Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data

Jansi, R.; Amutha, R.

doi:10.1007/s11045-020-00705-4

Cited by 27 publications

(15 citation statements)

References 37 publications

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“…The application of multiple Kinect sensors presented a promising prospect to enlarge the volume for motion capture and solve occlusion problems [ 71 ]. Meanwhile, depth data obtained from the Kinect could be incorporated with other motion-sensing information, such as data recorded by accelerometers [ 72 ] and inertial sensors [ 73 ], to develop a multimodel gait stability assessment system for clinic or home-based monitoring. Thirdly, this study used ankle landmarks instead of foot landmarks to represent the BOS because the foot tracking is usually noisy and inaccurate in the Kinect V2.…”

Section: Discussionmentioning

confidence: 99%

Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables

Mithraratne

Wilson

et al. 2021

Sensors

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Children with cerebral palsy (CP) have high risks of falling. It is necessary to evaluate gait stability for children with CP. In comparison to traditional motion capture techniques, the Kinect has the potential to be utilised as a cost-effective gait stability assessment tool, ensuring frequent and uninterrupted gait monitoring. To evaluate the validity and reliability of this measurement, in this study, ten children with CP performed two testing sessions, of which gait data were recorded by a Kinect V2 sensor and a referential Motion Analysis system. The margin of stability (MOS) and gait spatiotemporal metrics were examined. For the spatiotemporal parameters, intraclass correlation coefficient (ICC2,k) values were from 0.83 to 0.99 between two devices and from 0.78 to 0.88 between two testing sessions. For the MOS outcomes, ICC2,k values ranged from 0.42 to 0.99 between two devices and 0.28 to 0.69 between two test sessions. The Kinect V2 was able to provide valid and reliable spatiotemporal gait parameters, and it could also offer accurate outcome measures for the minimum MOS. The reliability of the Kinect V2 when assessing time-specific MOS variables was limited. The Kinect V2 shows the potential to be used as a cost-effective tool for CP gait stability assessment.

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Section: Discussionmentioning

confidence: 99%

Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables

Mithraratne

Wilson

et al. 2021

Sensors

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“…Research has been carried out on energy efficient and reliable fall detection systems combining both inertial signals and Kinect depth images [107]- [110], where inertial signals were used to indicate a potential fall and depth images were used to authenticate the eventual fall. Depth maps were not processed frame by frame, rather were stored in a circular buffer.…”

Section: A) Fusion Of Depth Images and Inertial Signalsmentioning

confidence: 99%

Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Majumder

Kehtarnavaz

2021

IEEE Sensors J.

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Human action recognition is used in many applications such as video surveillance, human-computer interaction, assistive living, and gaming. Many papers have appeared in the literature showing that the fusion of vision and inertial sensing improves recognition accuracies compared to the situations when each sensing modality is used individually. This paper provides a survey of the papers in which vision and inertial sensing are used simultaneously within a fusion framework in order to perform human action recognition. The surveyed papers are categorized in terms of fusion approaches, features, classifiers, as well as multimodality datasets considered. Challenges as well as possible future directions are also stated for deploying the fusion of these two sensing modalities under realistic conditions.

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“…The studies that focus on this area can also be subdivided, with some of them focusing on the development of wearable systems or devices [18][19][20], while others use edge devices (most commonly mobile phones) for information processing [21][22][23]. These products are usually highly effective in detecting falls.…”

Section: Fall Detectionmentioning

confidence: 99%

Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home

Rodriguez-Goncalves

García-Crespo

Matheus-Chacin

et al. 2021

Sensors

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In many countries, the number of elderly people has grown due to the increase in the life expectancy of the population, many of whom currently live alone and are prone to having accidents that they cannot report, especially if they are immobilized. For this reason, we have developed a non-intrusive IoT device, which, through multiple integrated sensors, collects information on habitual user behavior patterns and uses it to generate unusual behavior rules. These rules are used by our SecurHome system to send alert messages to the dependent person's family members or caregivers if their behavior changes abruptly over the course of their daily life. This document describes in detail the design and development of the SecurHome system.

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Detection of fall for the elderly in an indoor environment using a tri-axial accelerometer and Kinect depth data

Cited by 27 publications

References 37 publications

Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables

Kinect V2-Based Gait Analysis for Children with Cerebral Palsy: Validity and Reliability of Spatial Margin of Stability and Spatiotemporal Variables

Vision and Inertial Sensing Fusion for Human Action Recognition: A Review

Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home

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