A machine learning approach to identify fall risk for older adults

Chakraborty, Prithwi Raj; Sorwar, Golam

doi:10.1016/j.smhl.2022.100303

Cited by 4 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have studied the assessment of fall risk [11][12][13][14], but most of them examined older adults, and little research has been conducted on fall risk identification for seafarers on a ship. In addition, existing studies related to classifying fall risks have been undertaken via different devices like radars and cameras [15][16][17][18][19].…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, ML techniques such as classification and clustering tackle many automated recognition or prediction problems. Many researchers have utilized ML to predict or detect falls and identify the risk of falls [11][12][13][14]. Thakur and Han [11] proposed an optimal ML approach to improve fall detection in assisted living by comparing 19 different ML methods.…”

Section: Introductionmentioning

confidence: 99%

“…Noh et al [13] developed the extreme gradient boosting (XGB) model to predict the fall risk level in older adults by identifying the optimal gait features. Chakraborty and Sorwar [14] discriminated between fallers and non-fallers based on the long-term monitoring of natural fall data using three different ML approaches. These studies primarily examined older adults in the biomedical or healthcare domains.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning-Based Approach to Identifying Fall Risk in Seafarers Using Wearable Sensors

Choi,

Knarr,

Youn

et al. 2024

JMSE

View full text Add to dashboard Cite

Falls on a ship cause severe injuries, and an accident falling off board, referred to as “man overboard” (MOB), can lead to death. Thus, it is crucial to accurately and timely detect the risk of falling. Wearable sensors, unlike camera and radar sensors, are affordable and easily accessible regardless of the weather conditions. This study aimed to identify the fall risk level (i.e., high and low risk) among individuals on board using wearable sensors. We collected walking data from accelerometers during the experiment by simulating the ship’s rolling motions using a computer-assisted rehabilitation environment (CAREN). With the best features selected by LASSO, eight machine learning (ML) models were implemented with a synthetic minority oversampling technique (SMOTE) and the best-tuned hyperparameters. In all ML models, the performance in classifying fall risk showed overall a good accuracy (0.7778 to 0.8519), sensitivity (0.7556 to 0.8667), specificity (0.7778 to 0.8889), and AUC (0.7673 to 0.9204). Logistic regression showed the best performance in terms of the AUC for both training (0.9483) and testing (0.9204). We anticipate that this study will effectively help identify the risk of falls on ships and aid in developing a monitoring system capable of averting falls and detecting MOB situations.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Based Approach to Identifying Fall Risk in Seafarers Using Wearable Sensors

Choi,

Knarr,

Youn

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

Fall-risk assessment of aged workers using wearable inertial measurement units based on machine learning

Kou,

Xu,

et al. 2024

Safety Science

View full text Add to dashboard Cite

Fall risk prediction using temporal gait features and machine learning approaches

Lim,

Connie,

Goh

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

IntroductionFalls have been acknowledged as a major public health issue around the world. Early detection of fall risk is pivotal for preventive measures. Traditional clinical assessments, although reliable, are resource-intensive and may not always be feasible.MethodsThis study explores the efficacy of artificial intelligence (AI) in predicting fall risk, leveraging gait analysis through computer vision and machine learning techniques. Data was collected using the Timed Up and Go (TUG) test and JHFRAT assessment from MMU collaborators and augmented with a public dataset from Mendeley involving older adults. The study introduces a robust approach for extracting and analyzing gait features, such as stride time, step time, cadence, and stance time, to distinguish between fallers and non-fallers.ResultsTwo experimental setups were investigated: one considering separate gait features for each foot and another analyzing averaged features for both feet. Ultimately, the proposed solutions produce promising outcomes, greatly enhancing the model’s ability to achieve high levels of accuracy. In particular, the LightGBM demonstrates a superior accuracy of 96% in the prediction task.DiscussionThe findings demonstrate that simple machine learning models can successfully identify individuals at higher fall risk based on gait characteristics, with promising results that could potentially streamline fall risk assessment processes. However, several limitations were discovered throughout the experiment, including an insufficient dataset and data variation, limiting the model’s generalizability. These issues are raised for future work consideration. Overall, this research contributes to the growing body of knowledge on fall risk prediction and underscores the potential of AI in enhancing public health strategies through the early identification of at-risk individuals.

show abstract

A machine learning approach to identify fall risk for older adults

Cited by 4 publications

References 23 publications

Machine Learning-Based Approach to Identifying Fall Risk in Seafarers Using Wearable Sensors

Machine Learning-Based Approach to Identifying Fall Risk in Seafarers Using Wearable Sensors

Fall-risk assessment of aged workers using wearable inertial measurement units based on machine learning

Fall risk prediction using temporal gait features and machine learning approaches

Contact Info

Product

Resources

About