Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.

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Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors

Sensors ◽

10.3390/s19173713 ◽

2019 ◽

Vol 19 (17) ◽

pp. 3713

Author(s):

Federica Aprigliano ◽

Silvestro Micera ◽

Vito Monaco

Keyword(s):

Wearable Sensors ◽

Detection Algorithm ◽

Lower Limbs ◽

False Alarms ◽

Daily Life Activities ◽

Impact Detection ◽

Wearable Robots ◽

Adaptive Oscillators ◽

Young Subjects ◽

Smart Garments

This study aimed to investigate the performance of an updated version of our pre-impact detection algorithm parsing out the output of a set of Inertial Measurement Units (IMUs) placed on lower limbs and designed to recognize signs of lack of balance due to tripping. Eight young subjects were asked to manage tripping events while walking on a treadmill. An adaptive threshold-based algorithm, relying on a pool of adaptive oscillators, was tuned to identify abrupt kinematics modifications during tripping. Inputs of the algorithm were the elevation angles of lower limb segments, as estimated by IMUs located on thighs, shanks and feet. The results showed that the proposed algorithm can identify a lack of balance in about 0.37 ± 0.11 s after the onset of the perturbation, with a low percentage of false alarms (<10%), by using only data related to the perturbed shank. The proposed algorithm can hence be considered a multi-purpose tool to identify different perturbations (i.e., slippage and tripping). In this respect, it can be implemented for different wearable applications (e.g., smart garments or wearable robots) and adopted during daily life activities to enable on-demand injury prevention systems prior to fall impacts.

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Optimization of an Accelerometer and Gyroscope-Based Fall Detection Algorithm

Journal of Sensors ◽

10.1155/2015/452078 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 58

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.

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FAFNet: A False Alarm Filter Algorithm for License Plate Detection Based on Deep Neural Network

Traitement du signal ◽

10.18280/ts.380525 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1495-1501

Author(s):

Hui Huang ◽

Zhe Li

Keyword(s):

Neural Network ◽

False Alarm ◽

Real Time ◽

Large Scale ◽

Real Life ◽

Detection Algorithm ◽

False Alarms ◽

License Plate ◽

Embedded Devices ◽

License Plate Detection

The license plate detection technology has been widely applied in our daily life, but it encounters many challenges when performing license plate detection tasks in special scenarios. In this paper, a license plate detection algorithm is proposed for the problem of license plate detection, and an efficient false alarm filter algorithm, namely the FAFNet (False-Alarm Filter Network) is proposed for solving the problem of false alarms in license plate location scenarios in China. At first, this paper adopted the YOLOv5 target detection algorithm to detect license plates, and used the FAFNet to re-identify the images to avoid false detection. FAFNet is a lightweight convolutional neural network (CNN) that can solve the false alarm problem of real-time license plate recognition on embedded devices, and its performance is good. Next, this paper proposed a model generalization method for the purpose of making the proposed FAFNet be applicable to the license plate false alarm scenarios in other countries without the need to re-train the model. Then, this paper built a large-scale false alarm filter dataset, all samples in the dataset came from the industries and contained a variety of complex real-life scenarios. At last, experiments were conducted and the results showed that, the proposed FAFNet can achieve high-accuracy false alarm filtering and can run in real-time on embedded devices.

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REAL-LIFE VEHICLE ROUTING WITH TIME WINDOWS FOR VISUAL ATTRACTIVENESS AND OPERATIONAL ROBUSTNESS

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595912500170 ◽

2012 ◽

Vol 29 (04) ◽

pp. 1250017 ◽

Cited By ~ 6

Author(s):

B. L. HOLLIS ◽

P. J. GREEN

Keyword(s):

Vehicle Routing ◽

Search Algorithm ◽

Time Windows ◽

Real Life ◽

Second Phase ◽

Two Phase ◽

Guided Local Search ◽

Solution Approach ◽

Robust Solutions ◽

Phase Solution

This paper describes an algorithm for producing visually attractive and operationally robust solutions to a real-life vehicle routing problem with time windows. Visually attractive and operationally robust solutions are usually synonymous with compact, nonoverlapping routes with little or no intra-route cross over. The visual attractiveness of routes, for practical routing applications, is often of paramount importance. We present a two phase solution approach. The first phase is inspired by the sequential insertion algorithm of Solomon (1987) and includes a range of novel enhancements to ensure visually attractive solutions are produced in the face of a range of nonstandard real-life constraints: A constrained and heterogeneous vehicle fleet; tight time windows and banned delivery windows; multiple route capacity measures; driver breaks; minimum route volumes; vehicle-location compatibility rules; nonreturn to base routes; peak hour traveling times; vehicle type dependent service times; and replenishment back at the depot. The second phase is based on the guided local search algorithm of Kilby et al. (1999). It uses an augmented objective function designed to seek solutions which strike a balance between minimizing traditional cost measures, whilst maximizing the visual attractiveness of the solution. Our two phase solution approach is particularly adept at producing solutions that both aggressively minimize the total number of routes, a feature that we believe has been missing in algorithms presented in equivalent literature, as well as minimizing traditional cost measures whilst delivering a very high degree of visual attractiveness. The algorithm presented has been successfully implemented and deployed for the real-life, daily beverage distribution problem of Schweppes Australia Pty. Ltd. for a range of capital cities throughout Australia.

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Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

Journal of Sensors ◽

10.1155/2017/8241910 ◽

2017 ◽

Vol 2017 ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

Glen Debard ◽

Marc Mertens ◽

Toon Goedemé ◽

Tinne Tuytelaars ◽

Bart Vanrumste

Keyword(s):

False Alarm ◽

Detection System ◽

Real Life ◽

Fall Detection ◽

Training Data ◽

False Alarms ◽

Detection Systems ◽

Number Of False Alarms ◽

Real Life Data ◽

Precision Recall Curve

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

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Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm

Frontiers in Pediatrics ◽

10.3389/fped.2021.651356 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ahnjili ZhuParris ◽

Matthijs D. Kruizinga ◽

Max van Gent ◽

Eva Dessing ◽

Vasileios Exadaktylos ◽

...

Keyword(s):

Sound Source ◽

Real Life ◽

Detection Algorithm ◽

Training Dataset ◽

Validation Dataset ◽

Independent Dataset ◽

Audio Features ◽

Audio Clip ◽

Technical Validation ◽

Online Sources

Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying.Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm.Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone.Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings.

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Assessing the Feasibility of Augmenting Fall Detection Systems by Relying on UWB-Based Position Tracking and a Home Robot

Sensors ◽

10.3390/s20185361 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5361

Author(s):

Maurizio Capra ◽

Stefano Sapienza ◽

Paolo Motto Ros ◽

Alessio Serrani ◽

Maurizio Martina ◽

...

Keyword(s):

Emergency Response ◽

Detection System ◽

Tracking System ◽

Fall Detection ◽

Detection Algorithm ◽

Line Of Sight ◽

False Alarms ◽

Notification System ◽

Detection Systems ◽

Home Robot

Falls in the home environment are a primary cause of injury in older adults. According to the U.S. Centers for Disease Control and Prevention, every year, one in four adults 65 years of age and older reports experiencing a fall. A variety of different technologies have been proposed to detect fall events. However, the need to detect all fall instances (i.e., to avoid false negatives) has led to the development of systems marked by high sensitivity and hence a significant number of false alarms. The occurrence of false alarms causes frequent and unnecessary calls to emergency response centers, which are critical resources that should be utilized only when necessary. Besides, false alarms decrease the level of confidence of end-users in the fall detection system with a negative impact on their compliance with using the system (e.g., wearing the sensor enabling the detection of fall events). Herein, we present a novel approach aimed to augment traditional fall detection systems that rely on wearable sensors and fall detection algorithms. The proposed approach utilizes a UWB-based tracking system and a home robot. When the fall detection system generates an alarm, the alarm is relayed to a base station that utilizes a UWB-based tracking system to identify where the older adult and the robot are so as to enable navigating the environment using the robot and reaching the older adult to check if he/she experienced a fall. This approach prevents unnecessary calls to emergency response centers while enabling a tele-presence using the robot when appropriate. In this paper, we report the results of a novel fall detection algorithm, the characteristics of the alarm notification system, and the accuracy of the UWB-based tracking system that we implemented. The fall detection algorithm displayed a sensitivity of 99.0% and a specificity of 97.8%. The alarm notification system relayed all simulated alarm notification instances with a maximum delay of 106 ms. The UWB-based tracking system was found to be suitable to locate radio tags both in line-of-sight and in no-line-of-sight conditions. This result was obtained by using a machine learning-based algorithm that we developed to detect and compensate for the multipath effect in no-line-of-sight conditions. When using this algorithm, the error affecting the estimated position of the radio tags was smaller than 0.2 m, which is satisfactory for the application at hand.

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Firearm Recognition Using Convolutional Neural Network

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit195226 ◽

2019 ◽

pp. 136-141

Author(s):

T. H. Deepthi ◽

R. Gaayathri ◽

S. Shanthosh ◽

A. Sahaya Gebin ◽

R. Anitha Nithya

Keyword(s):

Response Times ◽

Real Life ◽

Detection Algorithm ◽

Automated Detection ◽

False Alarms ◽

Effective Response ◽

Number Of False Alarms ◽

Single Operator ◽

Zero Rate ◽

Housing Estates

Closed circuit television systems (CCTV) are becoming more popular and are being deployed in many offices, housing estates and in the most public spaces. Monitoring systems have been implemented in many foreign cities. This makes for an enormous load for the CCTV operators, as the number of camera views a single operator can monitor is limited by the human factors. The projects focus on the task of automated detection and recognition of dangerous situations for CCTV systems. We propose algorithms that are able to alert the human operator when a firearm is visible in the image and also have focussed on limiting the number of false alarms, in order to allow a real life application of the system. Managed to propose a version of a firearm detection algorithm that offers a near zero rate of false alarms and have shown that it is possible to create a system that are capable of an early warning in a dangerous situation, which may lead to faster and more effective response times and a reduction in the number of potential victims.

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A novel fuzzy logic algorithm for accurate fall detection of smart wristband

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219881578 ◽

2019 ◽

Vol 42 (4) ◽

pp. 786-794 ◽

Cited By ~ 3

Author(s):

Hongtao Zhang ◽

Muhannand Alrifaai ◽

Keming Zhou ◽

Huosheng Hu

Keyword(s):

Fuzzy Logic ◽

Fall Detection ◽

The Elderly ◽

Detection Algorithm ◽

High Rate ◽

False Alarms ◽

Fuzzy Logic Algorithm ◽

Wide Range ◽

Serious Injury ◽

Commercial Applications

Falling is a major cause of serious injury or even death for the elderly population. To improve the safety of elderly people, a wide range of wearable fall detection devices have been developed over recent years, such as smart watches, waistbands and other wearable fall detectors. However, most of these fall detection devices are threshold-based and have a high rate of false alarm. This paper presents a novel fuzzy logic fall detection algorithm used in smart wristbands to reduce false alarms and achieve accurate fall detection. Experiments have been conducted in our laboratory and the results show that the proposed algorithm can accurately distinguish fall events from non-fall daily activities such as walking, jumping, clapping, and so forth. It shows good potential for commercial applications.

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GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction

Journal of Healthcare Engineering ◽

10.1155/2020/8887340 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Wen-Yu Cai ◽

Jia-Hao Guo ◽

Mei-Yan Zhang ◽

Zhi-Xiang Ruan ◽

Xue-Chen Zheng ◽

...

Keyword(s):

Public Health Problem ◽

Fall Detection ◽

Detection Algorithm ◽

Gradient Boosting ◽

False Alarms ◽

Detection Accuracy ◽

Major Public Health Problem ◽

Actual Application ◽

Comprehensive Data ◽

Surveillance Cameras

Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.

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