scholarly journals Wearable Embedded Intelligence for Detection of Falls Independently of on-Body Location

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2426 ◽  
Author(s):  
José Alves ◽  
Joana Silva ◽  
Eduardo Grifo ◽  
Carlos Resende ◽  
Inês Sousa
Keyword(s):  
Sampling Rate ◽  
Fall Detection ◽  
The Elderly ◽  
Activity Level ◽  
False Alarms ◽  
Trade Off ◽  
Number Of False Alarms ◽  
Body Location ◽  
Common Problems ◽  
Reliable Solution

Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user’s waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level.

scholarly journals Identification of Soft Fall based on Falling State Occurrences

2021 ◽  
Vol 15 (3) ◽  
pp. 324-332
Author(s):  
Thein Gi Kyaw ◽  
Anant Choksuriwong ◽  
Nikom Suvonvorn
Keyword(s):  
Daily Living ◽  
Real Life ◽  
Fall Detection ◽  
The Elderly ◽  
Learning Model ◽  
Experimental Results ◽  
False Alarms ◽  
Detection Techniques ◽  
Number Of False Alarms ◽  
Acceleration Data

Fall detection techniques for helping the elderly were developed based on identifying falling states using simulated falls. However, some real-life falling states were left undetected, which led to this work on analysing falling states. The aim was to find the differences between active daily living and soft falls where falling states were undetected. This is the first consideration to be based on the threshold-based algorithms using the acceleration data stored in an activity database. This study addresses soft falls in addition to the general falls based on two falling states. Despite the number of false alarms being higher rising from 18.5% to 56.5%, the sensitivity was increased from 52% to 92.5% for general falls, and from 56% to 86% for soft falls. Our experimental results show the importance of state occurrence for soft fall detection, and will be used to build a learning model for soft fall detection.

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.

An intelligent emergency response system: preliminary development and testing of automated fall detection

2005 ◽  
Vol 11 (4) ◽  
pp. 194-198 ◽  
Author(s):  
Tracy Lee ◽  
Alex Mihailidis
Keyword(s):  
Emergency Response ◽  
Digital Camera ◽  
Random Order ◽  
Fall Detection ◽  
The Elderly ◽  
False Alarms ◽  
Emergency Response System ◽  
Response System ◽  
And Performance ◽  
Lying Down

We have designed an intelligent emergency response system to detect falls in the home. It uses image-based sensors. A pilot study was conducted using 21 subjects to evaluate the efficacy and performance of the fall-detection component of the system. Trials were conducted in a mock-up bedroom setting, with a bed, a chair and other typical bedroom furnishings. A small digital videocamera was installed in the ceiling at a height of approximately 2.6 m. The digital camera covered an area of approximately 5.0 m × 3.8 m. The subjects were asked to assume a series of postures, namely walking/standing, sitting/lying down in an inactive zone, stooping, lying down in a 'stretched' position, and lying down in a 'tucked' position. These five scenarios were repeated three times by each subject in a random order. These test positions totalled 315 tasks with 126 fall-simulated tasks and 189 non-fall-simulated tasks. The system detected a fall on 77% of occasions and missed a fall on 23%. False alarms occurred on only 5% of occasions. The results encourage the potential use of a vision-based system to provide safety and security in the homes of the elderly.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
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%.

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

2019 ◽  
Vol 42 (4) ◽  
pp. 786-794 ◽  
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.

Physical Activity Level and Motor Aptitude: Motor Domains and Identification Capacity of Brazilian Insufficiently Active Older Adults

2020 ◽  
Vol 13 ◽  
Author(s):  
Lucia Maria Andreis ◽  
Fernando de Aguiar Lemos ◽  
Lorenna Walesca de Lima Silva ◽  
Cassiana Luiza Pistorello Garcia ◽  
Gabrielli Veras ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Physical Activity Level ◽  
Area Under The Curve ◽  
The Elderly ◽  
Activity Level ◽  
General Motor ◽  
Body Scheme ◽  
Significant Difference ◽  
Global Coordination

Background: A decrease in the physical activity level in old age is common, which results in an increase in the number of falls and chronic conditions. Associated with that occurs the decline in motor skills as a result of the deficit in the interaction of cognitive and motor processes. Physical activity level can be associated differently with each motor domains. Objective: We analyzed the relationship between physical activity level and motor aptitude, and to identify which motor domains were most sensitive to detect insufficiently active level in older adults. Methods: Participated in the study 385 elderly people of both sexes. For the evaluation of the subjects were adopted the International Questionnaire on Physical Activity and the Motor Scale for Older Adults. Results: The majority of the elderly were active. In the comparison of motor aptitude between active and insufficiently active (IAC) elders a significant difference was found in the Global Coordination, Balance, Body Scheme and General Motor Aptitude. From the analysis of the area under the curve (AUC), we verified that these domains also were the ones that presented adequate diagnostic accuracy to identify IAC elderly. Besides that active elderly have presented the General Motor Aptitude classified within normality while the IAC below the normal. Conclusion: Our data suggest that IAC older adults present lower motor aptitude than the active elderly, especially in the domains of Global Coordination, Balance, Body Scheme and General Motor Aptitude, and that these domains were sensitive to indicate IAC older adults.

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 Physical inactivity as a determinant of the physical activity level in the elderly

2001 ◽  
Vol 25 (7) ◽  
pp. 935-939 ◽  
Author(s):  
EP Meijer ◽  
AHC Goris ◽  
L Wouters ◽  
KR Westerterp
Keyword(s):  
Physical Activity ◽  
Physical Activity Level ◽  
Physical Inactivity ◽  
The Elderly ◽  
Activity Level

Algorithms for detecting broadband signals with a constant level of false alarms

2021 ◽  
Author(s):  
I.F. Lozovskiy
Keyword(s):  
Detection Threshold ◽  
Signal Amplitude ◽  
Rayleigh Distribution ◽  
Training Sample ◽  
Detection Algorithm ◽  
Constant Level ◽  
False Alarms ◽  
Interference Power ◽  
Number Of False Alarms ◽  
Distribution Type

The use of broadband souding signals in radars, which has become real in recent years, leads to a significant reduction in the size of resolution elements in range and, accordingly, in the size of the window in which the training sample is formed, which is used to adapt the detection threshold in signal detection algorithms with a constant level of false alarms. In existing radars, such a window would lead to huge losses. The purpose of the work was to study the most rational options for constructing detectors with a constant level of false alarms in radars with broadband sounding signals. The problem was solved for the Rayleigh distribution of the envelope of the noise and a number of non-Rayleigh laws — Weibull and the lognormal, the appearance of which is associated with a decrease in the number of reflecting elements in the resolution volume. For Rayleigh interference, an algorithm is proposed with a multi-channel in range incoherent signal amplitude storage and normalization to the larger of the two estimates of the interference power in the range segments. The detection threshold in it adapts not only to the interference power, but also to the magnitude of the «power jump» in range, which allows reducing the number of false alarms during sudden changes in the interference power – the increase in the probability of false alarms did not exceed one order of magnitude. In this algorithm, there is a certain increase in losses associated with incoherent accumulation of signals reflected from target elements, and losses can be reduced by certain increasing the size of the distance segments that make up the window. Algorithms for detecting broadband signals against interference with non-Rayleigh laws of distribution of the envelope – Weibull and lognormal, based on the addition of the algorithm for detecting signals by non-linear transformation of sample counts into counts with a Rayleigh distribution, are studied. The structure of the detection algorithm remains unchanged in practice. The options for detectors of narrowband and broadband signals are considered. It was found that, in contrast to algorithms designed for the Rayleigh distribution, these algorithms provide a stable level of false alarms regardless of the values of the parameters of non-Rayleigh interference. To reduce losses due to interference with the distribution of amplitudes according to the Rayleigh law, detectors consisting of two channels are used, in which one of the channels is tuned for interference with the Rayleigh distribution, and the other for lognormal or Weibull interference. Channels are switched according to special distribution type recognition algorithms. In such detectors, however, there is a certain increase in the probability of false alarms in a rather narrow range of non-Rayleigh interference parameters, where their distribution approaches the Rayleigh distribution. It is shown that when using broadband signals, there is a noticeable decrease in detection losses in non-Rayleigh noise due to lower detection thresholds for in range signal amplitudes incoherent storage.

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