An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

Liu, Leyuan; Hou, Yu; Jian, He; Lungu, Jonathan; Dong, Ruihai

doi:10.3390/s20154192

Cited by 20 publications

(10 citation statements)

References 30 publications

(29 reference statements)

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“…Many scholars have applied deep learning in real-time fall detection in older populations. Liu et al 40 used a sensor to obtain the motion data of the human body and used a deep neural network for fall detection (FD-DNN), which combines convolutional neural networks (CNN) with long short-term memory (LSTM) algorithms to predict falls in older adults, achieving 99.17% detection accuracy and 94.09% sensitivity.…”

Section: Discussionmentioning

confidence: 99%

A new approach for fall risk assessment in hospitalized older adults based on gait data

Xie

Liu

Huang

et al. 2022

Preprint

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Background:Falling in older adults is one of the most common and serious problems leading to disability. Therefore, it is necessary to assess the risk of falls in older adults and take preventive measures in advance. The traditional risk assessment depends on the scale, which may be affected by the subjective factors of patients. However, in recent years, instruments have been developed to collect objective data related to gait in older adults.The aim of this study was to use objective gait data to predict fall risk in older adults. Methods:In this study, a total of 207 hospitalized older adults were recruited, and the Morse Fall Scale (MFS) and six-degrees-of-freedom (6-DOF) gait kinematic parameters of the lower limb joints were collected using a marker-based instrument. Based on the gait data, two important tasks in fall risk assessment were conducted, analysis of abnormal gait patterns and risk level classification. There were three fall risk levels corresponding to the scale, and an end-to-end attention-based convolution model was proposed to analyze gait kinematic data. Results: The model achieved an accuracy score of 0.878 and a recall score of 0.897 on the test set. In addition, we applied an attention-based heatmap to visualize the input data and features across the model. The color bars in the heatmap highly correlate with the level of fall risk and can serve as an indicator of the abnormal gait pattern. Conclusions: An end-to-end attention-based convolution model achieved a favorable result.Besides, the heatmap could serve as the indicator of risk level for each step and also provide further clues to the mechanism of falling. It has the potential to assist doctors in clinical work and contribute to further knowledge discovery.

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Section: Discussionmentioning

confidence: 99%

A new approach for fall risk assessment in hospitalized older adults based on gait data

Xie

Liu

Huang

et al. 2022

Preprint

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“…Total number of data samples human activities include that were studied include, Standing, Sitting, Laying, walking fast, walking slow, walking downstairs, and walking upstairs [24]. We divided these activities into two groups per dataset.…”

Section: Fall Detection Samplesmentioning

confidence: 99%

Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House

Nchena¹,

Tomášek²

2023

Internet of Things - New Trends, Challenges and Hurdles

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This work proposes a smart system that could be useful in the delivery of elderly care services. Elderly care is a set of services that are provided to senior citizens to help them have a more comfortable and independent life which would not be possible without these services. This proposed system is unique in that it combines the detection algorithm with the automatic update of the dataset. It also uses a heuristic mechanism to reduce false detections. This is on the premise that the AI effort is good, but it could be made better with the inclusion of heuristics. Fall detection accuracy is initially solved by the first classifier, then another classifier evaluates the result with inferences before evoking an alarm. It checks the location of the subject to use in its inferences. Hence the smart house design consists of two machine learning systems. One system performs human activity classification while the other performs fall occurrence detection. Of the eight different classification methods utilized, XGBoost was most accurate with an average of 97.65% during training. A customized dataset is then generated with newly labeled data hence improving system performance.

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“…Wearable systems consist of sensors that can be attached to the human body for data collection. Some researchers presented new fall detection systems by using data from accelerometers and gyroscopes sensors attached to the body of the elderly [7][8][9]. Nowadays, smartphones have also become an important sensor device medium [10,11].…”

Section: Health Preventionmentioning

confidence: 99%

A Survey of Artificial-Intelligence Enabled Digital Transformation in Elderly Healthcare Field

Lee

Wang

et al. 2022

Advances in Transdisciplinary Engineering

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With the rapid growth of the elderly population, traditional elderly healthcare services can hardly meet the dynamic and uncertain demand and higher customer expectations. With the digital transformation with digital technological enablers of the elderly-related industry rising globally, Artificial Intelligence (AI) has begun to play a disruptive and critical role in the elderly healthcare industry. But there is still a lack of a holistic review about AI-enabled digital transformation (DT) in the elderly healthcare field. Therefore, 59 works of literature published from 2000 through 2021 were extracted and analysed from Web of Science. This study focuses on the application scenarios, the AI approach used, and the benefits of AI-enabled DT. And this literature review lays the foundation for future research in the elderly healthcare field.

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An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People

Cited by 20 publications

References 30 publications

A new approach for fall risk assessment in hospitalized older adults based on gait data

A new approach for fall risk assessment in hospitalized older adults based on gait data

Autonomous Update of a Dataset for Anomaly Detection Services in Elderly Care Smart House

A Survey of Artificial-Intelligence Enabled Digital Transformation in Elderly Healthcare Field

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