Combining novelty detectors to improve accelerometer-based fall detection

Medrano, Carlos; Igual, Raúl; García‐Magariño, Iván; Plaza, Inmaculada; Azuara, Guillermo

doi:10.1007/s11517-017-1632-z

Cited by 22 publications

(16 citation statements)

References 29 publications

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“…Bourke et al (2007) also found that accelerometers are regarded as the most popular sensors for fall detection mainly due to its affordable cost, easy installation and relatively good performance. Although smartphones have gained attention for studying falls, the underlying sensors of systems using them are still accelerometers and gyroscopes (Shi et al, 2016;Islam et al, 2017;Medrano et al, 2017;Chen et al, 2018). Users are more likely to carry smartphones all day rather than extra wearable devices, so smartphones are useful for eventual real-world deployments (Zhang et al, 2006;Dai et al, 2010).…”

Section: Individual Wearable Sensorsmentioning

confidence: 99%

“…Typical data storage devices include SD cards, local storage on the integration device, or remote storage on the cloud. For example, some studies used the camera and accelerometer in smartphones, and stored the data on the local storage of the smarphones (Ozcan and Velipasalar, 2016;Shi et al, 2016;Medrano et al, 2017). Other studies applied off-line methods and stored data in their own computer, and could be processed at a later stage.…”

Section: Data Storage and Analysismentioning

confidence: 99%

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Elderly Fall Detection Systems: A Literature Survey

2020

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Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

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Section: Individual Wearable Sensorsmentioning

confidence: 99%

Section: Data Storage and Analysismentioning

confidence: 99%

Elderly Fall Detection Systems: A Literature Survey

2020

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“…For heterogeneous fusion-based approach, the combination of threshold and Non-threshold method have been put forward to distinguish or predict falls. Currently, multiple combination strategies, such as combination of threshold and multiple kernel learning SVM [10], or threshold and kernel density estimation [48], etc. were proposed to reduce false alarms.…”

Section: ) Fusion-based Fall Detection and Fall Preventionmentioning

confidence: 99%

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

2019

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Falls are abnormal activity events that occur infrequently; however, they are serious health problems among elderly individuals. With the advancements of technologies, falls have been widely studied by scientific researchers to minimize serious consequences and negative impacts. Fall detection and fall prevention are two strategies to tackle fall issues with a variety of sensing techniques and classifier models. Currently, many reviews on fall-related technologies have been presented and analyzed; however, most of them give surveys on the subfield of fall-related systems, while others are not extensive and comprehensive reviews. In fact, the latest researches have a new trend of fusion-based methods to improve the performance of the fall-related systems based on a combination of different sensors or classifier models. Adaptive threshold and radio frequency-based systems are also researched and proposed recently, which are seldom mentioned in other reviews. Therefore, a global taxonomy for current fall-related studies from four aspects, including current literature reviews, fall detection, and prevention systems based on different sensor apparatus and analytic algorithm, low power techniques, and sensor placements for fall-related systems are conducted in this paper. Several research challenges and issues in the fall-related field are also discussed and analyzed. The objective of this review paper is to conclude and provide a good position of current fall-related studies to inspire researchers in this field. INDEX TERMSAdaptive algorithm, classification algorithms, fall detection, fall prevention, low power techniques, sensing techniques. LINGMEI REN received the Ph.D. degree from the

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“…These methods are based upon traditional techniques for the classi cation of data. Some famous analytical techniques for data processing are: Thresh-Holding [18] [19], Fuzzy Logic [20], Hidden Markov model [21], and Bayesian Filtering [22]. All these techniques are used to classify the falls from non-falls.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

Hussain

Umair

Ehatisham-ul-Haq

et al. 2020

Preprint

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Abstract Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.

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Combining novelty detectors to improve accelerometer-based fall detection

Cited by 22 publications

References 29 publications

Elderly Fall Detection Systems: A Literature Survey

Elderly Fall Detection Systems: A Literature Survey

Research of Fall Detection and Fall Prevention Technologies: A Systematic Review

An Efficient Machine Learning-based Elderly Fall Detection Algorithm

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