Emergency Detection System using PDA based on Self-response Algorithm

Jeon, Ah-young; Park, Jun-Mo; Jeon, Gye-Rok; Ye, Soo-Young; Kim, Jae-Hyung

doi:10.4313/teem.2007.8.6.293

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…A few examples are: tilt angle estimates to discriminate between lying and upright [ 10 ], discrete wavelet transform or vertical velocity estimates to recognize sit-to-stand transitions [ 18 , 19 ] and signal power to distinguish static activities from dynamic [ 10 , 20 , 21 , 22 ]. Machine learning classifiers, such as random forests, k-nearest neighbors and support vector machines, are often used to process acceleration features and classify activity types [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

Fridriksdottir

Bonomi

2020

Sensors

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The objective of this study was to investigate the accuracy of a Deep Neural Network (DNN) in recognizing activities typical for hospitalized patients. A data collection study was conducted with 20 healthy volunteers (10 males and 10 females, age = 43 ± 13 years) in a simulated hospital environment. A single triaxial accelerometer mounted on the trunk was used to measure body movement and recognize six activity types: lying in bed, upright posture, walking, wheelchair transport, stair ascent and stair descent. A DNN consisting of a three-layer convolutional neural network followed by a long short-term memory layer was developed for this classification problem. Additionally, features were extracted from the accelerometer data to train a support vector machine (SVM) classifier for comparison. The DNN reached 94.52% overall accuracy on the holdout dataset compared to 83.35% of the SVM classifier. In conclusion, a DNN is capable of recognizing types of physical activity in simulated hospital conditions using data captured by a single tri-axial accelerometer. The method described may be used for continuous monitoring of patient activities during hospitalization to provide additional insights into the recovery process.

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Section: Related Workmentioning

confidence: 99%

Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

Fridriksdottir

Bonomi

2020

Sensors

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“…Threshold is set by the characteristics of the above three stages of fall, as the judgment standard, which constitute the entire fall detection algorithm. In reference [3], they consider a fall occurred once at least 10 sampling points are detected in 4s, but it can't meet the requirements of modern medical detection. So we take 100Hz as sampling frequency.…”

Section: Process Of Fall Recognitionmentioning

confidence: 99%

“…A summary of the state of the art is shown in Table I. Most of the research groups use accelerometers integrated into small boxes worn [3][4] [6][7]. Reference [5] use eight pairs of force sensing resistors integrated into shoes.…”

Section: Introductionmentioning

confidence: 99%

The Design of Fall Detection and Positioning Alarm System Base on WCDMA

Mon¹,

Zhang²,

Yao³

2014

Proceedings of the 2014 International Conference on Future Computer and Communication Engineering

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In this paper, a novel fall detection and automatic alarm system has been designed and presented, which consists of sensor module, control module and communication module. The sensor modules composed of 3-axis accelerometers are placed on object's pocket to monitor the object's acceleration. System is driven by an inexpensive and low consumption microcontroller. When object is fall, the system will detect it by the abnormal acceleration and make accurate positioning through GPS immediately. The alarm as well as location information is sent via 3G network to the mobile phone number that has been set. The system has the advantages of portable, low-power and accuracy.

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“…When a fall occurs, it is crucial to immediately detect the situation, because these accidents usually lead to more severe illness or even death. Early detection of falls is essential for rescuing injured people from danger and getting help as quickly as possible [5]. For Mubashir and Shao (2013) [6], the demand for surveillance systems, especially for fall detection, has increased in the health sector with the rapid growth of the older adult population in the world.…”

Section: Introductionmentioning

confidence: 99%

Computational Solutions for Human Falls Classification

et al. 2021

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In the last two decades, studies about using technology for automatic detection of human fall increased considerably. The automatic detection of falls allows for quicker aid that is key to increasing the chances of treatment and mitigating the consequences of falls. However, each type of fall has its specificities, and determining the correct type of fall can help treat the person who has fallen. Although it is essential to use computational methods to classify falls, there are few studies about that in the literature, especially compared to the studies that propose solutions for fall detection. In this sense, we execute a systematic literature review (SLR) using the Kitchenham (2009) [1] method to investigate the computational solutions used to classify the different types of falls. We performed a search on Scopus, Web of Science and PubMed scientific databases looking for computational methods to falls classification in their papers. We use the grounded theory methodology for a more detailed qualitative analysis of the papers. As a result of our search, we selected a total of 36 studies for our review and found two different computational methods for classifying falls. Related to the steps used in each method, we found fourteen different types of sensors, four different techniques for background and foreground extraction of videos, twenty-one techniques for feature extraction, and seven different fall classification strategies. Finally, we also identified fifty-one different types of falls. In conclusion, we believe that the methods and techniques analyzed in our study can help developers to create new and better systems for classification, detection, and prevention of falls and falls database. Besides, we identified gaps that can be explored in future research related to the automatic classification of falls.

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Emergency Detection System using PDA based on Self-response Algorithm

Cited by 4 publications

References 2 publications

Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network

The Design of Fall Detection and Positioning Alarm System Base on WCDMA

Computational Solutions for Human Falls Classification

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