Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric

Amiroh, Khodijah; Rahmawati, Dewi; Wicaksono, Ardian Yusuf

doi:10.25077/jnte.v10n3.936.2021

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…This paper refers to the feature selection in [8] and configures the three directions of acceleration X, Y, and Z respectively. The selected features include statistical features such as mean, maximum (max), minimum (min), median and standard deviation (std), which are common in falls, and custom combined acceleration (slope) and peak difference (differ).…”

Section: Feature Extraction and Preprocessingmentioning

confidence: 99%

Research on ensemble learning algorithm for fall detection

Wang,

Liu,

Qiao

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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This research aims to address the issue of fall detection in the elderly using a wearable device-based fall detection system. Data is collected through acceleration sensors and barometric pressure sensors. After applying certain algorithms to the dataset, it was found that a single machine learning algorithm had poor generalization ability for fall detection. To improve classification accuracy, attempts were made to use ensemble learning algorithms for training and validation of the fall detection dataset. By employing Bagging and GBDT ensemble learning algorithms, the generalization ability of the model was successfully enhanced. On the validation set after 0.2 cross-validation, our model achieved an average accuracy of 99.28%, significantly improving the model's high performance and strong generalization ability.

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Section: Feature Extraction and Preprocessingmentioning

confidence: 99%

Research on ensemble learning algorithm for fall detection

Wang,

Liu,

Qiao

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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“…This space is divided into sections based on training sample classification. A point in this space is marked by class c, if class c is the most suitable classification found in the K-Nearest Neighbors of that point [17]. The closest or furthest neighbor is usually measured based on Euclidean Distance which is presented as follows: 𝑑 = √(𝑎 1 − 𝑏 1 ) 2 + (𝑎 2 − 𝑏 2 ) 2 + ⋯ (𝑎 𝑛 + 𝑏 𝑛 ) 2 (1)…”

Section: K-nearest Neighbormentioning

confidence: 99%

Classification Of Alcohol Type Using Gas Sensor And K-Nearest Neighbor

Ismail

Prasetyowati

2022

JNTE

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Ethanol, isopropyl and methanol belong to the same alcohol group. The latter is commonly used as an industrial solvent, not for personal consumption. Many traditional alcoholic drink sellers often mix alcoholic beverages, which are commonly called as oplosan, this mixed drink is very dangerous for human if it contains methanol. Based on this problem, it is necessary to make a measuring device for the alcohol content in the liquid to classify the alcohol type. The design of this gas sensor-based alcohol classification system and method consists of a series of hardware and software applications. The block diagram of the alcohol classification system measures the ethanol and methanol substances in each alcoholic drink using the MQ3 gas sensor and WeMos as a data acquisition device and microcontroller. The computer was used to process the acquisition data from the gas sensor being used then calculates the K-Nearest Neighbor (K-NN) to obtain the prediction results. The K-NN system testing consists of testing the effect of the K value and testing its accuracy. The result of testing the effect of the K value produces 100% optimum accuracy at the values namely K=1, K=3, K=5, K=10 and 55% on K=20.

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“…Fall-related impacts can lead to serious trauma and injuries. Even some of falls can result in permanent injuries restricting essential activities of daily living or even death [3]. The fear of falling leads to reduce physical activity, weaken in muscle, and increase risk of falling.…”

Section: Introductionmentioning

confidence: 99%

Achieving High Success in Fall Detection through Cross-Brand Inertial Sensor Utilization of Hybrid Data in Machine Learning

BUZPINAR

2024

Preprint

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Falls can result in severe injuries and even mortality among individuals of all age groups. Hence, numerous wearable sensor-based fall monitoring systems are being developed to provide assistance. Fall detection and activity tracking have been partially successful using smartwatches, smartphones, and specialized devices. However, a comprehensive solution that combines sensor data from different brands in a single model and performs fall detection with high accuracy and at a satisfactory level has not been encountered. This study aims to bridge this research gap by combining data from two different brands of IMUs (inertial measurement units) that incorporate accelerometers, magnetometers, and gyroscopes, in order to create a hybrid dataset. To achieve accurate predictions on data from both brands, machine learning (ML) models were trained using ML algorithms. The first dataset was obtained from 14 volunteers using a commercially available activity tracking system called Motion Trackers Wireless (MTw). The second dataset was collected from 30 volunteers using a custom-designed Activity Tracking Device (ATD) specifically developed for detecting falls and daily-life activities. In both cases, the sensors from the respective brands were positioned on the waist to capture data related to falls and daily-life activities. The data was organized using a time-series style to reveal relational effect of the sequential falling data. During the modelling, ten different classifiers trained, and classification was performed on unseen data using the data splitting method. The Extra Tree algorithm emerged as the most successful model, achieving an accuracy of 99.54%, precision of 99.18%, recall of 99.79%, and an F-score of 99.49% on the hybrid dataset constructed from the MTw and ATD datasets. This study demonstrates hybrid dataset to create a successful system with high accuracy and low false alarm rates using inertial sensor data from various brands.

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Intelligent System for Fall Prediction Based on Accelerometer and Gyroscope of Fatal Injury in Geriatric

Cited by 8 publications

References 10 publications

Research on ensemble learning algorithm for fall detection

Research on ensemble learning algorithm for fall detection

Classification Of Alcohol Type Using Gas Sensor And K-Nearest Neighbor

Achieving High Success in Fall Detection through Cross-Brand Inertial Sensor Utilization of Hybrid Data in Machine Learning

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