Challenges, issues and trends in fall detection systems

Igual, Raúl; Medrano, Carlos; Plaza, Inmaculada

doi:10.1186/1475-925x-12-66

Cited by 504 publications

(330 citation statements)

References 56 publications

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“…It should be noted that the detection rates provided by all these studies are very high. However, many authors on this field have noticed strong difficulties when comparing different acceleration-based studies [6,18]. This is due to the fact that each study uses its own dataset composed of simulated falls and ADL.…”

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confidence: 99%

A comparison of public datasets for acceleration-based fall detection

Igual

Medrano

Plaza

2015

Medical Engineering & Physics

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Falls are one of the leading causes of mortality among the older population, being the rapid detection of a fall a key factor to mitigate its main adverse health consequences. In this context, several authors have conducted studies on acceleration-based fall detection using external accelerometers or smartphones. The published detection rates are diverse, sometimes close to a perfect detector. This divergence may be explained by the difficulties in comparing different fall detection studies in a fair play since each study uses its own dataset obtained under different conditions. In this regard, several datasets have been made publicly available recently. This paper presents a comparison, to the best of our knowledge for the first time, of these public fall detection datasets in order to determine whether they have an influence on the declared performances. Using two different detection algorithms, the study shows that the performances of the fall detection techniques are affected, to a greater or lesser extent, by the specific datasets used to validate them. We have also found large differences in the generalization capability of a fall detector depending on the dataset used for training. In fact, the performance decreases dramatically when the algorithms are tested on a dataset different from the one used for training. Other characteristics of the datasets like the number of training samples also have an influence on the performance while algorithms seem less sensitive to the sampling frequency or the acceleration range.

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confidence: 99%

A comparison of public datasets for acceleration-based fall detection

Igual

Medrano

Plaza

2015

Medical Engineering & Physics

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“…Figure 2-1(below) shows the increase of the context-aware systems compared to wearables and smartphone based system [23]. The bar graph (associated with the left axis) shows the estimated percentage of studies published every year in relation to the total number of existing studies for each category (e.g., 43.3% of the existing smartphone-based studies were published in 2012) [23] A full System was developed to detect falls [5,8]. As shown in Figure 2-2 (below), the system includes the sensor data acquisition, manipulating, data reading, storage, display and communication.…”

Section: Rationalementioning

confidence: 99%

P1‐429: Adding Intelligence to a Floor‐Based Array Personnel Detector

Tyrer

Muheidat

2016

Alzheimer's & Dementia

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“…A fall detection system can be defined as an assistive device whose main objective is to alert when a fall event has occurred [3].…”

Section: Introductionmentioning

confidence: 99%

“…According to the existing literature, current fall detectors are sensors deployed in the environment such as cameras, floor sensors, infrared sensors, microphones and pressure sensors or miniature electronic sensor-based devices that are worn with or on top of clothing. However, sensors deployed in the environment are limited to those places where the sensors have been previously deployed and privacy is not a major concern [3]. On the other hand, the wide majority of wearable devices are in the form of accelerometers and gyroscopes, which have no other direct benefits for elderly health conditions; moreover, elderly are not willing to continuously wear a device that spies on them just because a fall may happen.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Results from a Proof of Concept Study for Fall Detection via ECG Morphology

Castaldo

Pecchia

2016

XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016

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Abstract-Falls are a major problem in later life. Early fall detection systems are increased over the years as undetected falls can have severe consequences for the fallers. Fall detection systems are based mainly on posture detection using accelerometers and gyroscopes. Alternatively, this study aims to understand if it is possible to detect posture changes using only electrocardiogram (ECG) morphology, which is significantly associated with moving from one position to another. This paper presents preliminary results of a feasibility study aiming to investigate at what extend it was possible to detect lying and standing position, using wearable devices to observe ECG morphology alterations. According to the literature, 29 ECG features were extracted. 11 healthy subjects (aged 19-36 years) were monitoring while lying down and standing up. ECG and accelerometer signals were recorded continually using a chest wearable monitoring device, the BioHarness M3 (ZephyrTech, NZ).Variations in the ECG features while the subjects lay down and stood up were analysed with the parametric statistical paired T-test. The results of the current study suggested that 4 ECG features were effective in detecting changes while lying down or standing up. Linear Discriminant Analysis (LDA) was used to generate a classifier based on these ECG features to detect automatically the changes while lying down or standing up with total classification accuracy, sensitivity and specificity rates of 77.3%, 81.8%, and 72.7% respectively. The results obtained from the current study support a preliminary proof of concept and pave the way to more complex studies aiming to detecting real falls using ECG variations.

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Challenges, issues and trends in fall detection systems

Cited by 504 publications

References 56 publications

A comparison of public datasets for acceleration-based fall detection

A comparison of public datasets for acceleration-based fall detection

P1‐429: Adding Intelligence to a Floor‐Based Array Personnel Detector

Preliminary Results from a Proof of Concept Study for Fall Detection via ECG Morphology

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