to facilitate ageing in place. One example of such systems are fall detection systems. Currently, there exists a wide variety of fall detection systems using different methodologies and technologies. However, these systems often do not take into account the fall handling process, which starts after a fall is identified or this process only consists of sending a notification. The FallRisk system delivers an accurate analysis of incidents occurring in the home of the older adults using several sensors and smart devices. Moreover, the input from these devices can be used to create a social-aware event handling process, which leads to assisting the older adult as soon as possible and in the best possible way. The FallRisk system consists of several components, located in different places. When an incident is identified by the FallRisk system, the event handling process will be followed to assess the fall incident and select the most appropriate caregiver, based on the input of the smartphones of the caregivers. In this process, availability and location are automatically taken into account. The event handling process was evaluated during a decision tree workshop to verify if the current day practices reflect the requirements of all the stakeholders. Other knowledge, which is uncovered during this workshop can be taken into account to further improve the process. The FallRisk offers a way to detect fall incidents in an accurate way and uses context information to assign the incident to the most appropriate caregiver. This way, the consequences of the fall are minimized and help is at location as fast as possible. It could be concluded that the current guidelines on fall handling reflect the needs of the stakeholders. However, current technology evolutions, such as the uptake of wearables and smartphones, enables the improvement of these guidelines, such as the automatic ordering of the caregivers based on their location and availability.

Percentage of human-occupied areas for fall detection from two views

Falls are the major causes of fatal injury for the elderly population. To remedy this, several elderly people monitoring systems with fall detection functionality have been proposed. In this work, we investigate a video-based method of detecting fall incidents from multiple cameras. Our goal is to propose a novel method to detect falls on the floor with a multiple-camera system using the percentage of human-occupied areas. We suggest the use of two relatively orthogonal views to estimate the percentage of the surface of the person which is in contact with the ground according to the foreground information of each camera. These features are computed to differentiate by an automatic manner the lying on floor posture which can be considered as fall to other position such as standing up or sitting. This method is evaluated on a public multi-view fall detection dataset which contains videos of a healthy subject who performed 24 realistic scenarios. These scenarios show 22 fall events and 24 confounding events. The results of our experiments show that our proposed algorithm achieved 95.8 % sensitivity and 100 % specificity with less computational costs than state-of-the-art methods.

3D depth image analysis for indoor fall detection of elderly people

This paper presents a new fall detection method of elderly people in a room environment based on shape analysis of 3D depth images captured by a Kinect sensor. Depth images are pre-processed by a median filter both for background and target. The silhouette of moving individual in depth images is achieved by a subtraction method for background frames. The depth images are converted to disparity map, which is obtained by the horizontal and vertical projection histogram statistics. The initial floor plane information is obtained by V disparity map, and the floor plane equation is estimated by the least square method. Shape information of human subject in depth images is analyzed by a set of moment functions. Coefficients of ellipses are calculated to determine the direction of individual. The centroids of the human body are calculated and the angle between the human body and the floor plane is calculated. When both the distance from the centroids of the human body to the floor plane and the angle between the human body and the floor plane are lower than some thresholds, fall incident will be detected. Experiments with different falling direction are performed. Experimental results show that the proposed method can detect fall incidents effectively.

New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity.

Energy Saving in Forward Fall Detection using Mobile Accelerometer

Fall injury is one of the biggest risks to health and well-being of the elderly especially in independent living because falling accidents may cause instant death. There are many research interests aimed to detect fall incidents. Fall detection is envisioned critical on ICT-assisted healthcare future. In addition, mobile battery is currently another serious problem in which performance feasibility is considered as a standard to verify an effective method. In this paper, the authors study forward fall detection method from mobile phone perspective using accelerometer only without sacrificing accuracy to save energy. Using peak threshold algorithm in axes of mobile accelerometer, transition from activity of daily living (ADL) to forward fall event is recognized. In collected templates, Dynamic Time Warping (DTW) was applied to compute difference among them with new unlabeled samples. Results implemented on mobile phone easily show the feasibility of the method hence contribute significantly to fall detection in healthcare.

The Estimation of Heights and Occupied Areas of Humans from Two Orthogonal Views for Fall Detection

In this paper, we present a video-based method of detecting fall incidents of the elderly living alone. We propose using the measures of humans’ heights and occupied areas to distinguish three typical states of humans: standing, sitting, and lying. Two relatively orthogonal views are utilized, in turn, simplifying the estimation of occupied areas as the product of widths of the same person, observed in two cameras. However, the feature estimation based on sizes of silhouettes varies across the viewing window due to the camera perspective. To deal with it, we suggest using Local Empirical Templates (LET) that are dened as the sizes of standing people in local image patches. Two important characteristics of LET are: (1) LET in unknown scenes can be easily extracted by an automatic manner, and (2) by its nature, LET hold the perspective information that can be used for feature normalization. The normalization process is not only to cancel the perspective but also to take the features of standing people as the baselines. We realize that heights of standing people are greater than that of sitting and lying people. People in standing states also occupy smaller areas than whom in sitting and lying states. Thus, three humans’ states fall into three separable regions of the proposed feature space, composing of normalized heights and normalized occupied areas. Fall incidents can be inferred from time-series analysis of human state transition. We test the performance of our method on 24 video samples in Multi-view Fall Dataset

Compressed-domain Fall Incident Detection for Intelligent Homecare

This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. For object extraction, global motion parameters are estimated to distinguish local object motions from camera motions so as to obtain a rough object mask. We then perform change detection and/or background subtraction on the DC+2AC images extracted from the incoming coded bitstream to refine the object mask. Subsequently, an object clustering algorithm is used to automatically separate the individual video objects iteratively. After detecting the moving objects, compressed-domain features of each object are then extracted for identifying and locating fall incidents. Our experiments show that the proposed method can correctly detect fall incidents in real time.