Improve Fall Detection Research Articles

Transformation in Elderly Care: Role of Machine Learning and Artificial Intelligence to Improve Fall Detection and Prevention in Long - Term Care Facilities

Transformation in Elderly Care: Role of Machine Learning and Artificial Intelligence to Improve Fall Detection and Prevention in Long - Term Care Facilities

Improved fall detection algorithm based on yolov8s

For the problem of physical injuries caused by untimely fall relief, this paper proposes an improved fall detection algorithm based on YOLOv8s.First, through many experiments, in the YOLOv8 algorithm model, select the YOLOv8s framework. Second, in the backbone part insert SE (Squeeze-and-Excitation) attention mechanism, strengthen the network feature extraction ability and adaptive ability, makes the network in the process of feature extraction network can pay more attention to the target, improve the network in the fall scene, improve the model accuracy. Lastly, Spatial and channel reconstruction convolution (SCConv) and C2f module are introduced into the network, reconstructed into C2f_SCConv module to integrate multi-scale features and reduce spatial and channel redundancy in the convolutional neural network, so as to improve the efficiency and accuracy and improve the representation ability of the model. After the improved YOLOv8s algorithm, the mAP increased from the initial 57.6% to 65.1%, which provided a good reference value for the subsequent study of the fall detection algorithm.

Improved Fall Detection Method Based on Local Elliptical Fitting of Human Skeletal Points

Improved Fall Detection Method Based on Local Elliptical Fitting of Human Skeletal Points

Transfer Learning on Small Datasets for Improved Fall Detection.

Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications.

Fall detection based on dynamic key points incorporating preposed attention.

Accidental falls pose a significant threat to the elderly population, and accurate fall detection from surveillance videos can significantly reduce the negative impact of falls. Although most fall detection algorithms based on video deep learning focus on training and detecting human posture or key points in pictures or videos, we have found that the human pose-based model and key points-based model can complement each other to improve fall detection accuracy. In this paper, we propose a preposed attention capture mechanism for images that will be fed into the training network, and a fall detection model based on this mechanism. We accomplish this by fusing the human dynamic key point information with the original human posture image. We first propose the concept of dynamic key points to account for incomplete pose key point information in the fall state. We then introduce an attention expectation that predicates the original attention mechanism of the depth model by automatically labeling dynamic key points. Finally, the depth model trained with human dynamic key points is used to correct the detection errors of the depth model with raw human pose images. Our experiments on the Fall Detection Dataset and the UP-Fall Detection Dataset demonstrate that our proposed fall detection algorithm can effectively improve the accuracy of fall detection and provide better support for elderly care.

Improving Fall Detection Devices for Older Adults Using Quality Function Deployment (QFD) Approach.

Engineering invention must be in tandem with public demands. Often it is difficult to identify the priorities of consumers where technological advancement is needed. In line with the global challenge of increasing fall prevalence among older adults, providing prevention solutions is the key. This study aims at developing an improved fall detection device using an approach called Quality Function Deployment (QFD). The goal is to investigate features to incorporate in existing device from consumer's perspectives. A three-phases design process is constructed; (1) Questionnaire, (2) Ishikawa Method, and (3) QFD. The proposed method begins with identifying customer needs as the requirement analysis, followed by a method to convert them to design specifications to be added in a fall detection device using QFD tool. As the top feature is monitoring balance, the new improved fall detection devices incorporating balance features will help older adults to monitor their level of risk of falling.

Virtual reality in training artificial intelligence-based systems: a case study of fall detection

Artificial Intelligent (AI) systems generally require training data of sufficient quantity and appropriate quality to perform efficiently. However, in many areas, such training data is simply not available or incredibly difficult to acquire. The recent developments in Virtual Reality (VR) have opened a new door for addressing this issue. This paper demonstrates the use of VR for generating training data for AI systems through a case study of human fall detection. Fall detection is a challenging problem in the public healthcare domain. Despite significant efforts devoted to introducing reliable and effective fall detection algorithms and enormous devices developed in the literature, minimal success has been achieved. The lack of recorded fall data and the data quality have been identified as major obstacles. To address this issue, this paper proposes an innovative approach to remove the afformentioned obstacle using VR technology. In this approach, a framework is, first, proposed to generate human fall data in virtual environments. The generated fall data is then tested with state-of-the-art visual-based fall detection algorithms to gauge its effectiveness. The results have indicated that the virtual human fall data generated using the proposed framework have sufficient quality to improve fall detection algorithms. Although the approach is proposed and verified in the context of human fall detection, it is applicable to other computer vision problems in different contexts, including human motion detection/recognition and self-driving vehicles.

Fall Detection System on Smart Walker Based on Multisensor Data Fusion and SPRT Method

Traditional walkers are commonly used for the elderly in social life, which solves the basic problem of walking, but it is difficult to ensure safety when a fall occurs, and the human-computer interaction is poor. The image recognition method or the IMU sensor method fixed on the user, such as a wearable watch, is used by most of the current fall detection methods. Wearable sensors require the user’s wearing operation, which is a little troublesome, and the detection accuracy is related to the way of wearing. The image recognition method requires a high-priced camera and a fixed installation position, which is unable to adapt to outdoor activities. We investigated a low-cost method of mounting the sensor on the body of the walker. We propose in this paper an improved fall detection method, namely Precondition and Limit Threshold SPRT (PLT-SPRT), and a novel fall detection system on the smart walker based on PLT-SPRT. The signals of the upper and lower limb sensors are fused based on the Kalman filter algorithm, and the admittance control parameters are obtained through the system identification method. In this study, the improved sequential probability ratio test algorithm is used to set the null hypothesis and the alternative hypothesis, construct the likelihood ratio and optimize the decision function, which is used to judge whether falls occur. The system is simulated by Matlab software, the user intention after fusion is more accurate, and the optimized decision function is judged accurately. Verified by the embedded system based on STM32 of the smart walker equipment in the real world, it can accurately identify the fallen state, with low detection delay, and the fallen state is detected about 160ms earlier than the traditional threshold-based detection algorithm, at the same time, the accuracy is higher than 94.9%, which meets the high real-time requirements of fall detection and is the ideal solution for smart walkers.

Time-Frequency Analysis of Daily Activities for Fall Detection

Fall events in elderly populations often result in serious injury and may lead to long-term disability and/or death. While many fall detection systems have been developed using wearable sensors to distinguish falls from other daily activities, detection sensitivity and specificity decreases when exposed to more rigorous activities such as running and jumping. This research uses time-frequency analysis of accelerometer-only activity data to develop a strategy for improving fall detection accuracy. In this study, a wireless sensor system (WSS) consisting of a three-axis accelerometer, microprocessor and wireless communications module is used to collect daily activities performed following a script in the laboratory setting. Experiments were conducted on 36 healthy human subjects performing four types of falls (i.e., forward, backward, and left/right sideway falls) as well as normal movements such as standing, walking, stand-to-sit, sit-to-stand, stepping, running and jumping. In total, 1227 different activities were collected and analyzed. The developed algorithm computes the magnitude of three-axis accelerometer data to detect if a critical fall threshold is passed, then analyzes the power spectral density within a critical fall duration window (500 ms) to differentiate fall events from other rigorous activities. Fall events were observed with high energy in the 2–3.5 Hz range and distinct from other rigorous activities such as running (3.5–5.5 Hz) and jumping (1–2 Hz). Preliminary results indicate the power spectral density (PSD)-based algorithm can detect falls with high sensitivity (98.4%) and specificity (98.6%) using lab-based daily activity data. The proposed algorithm has the benefit of improved accuracy over existing time-domain only strategies and multisensor strategies.

Adaptive directional bounding box from RGB-D information for improving fall detection

Fall detection for aging people is still a mainstream research focus for the current aging society. Tools that are simple and inexpensive but have high accuracy rates are needed. RGB-D information retrieved from a home entertainment system was used to detect falls using typical bounding boxes techniques. These techniques have limitations. This research introduced the Adaptive Directional Bounding Box that made use of a comprehensive bounding box and a dynamic state machine in a new way to detect falls. The proposed approach offered a way to track and analyze continuous data streams of the visual images to automatically predict a fall event prior to the fall state in a single-phase instead of the typical two-phases. This can significantly affect the survival or severe injury of the elderly. The proposed method can improve accuracy by 25.5% and the response time by 21.31% on average as compared to existing approaches.

Automatic Fall Detection using Smartphone Acceleration Sensor

In this paper, we describe our work on developing an automatic fall detection technique using smart phone. Fall is detected based on analyzing acceleration patterns generated during various activities. An additional long lie detection algorithm is used to improve fall detection rate while keeping false positive rate at an acceptable value. An application prototype is implemented on Android operating system and is used to evaluate the proposed technique performance. Experiment results show the potential of using this app for fall detection. However, more realistic experiment setting is needed to make this technique suitable for use in real life situations.

Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic

There are a significant number of high fall risk individuals who are susceptible to falling and sustaining severe injuries. An automatic fall detection and diagnostic system is critical for ensuring a quick response with effective medical aid based on relative information provided by the fall detection system. This article presents and evaluates an accelerometer-based multiple classifier fall detection and diagnostic system implemented on a single wearable Shimmer device for remote health monitoring. Various classifiers have been utilised within literature, however there is very little current work in combining classifiers to improve fall detection and diagnostic performance within accelerometer-based devices. The presented fall detection system utilises multiple classifiers with differing properties to significantly improve fall detection and diagnostic performance over any single classifier and majority voting system. Additionally, the presented multiple classifier system utilises comparator functions to ensure fall event consistency, where inconsistent events are outsourced to a supervisor classification function and discrimination power is considered where events with high discrimination power are evaluated to further improve the system response. The system demonstrated significant performance advantages in comparison to other classification methods, where the proposed system obtained over 99% metrics for fall detection recall, precision, accuracy and F-value responses.

Improving fall detection by the use of depth sensor and accelerometer

Since falls are a major cause of harm to older people, there is considerable demand for low-cost fall detection systems. To meet demands of the end-users we propose a new architecture for low cost and reliable fall detection, where an accelerometer is used to indicate a potential fall and the Kinect sensor is used to authenticate the eventual fall alert. In consequence, the depth maps are not processed frame-by-frame, but instead we download from a circular buffer a sequence of depth maps acquired prior to the fall and then process them to authenticate fall event. We determine features both in the depth maps and point clouds to extract discriminative fall descriptors. Since people typically follow typical motion patterns related to specific locations in home or typical daily activities, we propose to utilize k-nn classifier to implement an exemplar-based fall detector. We show that such a classifier is competitive on our publicly available URFD dataset in terms of sensitivity and specificity while being much more simple to implement on an embedded platform.

Fall Detection by Wearable Device Using Support Vector Machine

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.

Detecting falls using a fall indicator defined by a linear combination of kinematic measures

The objective of this study was to determine whether a novel fall detection model based on the statistical process control chart performed better when the fall indicator was defined by a linear combination of kinematic measures. To specify the fall indicator, an optimization procedure was performed in which the trial and error method was used to determine the relative weightings of the selected kinematic measures associated with the optimal fall detection performance. The highest sensitivity, highest specificity, and lowest sum of squared errors of the fall detection model obtained from this study were 97.3%, 99.2% and 0.00133 respectively. These findings suggested that using the fall indicator defined by a linear combination of kinematic measures can lead to improved fall detection performance compared to that defined by a single kinematic measure.

An Online One Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment

In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.