Preimpact Fall Detection for Elderly Based on Fractional Domain

Liu, Ning; Zhang, Dedi; Su, Zhong; Wang, Tianrun

doi:10.1155/2021/6661034

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…The hardware block diagram is shown in the figure . The auxiliary bracelet software program mainly realizes the functions of real-time measurement of blood oxygen Among them, the motion fall detection device does not give an alarm when the three-axis acceleration sensor is placed vertically, and gives an alarm when the change rate of the sensor's inclination angle reaches a preset value. Through the communication chip, the heart rate, body temperature, data and motion state are transmitted to the wireless gateway, then displayed and stored in the server interrupt [2].…”

Section: Types Of Components In Systemmentioning

confidence: 99%

Patient Signs Safety Monitoring System Based on Internet of Things and Cloud Computing

Dong,

Wang,

Zhou

2023

ACC

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The contemporary lifestyle has led to a pressing demand for prompt, efficient, and secure medical care. However, the modern healthcare system faces several challenges such as inadequate funding, fragmentation within the medical community, and reduced service efficacy. Innovative solutions, such as the development of an intelligent detection bracelet, have been devised to tackle these issues. The wristband collects health data, encrypts it and transmits it via Internet of Things (IoT) technology to a cloud computing platform for hierarchical management and analysis. Numerous remote health monitoring systems have been developed to facilitate timely notifications and patient feedback, particularly for individuals with deteriorating suboptimal health conditions. The design of these systems aims to ensure proactive measures while minimizing disruptions, thereby enabling individuals to access personalized healthcare support and prompt medical attention through the utilization of IoT technology and remote health monitoring. This effectively addresses resource shortages and enhances overall health outcomes.

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Section: Types Of Components In Systemmentioning

confidence: 99%

Patient Signs Safety Monitoring System Based on Internet of Things and Cloud Computing

Dong,

Wang,

Zhou

2023

ACC

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“…In the cloud, multiple algorithm types can be used to determine whether a person has fallen. In the current literature, a lot of research is performed on machine learning algorithms [30][31][32][33][34][35][36][37][38][39] that detect falls. However, these systems require a lot of training data, which is difficult to obtain.…”

Section: Related Workmentioning

confidence: 99%

Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

et al. 2022

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Due to the ever growing population of elderly people, there is a dramatic increase in fall accidents. Currently, multiple ideas exist to prevent the elderly from falling, by means of technology or individualised fall prevention training programs. Most of them are costly, difficult to implement or less used by the elderly, and they do not deliver the required results. Furthermore, the increasingly older population will also impact the workload of the medical and nursing personnel. Therefore, we propose a novel fall detection and warning system for nursing homes, relying on Bluetooth Low Energy wireless communication. This paper describes the hardware design of a fall-acceleration sensing wearable for the elderly. Moreover, the paper also focuses on a novel algorithm for real-time filtering of the measurement data as well as on a strategy to confirm the detected fall events, based on changes in the person’s orientation. In addition, we compare the performance of the algorithm to a machine learning procedure using a convolutional neural network. Finally, the proposed filtering technique is validated via measurements and simulation. The results show that the proposed algorithm as well as the convolutional neural network both results in an excellent accuracy when validating on a common database.

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“…Even though machine learning algorithms are very complex but using these algorithms better results have been obtained. Research papers (16) , (19)(20)(21)(22)(23)(24)(25)(26) used machine learning-based approaches for fall detection. Machine learning algorithms performed very well as compared to threshold-based methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Outlier based Human Fall Detection using One-Class Classification

Gupta¹,

Kumar²

2021

IJST

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Objective: This work aims to develop a human fall detection method that is trained using data of routine movement of people only collected from accelerometer sensor to stay away from irregular fall detection model. This work also aims to analyze the effect of calculated features on the fall detection model. Methodology: In the proposed method, The fall detection model is built using one-class classification. At first, data of accelerometer sensor in three directions has been used to detect the fall events. Five feature vectors i.e. resultant, variance, standard deviation, Root Mean square, and Euclidean Norm have been calculated. These four features along with one class SVM have been used to build fall detection model in ways (1) Using only resultant features only (2) Using all calculated features. This model was trained using only activities of daily living (ADL) and tested on both daily living activities and fall activities. Findings: It is found that when the model was built using all calculated features, the sensitivity was 100% and specificity was 94.92% when mobile is in the pocket and; sensitivity was 100% and specificity was 93.61% when mobile is in handbag which is better than when the model was built using resultant features only. We have also studied the effect of individual features on this fall detection model and it is found that variance played a very important role to classify fall activities and ADL activities. Novelty -The proposed fall detection method is built using one-class classification so that the proposed model will be reliable to detect falls in real life. In the proposed work, the effect of features on the fall detection model is also analyzed.

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Preimpact Fall Detection for Elderly Based on Fractional Domain

Cited by 5 publications

References 30 publications

Patient Signs Safety Monitoring System Based on Internet of Things and Cloud Computing

Patient Signs Safety Monitoring System Based on Internet of Things and Cloud Computing

Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

Outlier based Human Fall Detection using One-Class Classification

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