Using Smartwatch as Telecare and Fall Detection Device

Gjoreski, Hristijan; Bizjak, Jani; Gams, Matja

doi:10.1109/ie.2016.55

Cited by 14 publications

(4 citation statements)

References 10 publications

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“…The 3DACC sensor is located on the waist in this study. Similarly, thresholds are used to detect fall events in [28]; if a fall event is followed by a 20 seconds calm period (that is, with a reduced amount of movement), then the fall alarm is signaled. Thresholds were also used for FD in [29], the sensor was the 3DACC signal from a Smartphone.…”

Section: Related Work On Fd Using Wearable Devicesmentioning

confidence: 99%

Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges

Villar¹,

Chira²,

Cal³

et al. 2021

Neurocomputing

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Section: Related Work On Fd Using Wearable Devicesmentioning

confidence: 99%

Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges

Villar¹,

Chira²,

Cal³

et al. 2021

Neurocomputing

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“…The 3DACC sensor is located on the waist in this study. Similarly, thresholds are used to detect fall events in [53]; if a fall event is followed by a 20 seconds calm period (that is, with a reduced amount of movement), then the fall alarm is signaled. Thresholds were also used for FD in [54], the sensor was the 3DACC signal from a Smartphone.…”

Section: Related Workmentioning

confidence: 99%

Mixing user-centered and generalized models for Fall Detection

et al. 2021

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“…An alternative solution was proposed in [ 32 ], where a smartwatch works autonomously to detect falls and send notifications. Threshold-based solutions were proposed assuming that only those falls for which the user faints are the ones to be detected: in the rest of the cases, the user is in charge of calling the ehealth service.…”

Section: Introductionmentioning

confidence: 99%

Improving Fall Detection Using an On-Wrist Wearable Accelerometer

Khojasteh

Villar

Chira

et al. 2018

Sensors

120

101

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Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms.

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Using Smartwatch as Telecare and Fall Detection Device

Cited by 14 publications

References 10 publications

Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges

Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges

Mixing user-centered and generalized models for Fall Detection

Improving Fall Detection Using an On-Wrist Wearable Accelerometer

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