Fall Detection Performance Research Articles

Fall detection using smart floor sensor and supervised learning.

Falls are a major risk for elderly people's health and independence. Fast and reliable fall detection systems can improve chances of surviving the accident and coping with its physical and psychological consequences. Recent research has come up with various solutions, all suffering from significant drawbacks, one of them being the intrusiveness into patient's life. This paper proposes a novel fall detection monitoring system based on a sensitive floor sensor made out of a piezoelectric material and a machine learning approach. The detection is done by a combination between a supervised Random Forest and an aggregation of its output over time. The database was made using acquisitions from 28 volunteers simulating falls and other behaviours. Unlike existent fall detection systems, our solution offers the advantages of having a passive sensor (no power supply is needed) and being completely unobtrusive since the sensor comes with the floor. Results are compared with state-of-the-art classification algorithms. On our database, good performance of fall detection was obtained with a True Positive Rate of 94.4% and a False Positive Rate of 2.4%.

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

A Hybrid Temporal Reasoning Framework for Fall Monitoring

This paper presents a real-time method for detecting a fall at different phases using a wireless tri-axial accelerometer and reports the classification performance when the sensor is placed on different body parts. The proposed hybrid framework combines a rule-based knowledge representation scheme with a time control mechanism and machine-learning-based activity classification. Real-time temporal reasoning is performed using a standard rule-based inference engine. The framework is validated for fall detection performance, false alarm evaluation, and comparison with a highly cited baseline method. Based on a data set with 14 fall types (280 falls) collected from 16 subjects, the highest accuracy values of 86.54%, 87.31%, and 91.15% are obtained for fall detection at pre-impacts, impacts, and post-impacts, respectively. Without post-impact activity information, the side of the waist and chest are the best sensor positions, followed by the head, front of the waist, wrist, ankle, thigh, and upper arm. With post-impact activity information, the best sensor position is the side of the waist, followed by the head, wrist, front of the waist, thigh, chest, ankle, and upper arm. Most false alarms occur during transitions of lying postures. The proposed method is more robust to a variety of fall and activity types and yields better classification performance and false alarm rates compared with the baseline method. The results provide guidelines for sensor placement when developing a fall monitoring system.

SisFall: A Fall and Movement Dataset.

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.

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.

A High Reliability Wearable Device for Elderly Fall Detection

Falls are critical events among elderly people that requires timely rescue. In this paper, we propose a fall detection system consisting of an inertial unit that includes triaxial accelerometer, gyroscope, and magnetometer with efficient data fusion and fall detection algorithms. Starting from the raw data, the implemented orientation filter provides the correct orientation of the subject in terms of yaw, pitch, and roll angles. The system is tested according to experimental protocols, engaging volunteers who performed simulated falls, simulated falls with recovery, and activities of daily living. By placing our wearable sensor on the waist of the subject, the unit is able to achieve fall detection performance above those of similar systems proposed in literature. The results obtained through commonly adopted protocols show excellent accuracy, sensitivity and specificity, improving the results of other techniques proposed in the literature.

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.

FALL DETECTION USING THREE WEARABLE TRIAXIAL ACCELEROMETERS AND A DECISION-TREE CLASSIFIER

Unintentional falls cause serious health problem and high medical cost, particularly among the elders. Efficient fall detection can ensure fallen subjects with timely rescue, less pain and lower health-care expense. However, the accuracy of the present fall detection system with single accelerometer does not meet the requirement of practical application. In this paper, a fall detection method using three wearable triaxial accelerometers and a decision-tree classifier is proposed. The three triaxial accelerometers are, respectively mounted on the head, the waist and the ankle to capture the acceleration signals of human movement. A Kalman filter is adopted to estimate the body tilt angle. After the features are extracted, the trained decision-tree model is used to predict the fall. The efficiency improvement is evidenced by the scripted and unscripted lateral fall experiments, involving five young healthy volunteers (three males and two females; age: 23.3 ± 1 years). The classification of fall and activities of daily living (ADL) achieve recall, precision and F-value of 93.1%, 95.9%, and 94.5%, respectively, and the system detects all falls during the extended unscripted trials. The experimental results indicate that the complementary movement information coming from three accelerometers can enhance the performance of fall detection. The proposed method is efficient, and it has remarkable improvements in comparison to the method of using one or two accelerometers.

Efficient Source Separation Algorithms for Acoustic Fall Detection Using a Microsoft Kinect

Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

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.

A Dynamic Evidential Network for Fall Detection

This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user’s trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Posture Recognition Based Fall Detection System

In this paper, we introduce a video-based fall detection system for an elderly person in a realhome environment. We extract global (ellipse) and local (shape context) features from static postures and an improved Directed Acyclic Graphic Support Vector Machine (DAGSVM) is applied for posture classification. After classifying different postures, certain rules are set to detect falls. This fall detection system is shown by evaluation on real datasets to achieve a good fall detection performance.

A Framework for Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Accelerometer Signals

As an essential branch of context awareness, activity awareness, especially daily activity monitoring and fall detection, is important to healthcare for the elderly and patients with chronic diseases. In this paper, a framework for activity awareness using surface electromyography and accelerometer (ACC) signals is proposed. First, histogram negative entropy was employed to determine the start- and end-points of static and dynamic active segments. Then, the angle of each ACC axis was calculated to indicate body postures, which assisted with sorting dynamic activities into two categories: dynamic gait activities and dynamic transition ones, by judging whether the pre- and post-postures are both standing. Next, the dynamic gait activities were identified by the double-stream hidden Markov models. Besides, the dynamic transition activities were distinguished into normal transition activities and falls by resultant ACC amplitude. Finally, a continuous daily activity monitoring and fall detection scheme was performed with the recognition accuracy over 98%, demonstrating the excellent fall detection performance and the great feasibility of the proposed method in daily activities awareness.

Automatic individual calibration in fall detection – an integrative ambulatory measurement framework

The objective of the current study was to demonstrate the utility of a new integrative ambulatory measurement (IAM) framework by developing and evaluating an individual calibration function in fall detection application. Ten healthy elderly persons were involved in a laboratory study and tested in a protocol comprising various types of activities of daily living and slip-induced backward falls. Inertial measurement units attached to the trunk and thigh segments were used to measure trunk angular kinematics and thigh accelerations. The effect of individual calibration was evaluated with previously developed fall detection algorithm. The results indicated that with individual calibration, the fall detection performance achieved approximately the same level of sensitivity (100% vs. 100%) and specificity (95.25% vs. 95.65%); however, response time was significantly lower than without (249 ms vs. 255 ms). It was concluded that the automatic individual calibration using the IAM framework improves the performance of fall detection, which has a greater implication in preventing/minimising injuries associated with fall accidents.

A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration

Falling is an important problem in the health maintenance of people above middle age. Portable accelerometer systems have been designed to detect falls. However, false alarms induced by some dynamic motions, such as walking and jumping, are difficult to avoid. Acceleration cross-product (AC)-related methods are proposed and examined by this study to seek solutions for detecting falls with less motion-evoked false alarms. A set of tri-axial acceleration data is collected during simulated falls, posture transfers and dynamic activities by wireless sensors for making methodological comparisons. The performance of fall detection is evaluated in aspects of parameter comparison, threshold selection, sensor placement and post-fall posture (PP) recruitment. By parameter comparison, AC leads to a larger area under the receiver operating characteristic (ROC) curve than acceleration magnitude (AM). Three strategies of threshold selection, for 100% sensitivity (Sen100), for 100% specificity (Spe100) and for the best sum (BS) of sensitivity and specificity, are evaluated. Selecting a threshold based on Sen100 and BS leads to more practicable results. Simultaneous data recording from sensors in the chest and waist is performed. Fall detection based on the data from the chest shows better global accuracy. PP recruitment leads to lower false alarm ratios (FR) for both AC- and AM-based methods.