Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

Thanh, Pham Van; Tran, Duc–Tan; Nguyen, Dinh-Chinh; Ánh, Nguyễn Đức; Dinh, Dang Nhu; El-Rabaie, El-Sayed M.; Sandrasegaran, Kumbesan

doi:10.1007/s13369-018-3496-4

Cited by 33 publications

(18 citation statements)

References 21 publications

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“…Before applying our proposed features in step counting; the calibration, modeling, and simple Kalman filters are used to eliminate 3-DOF accelerometer errors. The detail of the calibration, modeling, and simple Kalman filter can be found in our prior research [ 25 ]. Then, 3-DOF accelerometer is used to record acceleration in three axes Ax , Ay , and Az .…”

Section: Methodsmentioning

confidence: 99%

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Pham¹,

Nguyễn²,

Dang³

et al. 2018

Sensors

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Accurate step counting is essential for indoor positioning, health monitoring systems, and other indoor positioning services. There are several publications and commercial applications in step counting. Nevertheless, over-counting, under-counting, and false walking problems are still encountered in these methods. In this paper, we propose to develop a highly accurate step counting method to solve these limitations by proposing four features: Minimal peak distance, minimal peak prominence, dynamic thresholding, and vibration elimination, and these features are adaptive with the user’s states. Our proposed features are combined with periodicity and similarity features to solve false walking problem. The proposed method shows a significant improvement of 99.42% and 96.47% of the average of accuracy in free walking and false walking problems, respectively, on our datasets. Furthermore, our proposed method also achieves the average accuracy of 97.04% on public datasets and better accuracy in comparison with three commercial step counting applications: Pedometer and Weight Loss Coach installed on Lenovo P780, Health apps in iPhone 5s (iOS 10.3.3), and S-health in Samsung Galaxy S5 (Android 6.01).

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Section: Methodsmentioning

confidence: 99%

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Pham¹,

Nguyễn²,

Dang³

et al. 2018

Sensors

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“…Technological solutions may assist in providing a more objective and real-time assessment of falls. Wearable devices, such as a watch, can provide remote, objective evidence of falls from the patient's home 35 but have not yet been applied to PSP. Measuring balance may help to identify those with PSP at risk of falling.…”

Section: Measuring Falls and Balancementioning

confidence: 99%

Falls in Progressive Supranuclear Palsy

Brown

Rowe

Passamonti

et al. 2019

Movement Disord Clin Pract

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Background Background: Despite falls being an almost universal clinical feature and central to the presentation and diagnostic criteria of progressive supranuclear palsy, our understanding of falls is surprisingly limited and there are few effective treatment options. Objectives Objectives: To provide an overview of the topic of the impact, assessment, mechanism, and management of falls in progressive supranuclear palsy. Methods Methods: We performed a literature search for "falls" and "progressive supranuclear palsy" and included additional relevant literature known to us. We synthesized this literature with experience from clinical practice. ResultsResults: We review current understanding of the pathophysiology of falls, highlighting the roles of the indirect pathway and the pedunculopontine nucleus. We go on to identify shortcomings in commonly used assessments to measure falls. We discuss medical and nonmedical fall prevention strategies, and finally we discuss balancing falls risk against promoting independence. Conclusion Conclusion: Falls are central to progressive supranuclear palsy presentation and diagnosis. Indirect locomotor and pedunculopontine nucleus dysfunction are thought to be the neural substrate of falls in this condition. Attempts to measure and prevent falls, by medical and nonmedical means, are currently limited. A personalized approach is advocated in the management of falls.

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“…Additionally, their performance varies, as they tend to slide away from the correct position when worn over time [8,9]. To ensure better impact attenuation during falls, some researchers have also developed wearable airbags [10][11][12]. Jeong et al [13] developed a hip impact simulator and compared the impact attenuation performance of hip protection pads with a wearable airbag.…”

Section: Introductionmentioning

confidence: 99%

“…Thanh et al [10] captured 3-axis acceleration data from the waist position, to which they applied a Kalman filter to reduce measurement and sensor errors. Their threshold-based fall detection algorithm used the root mean square of acceleration, vertical angle, and vertical velocity, and achieved 100% fall detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags

Jung

Koo

Kim

et al. 2020

Sensors

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Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.

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Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

Cited by 33 publications

References 21 publications

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Highly Accurate Step Counting at Various Walking States Using Low-Cost Inertial Measurement Unit Support Indoor Positioning System

Falls in Progressive Supranuclear Palsy

Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags

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