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 Consumption Analysis of Smartphone based Fall Detection Systems with Multiple External Wireless Sensors

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 622
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari
Keyword(s):  
Wireless Sensors ◽  
Inertial Sensors ◽  
Cost Effective ◽  
Fall Detection ◽  
Monitoring Point ◽  
Research Attention ◽  
Battery Lifetime ◽  
Detection Systems ◽  
Wearable Systems ◽  
Multiple Sensing

Fall Detection Systems (FDSs) based on wearable technologies have gained much research attention in recent years. Due to the networking and computing capabilities of smartphones, these widespread personal devices have been proposed to deploy cost-effective wearable systems intended for automatic fall detection. In spite of the fact that smartphones are natively provided with inertial sensors (accelerometers and gyroscopes), the effectiveness of a smartphone-based FDS can be improved if it also exploits the measurements collected by small low-power wireless sensors, which can be firmly attached to the user’s body without causing discomfort. For these architectures with multiple sensing points, the smartphone transported by the user can act as the core of the FDS architecture by processing and analyzing the data measured by the external sensors and transmitting the corresponding alarm whenever a fall is detected. In this context, the wireless communications with the sensors and with the remote monitoring point may impact on the general performance of the smartphone and, in particular, on the battery lifetime. In contrast with most works in the literature (which disregard the real feasibility of implementing an FDS on a smartphone), this paper explores the actual potential of current commercial smartphones to put into operation an FDS that incorporates several external sensors. This study analyzes diverse operational aspects that may influence the consumption (as the use of a GPS sensor, the coexistence with other apps, the retransmission of the measurements to an external server, etc.) and identifies practical scenarios in which the deployment of a smartphone-based FDS is viable.

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 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 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 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.

Reading from the Black Box: What Sensors Tell Us about Resting and Recovery after Real-World Falls

Gerontology ◽  
2017 ◽  
Vol 64 (1) ◽  
pp. 90-95 ◽  
Author(s):  
Lars Schwickert ◽  
Jochen Klenk ◽  
Wiebren Zijlstra ◽  
Maxim Forst-Gill ◽  
Kim Sczuka ◽  
...  
Keyword(s):  
Real World ◽  
Inertial Sensors ◽  
Area Under The Curve ◽  
Fall Detection ◽  
Standing Position ◽  
Interquartile Range ◽  
Successful Recovery ◽  
Standing Up ◽  
Long Time ◽  
The Impact

Background: Lying on the floor for a long time after falls, regardless of whether an injury results, remains an unsolved health care problem. In order to develop efficient and acceptable fall detection and reaction approaches, it is relevant to improve the understanding of the circumstances and the characteristics of post-impact responses and the return or failure to return to pre-fall activities. Falls are seldom observed by others; until now, the knowledge about movement kinematics during falls and following impact have been anecdotal. Objective: This study aimed to analyse characteristics of the on-ground and recovery phases after real-world falls. The aim was to compare self-recovered falls (defined as returns to standing from the floor) and non-recovered falls with long lies. Methods and Participants: Data from subjects in different settings and of different populations with high fall risk were included. Real-world falls collected by inertial sensors worn on the lower back were taken from the FARSEEING database if reliable information was available from fall reports and sensor signals. Trunk pitch angle and acceleration were analysed to describe different patterns of recovery movements while standing up from the floor after the impact of a fall. Results: Falls with successful recovery, where an upright posture was regained, were different from non-recovered falls in terms of resting duration (median 10.5 vs. 34.5 s, p = 0.045). A resting duration longer than 24.5 s (area under the curve = 0.796) after the fall impact was a predictor for the inability to recover to standing. Successful recovery to standing showed lower cumulative angular pitch movement than attempted recovery in fallers that did not return to a standing position (median = 76°, interquartile range 24-170° vs. median = 308°, interquartile range 30-1,209°, p = 0.06). Conclusion: Fall signals with and without successful returns to standing showed different patterns during the phase on the ground. Characteristics of real-world falls provided through inertial sensors are relevant to improve the classification and the sensing of falls. The findings are also important for redesigning emergency response processes after falls in order to better support individuals in case of an unrecovered fall. This is crucial for preventing long lies and other fall-related incidents that require an automated fall alarm.

scholarly journals UniMiB AAL: An Android Sensor Data Acquisition and Labeling Suite

2018 ◽  
Vol 8 (8) ◽  
pp. 1265 ◽  
Author(s):  
Davide Ginelli ◽  
Daniela Micucci ◽  
Marco Mobilio ◽  
Paolo Napoletano
Keyword(s):  
Daily Living ◽  
Ad Hoc ◽  
Inertial Sensors ◽  
Sensor Data ◽  
Controlled Environment ◽  
The Subject ◽  
Android Applications ◽  
Health Support ◽  
The Many ◽  
Fitness Trackers

In recent years, research on techniques to identify and classify activities of daily living (ADLs) has significantly grown. This is justified by the many application domains that benefit from the application of these techniques, which span from entertainment to health support. Usually, human activities are classified by analyzing signals that have been acquired from sensors. Inertial sensors are the most commonly employed, as they are not intrusive, are generally inexpensive and highly accurate, and are already available to the user because they are mounted on widely used devices such as fitness trackers, smartphones, and smartwatches. To be effective, classification techniques should be tested and trained with datasets of samples. However, the availability of publicly available datasets is limited. This implies that it is difficult to make comparative evaluations of the techniques and, in addition, that researchers are required to waste time developing ad hoc applications to sample and label data to be used for the validation of their technique. The aim of our work is to provide the scientific community with a suite of applications that eases both the acquisition of signals from sensors in a controlled environment and the labeling tasks required when building a dataset. The suite includes two Android applications that are able to adapt to both the running environment and the activities the subject wishes to execute. Because of its simplicity and the accuracy of the labeling process, our suite can increase the number of publicly available datasets.

scholarly journals Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5774
Author(s):  
Chih-Lung Lin ◽  
Wen-Ching Chiu ◽  
Ting-Ching Chu ◽  
Yuan-Hao Ho ◽  
Fu-Hsing Chen ◽  
...  
Keyword(s):  
Activities Of Daily Living ◽  
Daily Living ◽  
Inertial Sensors ◽  
Daily Life ◽  
Detection System ◽  
Fall Detection ◽  
Experimental Results ◽  
Triaxial Accelerometer ◽  
Timely Manner

This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer’s head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick’s filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.

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.

Suppressed, but Not Forgotten

2011 ◽  
Vol 70 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Beat Meier ◽  
Anja König ◽  
Samuel Parak ◽  
Katharina Henke
Keyword(s):  
Memory Trace ◽  
Thought Suppression ◽  
Test Phase ◽  
Indirect Test ◽  
Final Test ◽  
Test Experiment ◽  
And Control ◽  
Cue Words ◽  
The Impact

This study investigates the impact of thought suppression over a 1-week interval. In two experiments with 80 university students each, we used the think/no-think paradigm in which participants initially learn a list of word pairs (cue-target associations). Then they were presented with some of the cue words again and should either respond with the target word or avoid thinking about it. In the final test phase, their memory for the initially learned cue-target pairs was tested. In Experiment 1, type of memory test was manipulated (i.e., direct vs. indirect). In Experiment 2, type of no-think instructions was manipulated (i.e., suppress vs. substitute). Overall, our results showed poorer memory for no-think and control items compared to think items across all experiments and conditions. Critically, however, more no-think than control items were remembered after the 1-week interval in the direct, but not in the indirect test (Experiment 1) and with thought suppression, but not thought substitution instructions (Experiment 2). We suggest that during thought suppression a brief reactivation of the learned association may lead to reconsolidation of the memory trace and hence to better retrieval of suppressed than control items in the long term.

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