Smartphone-based Human Fall Detection System

Valcourt, Luis; Hoz, Yueng De La; Labrador, Miguel A.

doi:10.1109/tla.2016.7437252

Cited by 15 publications

(2 citation statements)

References 4 publications

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“…In the Post-fall it waits 1500ms, in which there is no activity and moves to the Activity-test, but if a new threshold peak is detected, it returns to the post-peak. In the Activity-test if it does not pass the test it returns to Sampling, but if it passes it is detected as a fall event (Valcourt et al, 2016).…”

Section: Threshold-based Fall Detection Methodsmentioning

confidence: 99%

Classification of Methods and Algorithms for Detection of Falls in Older Adults

2022

Proceedings of the 20th International Conference on E-Society (ES 2022) and 18th International Conference on Mobile Learning (M

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Approximately 30% of older adults fall at least once a year and 50% of that number will fall twice. Likewise, the number of falls that an older adult may suffer rises with increasing age. Falls have a high morbidity and mortality rate and are considered a major public health problem. It is estimated that 7% of hospital visits by older adults are the result of a fall and 40% of these require hospitalization. This article presents some of the main detection methods and algorithms used for fall detection and discusses their advantages and disadvantages. Each of these methods and algorithms directly or indirectly requires varying processing, connectivity, storage, and portability capabilities and provides varying degrees of accuracy. Based on this analysis, an experimental development for fall arrest in older adults will be proposed that aims to achieve 95% accuracy using a minimally invasive wearable sensor.

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Section: Threshold-based Fall Detection Methodsmentioning

confidence: 99%

Classification of Methods and Algorithms for Detection of Falls in Older Adults

2022

Proceedings of the 20th International Conference on E-Society (ES 2022) and 18th International Conference on Mobile Learning (M

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“…Here, a fall is reported when the acceleration goes beyond a pre-defined thresholds. In [10], authors use the smartphones built-in accelerometer and the gyroscope in order to identify the location of the cellphone on the body (chest, pocket, holster, etc).Using this information, the system determine when a fall occurs. Another example is presented in Kangas et al [11].…”

Section: B Fall Detection Algorithmsmentioning

confidence: 99%

Rapid Response Fall Detection System

Flores¹,

Cruz²,

Rolon³

et al. 2017

Proceedings of the 15th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Global Partnership F

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This paper presents an application titled "Rapid Response Fall Detection System"-RRFD. This application aims to detect when a cyclist has suffer an accident, and alerts the emergency contacts about the accident. "RRFD" uses the cellphone built-in accelerometer for fall detection, and the GPS to provide an accurate location. When a fall is detected, the application sends an alert via text message to previously selected contacts. Any sudden fall will activate the application and send the text with information of whether the rider requires assistance and / or immediate medical help. In the event that the cyclist is unable to request assistance or help, it provides the route and location of the cyclist using various tools of high accuracy.

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