Collaborative Fall Detection using a Wearable Device and a Companion Robot

Liang, Fei; Hernandez, Ricardo Antonio Parrao; Lu, Jiaxing; Ong, Brandon; Moore, Matthew; Sheng, Weihua; Zhang, Senlin

doi:10.1109/icra48506.2021.9561323

Cited by 11 publications

(2 citation statements)

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“…Additionally, a fall detector is applied in workplaces in order to enhance employees’ safety [ 8 ]. Therefore, this task is one of the most rising research challenges, owing to the fact that accident determination can be life saving if combined with a call-for-help or an alarm system [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…Yet, the risk of either not detecting an event or recognizing it with delay persists. This limitation is of particular significance, as prompt detection is of the utmost importance in cases of older people, while it is only helpful if seen by the system immediately [ 9 , 26 ]. On the contrary, when a fall detector operates with high-frequency data provided through a wearable device, these limitations do not exist, as the sensor continuously receives information from the individual [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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PIPTO: Precise Inertial-Based Pipeline for Threshold-Based Fall Detection Using Three-Axis Accelerometers

Moutsis,

Tsintotas,

Gasteratos

2023

Sensors

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After traffic-related incidents, falls are the second cause of human death, presenting the highest percentage among the elderly. Aiming to address this problem, the research community has developed methods built upon different sensors, such as wearable, ambiance, or hybrid, and various techniques, such as those that are machine learning- and heuristic based. Concerning the models used in the former case, they classify the input data between fall and no fall, and specific data dimensions are required. Yet, when algorithms that adopt heuristic techniques, mainly using thresholds, are combined with the previous models, they reduce the computational cost. To this end, this article presents a pipeline for detecting falls through a threshold-based technique over the data provided by a three-axis accelerometer. This way, we propose a low-complexity system that can be adopted from any acceleration sensor that receives information at different frequencies. Moreover, the input lengths can differ, while we achieve to detect multiple falls in a time series of sum vector magnitudes, providing the specific time range of the fall. As evaluated on several datasets, our pipeline reaches high performance results at 90.40% and 91.56% sensitivity on MMsys and KFall, respectively, while the generated specificity is 93.96% and 85.90%. Lastly, aiming to facilitate the research community, our framework, entitled PIPTO (drawing inspiration from the Greek verb “πι´πτω”, signifying “to fall”), is open sourced in Python and C.

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Section: Introductionmentioning

confidence: 99%