A wearable device for fall detection elderly people using tri dimensional accelerometer

Kurniawan, Arief; Hermawan, A. R.; Purnama, Iwan

doi:10.1109/isitia.2016.7828740

Cited by 22 publications

(10 citation statements)

References 3 publications

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“…Such systems can be broadly classified into threshold based and machine learning based systems. In [26][27][28], the tri-axial accelerometer and gyroscope sensor measurements are monitored for calculating the RMS values of acceleration and angular velocity of each axis and compared with a threshold value to detect fall events. In [29], the same methodology is performed with sensors present in a smartphone.…”

Section: Systems Based On Wearable Devicesmentioning

confidence: 99%

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Possible Life Saver: A Review on Human Fall Detection Technology

et al. 2020

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Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

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Section: Systems Based On Wearable Devicesmentioning

confidence: 99%

“…Works on sensors which is prone to failure of detection or false trigger. [28] Triaxial accelerometer is used to measure the X, Y, and Z values and sent to a microcontroller where the measured values are compared with a threshold value for fall detection.…”

Section: Accuracy = 93%mentioning

confidence: 99%

Possible Life Saver: A Review on Human Fall Detection Technology

et al. 2020

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“…Metode ambang batas (threshold) merupakan salah satu metode yang digunakan dalam algoritma klasifikasi aktivitas jatuh dan aktivitas biasa. [5][6][7]. Penentuan nilai threshold dilakukan dengan menganalisis semua nilai total percepatan dan total orientasi dari gerak aktivitas biasa atau activity of daily living (ADL), kemudian diambil nilai maksimalnya untuk dijadikan nilai threshold [5].…”

Section: B Metode Thresholdunclassified

“…Meski akurasi dari metode threshold tidak sebaik machine learning, namun metode ini lebih cocok digunakan pada perangkat yang dapat dipakai dan konsumsi energi yang rendah. Karena perhitungan metode ini sederhana dan dapat diimplementasikan pada perangkat dengan ukuran kecil [5,6]. Pada penelitian ini kami membuat suatu perangkat yang dapat mendeteksi jatuh yang harapannya dapat digunakan oleh lanjut usia menggunakan sensor accelerometer dan gyroscope berbasis mikrokontroler.…”

Section: Introductionunclassified

Sistem Pendeteksi Jatuh Wearable untuk Lanjut Usia Menggunakan Accelerometer dan Gyroscope

Tsani¹,

Mulyadi²

2019

JAEE

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Perkembangan populasi lanjut usia meningkat setiap tahunnya berbanding lurus dengan angka potensi jatuh pada lanjut usia. Jatuh menjadi salah satu kecelakaan yang paling umum terjadi pada lanjut usia. Sekitar 28-35% orang berusia 65 atau lebih di dunia mengalami kejadian jatuh setiap tahunnya. Jatuh dapat berakibat cedera fatal bahkan meningkatkan risiko kematian jika tidak segera ditangani. Untuk meminimalkan dampak yang ditimbulkan jatuh tersebut, telah banyak penelitian yang dilakukan salah satunya dengan metode threshold. Penelitian ini bertujuan membuat suatu sistem pendeteksi jatuh untuk mendapatkan nilai akurasi sistem. Sistem ini memanfaatkan nilai keluaran dari sensor accelerometer dan gyroscope berbasis mikrokontroler dengan metode threshold. Hasil notifikasi jatuh akan dikirimkan dan ditampilkan pada smartphone android. Perangkat ini bersifat wearable digunakan pada bagian pinggang. Penelitian ini menggunakan 6 jenis aktivitas biasa: berdiri, jongkok, berjalan, berlari, naik dan turun tangga, juga menggunakan 4 jenis aktivitas jatuh: jatuh ke kanan, kiri, depan dan belakang. Berdasarkan hasil pengujian yang telah dilakukan, didapatkan bahwa sistem yang dibuat memiliki sensitivitas sebesar 82,50%, spesifisitas sebesar 91,67%, akurasinya sebesar 88% dan tingkat keberhasilan smartphone menampilkan notifikasi jatuh sebesar 96,97%.

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“…Hsieh et al [ 21 ] reported a novel hierarchical fall detection algorithm based on the measured acceleration data involving threshold-based and knowledge-based approaches to detect a fall event. Kurniawan et al [ 22 ] used a triaxis accelerometer to obtain the acceleration of the human body and then determined whether the fall occurs depending on the acceleration. Albert et al [ 23 ] used the accelerometer of the mobile phone to obtain the acceleration data, which is applied in the machine learning classifier to detect falls and classify the type of falls.…”

Section: Introductionmentioning

confidence: 99%

Highly Portable, Sensor-Based System for Human Fall Monitoring

Mao

et al. 2017

Sensors

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Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user’s body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system.

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A wearable device for fall detection elderly people using tri dimensional accelerometer

Cited by 22 publications

References 3 publications

Possible Life Saver: A Review on Human Fall Detection Technology

Possible Life Saver: A Review on Human Fall Detection Technology

Sistem Pendeteksi Jatuh Wearable untuk Lanjut Usia Menggunakan Accelerometer dan Gyroscope

Highly Portable, Sensor-Based System for Human Fall Monitoring

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