Fall detection based on accelerometer and gyroscope using back propagation

Jefiza, Adlian; Pramunanto, Eko; Boedinoegroho, Hanny; Purnomo, Mauridhi Hery

doi:10.1109/eecsi.2017.8239149

Cited by 41 publications

(21 citation statements)

References 5 publications

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“…Results show an accuracy of 87.5%, sensitivity of 90.70%, and specificity of 83.78%, for kNN. Jefiza et al [32] use backpropagation neural network (BPNN) for fall detection, with data collected from 3-axis accelerometer and gyroscope, and reported an accuracy of 98.182%, precision of 98.33%, sensitivity of 95.161%, and specificity of 99.367%. Hossain et al [33] also attempt to distinguish falls from ADLs and compares SVM, kNN, and complex tree algorithms applied on data generated by accelerometers.…”

Section: Machine Learning-based Wearable Systems For Fallmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

115

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With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

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Section: Machine Learning-based Wearable Systems For Fallmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

115

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“…Neural Networks have been greatly improved in recent times [27][28][29]. This technique has consistently displayed a greater learning potential than traditional ML techniques.…”

Section: Related Workmentioning

confidence: 99%

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

et al. 2020

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People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.

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“…After the data is calculated based on the scale, the magnitude value of the xyz axis data on the accelerometer is calculated by using the following Equation 2 [25].…”

Section: Determine the Value Of Magnitudementioning

confidence: 99%

The Calorie Burning Calculation System in Jogging Using a Thresholding-Based Accelerometer Sensor

Okmayura

Jefiza

Ramadhani

2020

KINETIK

Self Cite

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Jogging has benefits for burning calories as well as for losing the weight of the body. However, the reality that often happens is that sportsmen overwork their body in exercising so that it has passed its normal threshold. This causes over or lower calorie burning in these athletes. To overcome these problems, we need a device that can later monitor calorie burning. This device is created on an Android-based platform using an accelerometer sensor. Using this sensor, the system can count the number of a person’s steps previously obtained through the threshold value as the threshold value to determine the steps. Calculation of calorie burning uses a formula that involves the number of footsteps, time, and weight of the user. This application testing involved 15 participants by comparing the calorie burning results through the application and manual calculation, so that the overall error percentage of 0.60% was obtained, so it can be concluded that 99.40% of the calorie burning calculation system in jogging using a thresholding-based accelerometer sensor is appropriate for athletes to provide information of their calories burning in jogging.

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Fall detection based on accelerometer and gyroscope using back propagation

Cited by 41 publications

References 5 publications

A Survey on Recent Advances in Wearable Fall Detection Systems

A Survey on Recent Advances in Wearable Fall Detection Systems

A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

The Calorie Burning Calculation System in Jogging Using a Thresholding-Based Accelerometer Sensor

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