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 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 AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1889
Author(s):  
Francisco Luna-Perejón ◽  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Anton Civit ◽  
Manuel Domínguez-Morales
Keyword(s):  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Life Activities ◽  
Learning Classifier ◽  
The People ◽  
Activity Types ◽  
Remote Monitoring Systems ◽  
Secondary Study

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.

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.

Using a Smartphone as a Track and Fall Detector

2015 ◽  
pp. 272-295
Author(s):  
Chia-Yin Ko ◽  
Fang-Yie Leu ◽  
I-Tsen Lin
Keyword(s):  
Social Networks ◽  
Emergency Medical Services ◽  
Real Time ◽  
Family Caregivers ◽  
Fall Detection ◽  
The Elderly ◽  
Elderly Individual ◽  
Communication Services ◽  
People With Dementia

This chapter proposes a smartphone-based system for both indoor and outdoor monitoring of people with dementia. The whole system comprises wandering detection, safety-zone monitoring, fall detection, communication services, alert notifications, and emergency medical services. To effectively track the elderly, the proposed system uses a smartphone camera to take real-time pictures along the user's path as he or she moves about. Those photos, accompanied with time and GPS signals, are delivered to and stored on the Cloud system. When necessary, family caregivers can download those data to quickly find a way to help the elderly individual. Additionally, this study uses tri-axial accelerometers to examine falls. To assure individuals' data is safeguarded appropriately, an RSA method has been adopted by the system to encrypt stored data. This reliable and minimally intrusive system provides people with dementia with an opportunity to maintain their social networks and to improve their quality of lives.

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.

Using a Smartphone as a Track and Fall Detector

2016 ◽  
pp. 922-944
Author(s):  
Chia-Yin Ko ◽  
Fang-Yie Leu ◽  
I-Tsen Lin
Keyword(s):  
Social Networks ◽  
Emergency Medical Services ◽  
Real Time ◽  
Family Caregivers ◽  
Fall Detection ◽  
The Elderly ◽  
Elderly Individual ◽  
Communication Services ◽  
People With Dementia

This chapter proposes a smartphone-based system for both indoor and outdoor monitoring of people with dementia. The whole system comprises wandering detection, safety-zone monitoring, fall detection, communication services, alert notifications, and emergency medical services. To effectively track the elderly, the proposed system uses a smartphone camera to take real-time pictures along the user's path as he or she moves about. Those photos, accompanied with time and GPS signals, are delivered to and stored on the Cloud system. When necessary, family caregivers can download those data to quickly find a way to help the elderly individual. Additionally, this study uses tri-axial accelerometers to examine falls. To assure individuals' data is safeguarded appropriately, an RSA method has been adopted by the system to encrypt stored data. This reliable and minimally intrusive system provides people with dementia with an opportunity to maintain their social networks and to improve their quality of lives.

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.

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