scholarly journals Smart Wearables with Sensor Fusion for Fall Detection in Firefighting

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6770
Author(s):  
Xiaoqing Chai ◽  
Renjie Wu ◽  
Matthew Pike ◽  
Hangchao Jin ◽  
Wan-Young Chung ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Fall Detection ◽  
The Elderly ◽  
Motion Sensors ◽  
Building Structures ◽  
Body Parts ◽  
Detection Systems ◽  
Measurement Units ◽  
The Impact ◽  
Smart Wearable

During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter’s personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter’s fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively.

scholarly journals A Study of the Use of Gyroscope Measurements in Wearable Fall Detection Systems

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 649 ◽  
Author(s):  
Eduardo Casilari ◽  
Moisés Álvarez-Marco ◽  
Francisco García-Lagos
Keyword(s):  
Inertial Sensors ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Systems ◽  
Combined Application ◽  
Measurement Units ◽  
Public Dataset ◽  
Cost Efficient ◽  
Wide Group

Due to the serious impact of falls on the quality of life of the elderly and on the economical sustainability of health systems, the study of new monitoring systems capable of automatically alerting about falls has gained much research interest during the last decade. In the field of Human Activity Recognition, Fall Detection Systems (FDSs) can be contemplated as pattern recognition architectures able to discriminate falls from ordinary Activities of Daily Living (ADLs). In this regard, the combined application of cellular communications and wearable devices that integrate inertial sensors offers a cost-efficient solution to track the user mobility almost ubiquitously. Inertial Measurement Units (IMUs) typically utilized for these architectures, embed an accelerometer and a gyroscope. This paper investigates if the use of the angular velocity (captured by the gyroscope) as an input feature of the movement classifier introduces any benefit with respect to the most common case in which the classification decision is uniquely based on the accelerometry signals. For this purpose, the work assesses the performance of a deep learning architecture (a convolutional neural network) which is optimized to differentiate falls from ADLs as a function of the raw data measured by the two inertial sensors (gyroscope and accelerometer). The system is evaluated against on a well-known public dataset with a high number of mobility traces (falls and ADL) measured from the movements of a wide group of experimental users.

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.

Effective Falls Detection Method Using Two Tri-Axial Accelerometers

2013 ◽  
Vol 647 ◽  
pp. 854-860
Author(s):  
Gye Rok Jeon ◽  
Young Jae Kim ◽  
Ah Young Jeon ◽  
Sang Hoon Lee ◽  
Jae Hyung Kim ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Detection Rate ◽  
Elderly Population ◽  
Detection Method ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Systems

Falls detection systems have been developed in recent years because falls are detrimental events that can have a devastating effect on health of the elderly population. Current fall detecting methods mainly employ accelerometer to discriminate falls from activities of daily living (ADL). However, this makes it difficult to distinguish real falls from certain fall-like activities such as jogging and jumping. In this paper, an accurate fall detection system was implemented using two tri-axial accelerometers. By attaching the accelerometers on the chest and the abdomen, our system can effectively differentiate between falls and non-fall events.The Diff_Z and Sum_diff_Z parameter resulted in falls detection rate of 100%, respectively.

scholarly journals Possible Life Saver: A Review on Human Fall Detection Technology

Robotics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 55 ◽  
Author(s):  
Zhuo Wang ◽  
Vignesh Ramamoorthy ◽  
Udi Gal ◽  
Allon Guez
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Severe Injuries ◽  
Detection Systems ◽  
Detection Technology ◽  
Ambient Systems ◽  
Measure Of Performance ◽  
Privacy Issues ◽  
Current Systems

Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

Design and Test of MEMS Attitude Measurement Unit for Fall Detection

2011 ◽  
Vol 483 ◽  
pp. 465-470 ◽  
Author(s):  
Xiao Yan Liu ◽  
Zhao Ying Zhou ◽  
Wei Xiong
Keyword(s):  
Measurement Error ◽  
Data Storage ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Measurement Unit ◽  
Attitude Measurement ◽  
Test Results ◽  
Measurement Units ◽  
Serious Injury

Fall is a risky event in the elderly people’s daily life, it often cause serious injury both in physiology and psychology. A MEMS attitude measurement system is designed for fall detection in real time. This paper presents the design and error test of the attitude measurement unit. Each unit contains orthogonally mounted triads of accelerometers, magnetometers and gyros. With an integrated microcontroller for attitude calculating and flash for data storage, the size of the unit is 32mm×23mm×12mm. An extended Kalman filter based on quaternions is designed for attitude measurement. The digital angle output rate is 100Hz. A new method based on coordinate transformation for attitude measurement error test is introduced, using a single axis turntable and a fixed angle wedge. Theory of the testing method is presented and test experiments are performed. Test results show that attitude measurement error is less than 2°, which meets the requirement of fall detection precision. The fall detection system consists of five attitude measurement units fixed on the human legs and waist.

scholarly journals Fall-Detection Algorithm Using 3-Axis Acceleration: Combination with Simple Threshold and Hidden Markov Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Dongha Lim ◽  
Chulho Park ◽  
Nam Ho Kim ◽  
Sang-Hoon Kim ◽  
Yun Seop Yu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Daily Living ◽  
Hidden Markov ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Threshold Method ◽  
Simple Method ◽  
Detection Systems

Falls are a serious medical and social problem among the elderly. This has led to the development of automatic fall-detection systems. To detect falls, a fall-detection algorithm that combines a simple threshold method and hidden Markov model (HMM) using 3-axis acceleration is proposed. To apply the proposed fall-detection algorithm and detect falls, a wearable fall-detection device has been designed and produced. Several fall-feature parameters of 3-axis acceleration are introduced and applied to a simple threshold method. Possible falls are chosen through the simple threshold and are applied to two types of HMM to distinguish between a fall and an activity of daily living (ADL). The results using the simple threshold, HMM, and combination of the simple method and HMM were compared and analyzed. The combination of the simple threshold method and HMM reduced the complexity of the hardware and the proposed algorithm exhibited higher accuracy than that of the simple threshold method.

scholarly journals A study on the impact of the users’ characteristics on the performance of wearable fall detection systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Antonio Santoyo-Ramón ◽  
Eduardo Casilari-Pérez ◽  
José Manuel Cano-García
Keyword(s):  
Daily Living ◽  
Inertial Sensors ◽  
Fall Detection ◽  
Test Phase ◽  
Controlled Environment ◽  
Discrimination Capability ◽  
Detection Systems ◽  
Activity Types ◽  
Experimental Subjects ◽  
The Impact

AbstractWearable Fall Detection Systems (FDSs) have gained much research interest during last decade. In this regard, Machine Learning (ML) classifiers have shown great efficiency in discriminating falls and conventional movements or Activities of Daily Living (ADLs) based on the analysis of the signals captured by transportable inertial sensors. Due to the intrinsic difficulties of training and testing this type of detectors in realistic scenarios and with their target audience (older adults), FDSs are normally benchmarked against a predefined set of ADLs and emulated falls executed by volunteers in a controlled environment. In most studies, however, samples from the same experimental subjects are used to both train and evaluate the FDSs. In this work, we investigate the performance of ML-based FDS systems when the test subjects have physical characteristics (weight, height, body mass index, age, gender) different from those of the users considered for the test phase. The results seem to point out that certain divergences (weight, height) of the users of both subsets (training ad test) may hamper the effectiveness of the classifiers (a reduction of up 20% in sensitivity and of up to 5% in specificity is reported). However, it is shown that the typology of the activities included in these subgroups has much greater relevance for the discrimination capability of the classifiers (with specificity losses of up to 95% if the activity types for training and testing strongly diverge).

scholarly journals Machine learning algorithms for fall detection using kinematic and heart rate parameters-a comprehensive analysis

2020 ◽  
Vol 9 (4) ◽  
pp. 772
Author(s):  
Anita Ramachandran ◽  
Adarsh Ramesh ◽  
Aditya Sukhlecha ◽  
Avtansh Pandey ◽  
Anupama Karuppiah
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Feature Selection ◽  
Fall Detection ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Detection Systems ◽  
Ensemble Techniques ◽  
The Impact

The application of machine learning techniques to detect and classify falls is a prominent area of research in the domain of intelligent assisted living systems. Machine learning (ML) based solutions for fall detection systems built on wearable devices use various sources of information such inertial motion units (IMU), vital signs, acoustic or channel state information parameters. Most existing research rely on only one of these sources; however, a need to do more experimenation to observe the efficiency of the ML classifiers while coupling features from diverse sources, was felt. In addition, fall detection systems based on wearable devices, require intelligent feature engineering and selection for dimensionality reduction, so as to reduce the computational complexity of the devices. In this paper we do a comprehensive performance analysis of ML classifiers for fall detection, on a dataset we collected. The analysis includes the impact of the following aspects on the performance of ML classifiers for fall detection: (i) using a combination of features from 2 sensors-an IMU sensor and a heart rate sensor, (ii) feature engineering and feature selection based on statistical methods, and (iii) using ensemble techniques for fall detection. We find that the inclusion of heart rate along with IMU sensor parameters improves the accuracy of fall detection. The conclusions from our experimentations on feature selection and ensemble analysis can serve as inputs for researchers designing wearable device-based fall detection systems.

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