Accelerometer-based fall detection for smartphones

Aguiar, Bruno; Rocha, Tiago; Silva, Joana; Sousa, Inês

doi:10.1109/memea.2014.6860110

Cited by 108 publications

(92 citation statements)

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“…Currently methods using triaxial acceleration sensors are the main methods for motion and fall detection in older populations: 1) [10] 2) [11] 3) [12] Nyan and colleagues have developed a fall detector using a triaxial accelerometer (MEMS) to collect data. To collect signals use dictated wavelet transformation (DWT).…”

Section: Introductionmentioning

confidence: 99%

A Wearable Fall Detector for Elderly People

Musalek¹

2017

DAAAM Proceedings

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This Publication has to be referred as: Musalek, M[iroslav] (2017). AbstractFalls and their subsequent possible side effects are for elderly people and people with various illnesses, such as epilepsy, are present the main obstacle to an independent and more dignified life. Even for people living independently, falls are common occurrences. Proper use of a fall detector will not only add more security to the seniors, but will also allow for timely medical intervention that can be life-critical. One of the limitations of this wearable technology is its limited lifetime and the occurrence of possible errors during the data acquisition and processing process. A fall detector designed to be able to monitor the activity of elderly people and designed to make the necessary warnings to ensure their safety as a result of the fall. In addition, other options that are used for this purpose are compared. This paper summarizes the functionality, architecture and implementation of the system.

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Section: Introductionmentioning

confidence: 99%

A Wearable Fall Detector for Elderly People

Musalek¹

2017

DAAAM Proceedings

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“…Based on a smart phone and the third generation (3G) networks, Kau et al [19] proposed the fall accident detection and corresponding wide area rescue system. Aguiar et al [18] proposed a fall detection system based on smart phone. When detecting a fall event, the user location is tracked and notifications Short Message Service (SMS) and email are sent to a set of contacts.…”

Section: Evolution Of Sensorsmentioning

confidence: 99%

“…Based on Kinect, Stone et al [10] used DT to compute a confidence that a fall preceded on a ground event. Using an accelerometer built-in smartphone, Aguiar et al [18] chose DT for retrieving features and threshold information to detect a fall as well. Using accelerometer, Pierleoni et al [22] employed SVM to select acceleration thresholds.…”

Section: Changes Of Fall Detection Algorithmsmentioning

confidence: 99%

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New Advances and Challenges of Fall Detection Systems: A Survey

2018

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Falling, as one of the main harm threats to the elderly, has drawn researchers' attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.

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“…At present, fall detection methods based on triaxial acceleration sensors are the mainstream methods used in fall monitoring systems for older adults [2][3][4]. The threshold method is a fall recognition algorithm that is commonly used in such fall detection systems [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Body Falling Gesture Recognition Based on SOM and Triaxial Acceleration Information

Chen¹,

Gao²,

Feng³

et al. 2016

Proceedings of the 2016 International Conference on Communications, Information Management and Network Security

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Abstract-In order to improve the performance of fall detection system for the elderly based on triaxial acceleration sensor, and accurately to judge the fall direction of human body, a method was put forward based on self-organizing map neural network (SOM) and the information of triaxial acceleration sensor to cluster and analyze the human motion. To verify the recognition results of the SOM method, 130 samples of 13 common action including fall were participated in the SOM network testing. The results show that the sensitivity, specificity and accuracy of the new system were 90%, 96.7%, 94.6%, respectively. These results were better than those of the method of threshold value.

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Accelerometer-based fall detection for smartphones

Cited by 108 publications

References 0 publications

A Wearable Fall Detector for Elderly People

A Wearable Fall Detector for Elderly People

New Advances and Challenges of Fall Detection Systems: A Survey

Body Falling Gesture Recognition Based on SOM and Triaxial Acceleration Information

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