A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies

Hassan, Ch Anwar ul; Karim, Faten Khalid; Abbas, Assad; Iqbal, Jawaid; Elmannai, Hela; Hussain, Saddam; Ullah, Syed Sajid; Khan, Muhammad Sufyan

doi:10.3390/su15053982

Cited by 4 publications

(3 citation statements)

References 84 publications

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“…Interviews with staff to understand these incidents revealed that residents may not have been wearing their MGA when they fell. The research literature has found that wearing a fall detection device all the time can be frustrating or annoying, and the older person may also need to remember to wear the device (Hassan et al, 2023). This is consistent with what was found in this study.…”

Section: Decrease In "Unwitnessed Falls"supporting

confidence: 91%

The European Conference on Aging & Gerontology 2023: Official Conference Proceedings

2023

EGen Official Conference Proceedings

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Following both international and local waves of criticism due to overwhelming high rates of youth unemployment, the South African post-Apartheid government has passed several economic empowerment policies to address the challenge. In addressing the youth unemployment and sustainability of livelihoods, institutions and their policies availed resources to the youth and left a gap in responding to the elderly's livelihoods. The purpose of this is to evaluate the impact of economic empowerment policies on the South African rural elderly and suggest a framework to respond to the challenge. A qualitative, inductive, interpretative approach was used to achieve the research objectives. Data were collected from 34 purposive (social network analysis group) interviews and 35 snowballing (semi-structured one-on-one) interviews with selected participants from the Jozini and Matatiele municipalities. Data were analysed using Nvivo 12. The findings reveal a lack of institutions and policies that focus on the sustainability of rural elderly livelihoods. It is recommended that policymakers collaborate with the rural elderly with the view to ensure economic empowerment policies respond to their economic empowerment and sustainability of livelihoods beyond retirement. The contribution of the article lies in the use of a sustainable livelihoods framework and Institutional Theory to suggest a solution.

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Section: Decrease In "Unwitnessed Falls"supporting

confidence: 91%

The European Conference on Aging & Gerontology 2023: Official Conference Proceedings

2023

EGen Official Conference Proceedings

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“…Additionally, other studies of both approaches sensor and vision-based indicated that using RF helps to enhance the results [34][35][36].…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of using Dimensionality Reduction Compared with Type of Algorithm on Detecting Patient Fall: Triage Case Study

Alshalawi,

Qadrouh,

Alhalabi

2024

Preprint

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Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same Accuracy of TTS.

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“…Consequently, the specificity (precision) and sensitivity (recall) of the targeted class are calculated to assess the algorithm's predictive accuracy for that specific class. These metrics in ML are determined based on the rates of "TP-True Positive, TN-True Negative, FP-False Positive, and FN-False Negative" [89]. True positive and true negative predictions are divided by all positive and negative predictions, respectively.…”

Section: Evaluating Parametersmentioning

confidence: 99%

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

Alharbi,

Hassan

2023

Sustainability

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In the realm of sustainable IoT and AI applications for the well-being of elderly individuals living alone in their homes, falls can have severe consequences. These consequences include post-fall complications and extended periods of immobility on the floor. Researchers have been exploring various techniques for fall detection over the past decade, and this study introduces an innovative Elder Fall Detection system that harnesses IoT and AI technologies. In our IoT configuration, we integrate RFID tags into smart carpets along with RFID readers to identify falls among the elderly population. To simulate fall events, we conducted experiments with 13 participants. In these experiments, RFID tags embedded in the smart carpets transmit signals to RFID readers, effectively distinguishing signals from fall events and regular movements. When a fall is detected, the system activates a green signal, triggers an alarm, and sends notifications to alert caregivers or family members. To enhance the precision of fall detection, we employed various machine and deep learning classifiers, including Random Forest (RF), XGBoost, Gated Recurrent Units (GRUs), Logistic Regression (LGR), and K-Nearest Neighbors (KNN), to analyze the collected dataset. Results show that the Random Forest algorithm achieves a 43% accuracy rate, GRUs exhibit a 44% accuracy rate, and XGBoost achieves a 33% accuracy rate. Remarkably, KNN outperforms the others with an exceptional accuracy rate of 99%. This research aims to propose an efficient fall detection framework that significantly contributes to enhancing the safety and overall well-being of independently living elderly individuals. It aligns with the principles of sustainability in IoT and AI applications.

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A Cost-Effective Fall-Detection Framework for the Elderly Using Sensor-Based Technologies

Cited by 4 publications

References 84 publications

The European Conference on Aging & Gerontology 2023: Official Conference Proceedings

The European Conference on Aging & Gerontology 2023: Official Conference Proceedings

The Effect of using Dimensionality Reduction Compared with Type of Algorithm on Detecting Patient Fall: Triage Case Study

Enhancing Elderly Fall Detection through IoT-Enabled Smart Flooring and AI for Independent Living Sustainability

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