Autoregressive-moving-average hidden Markov model for vision-based fall prediction—An application for walker robot

Taghvaei, Sajjad; Jahanandish, Mohammad Hassan; Kosuge, Kazuhiro

doi:10.1080/10400435.2016.1174178

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…Taghvaei et al [ 106 ] utilized a shallow learning model proposing an algorithm for real-time prediction of falls based on the acquired visual data of a user with a walking assistive system from a depth sensor. They fitted an autoregressive-moving-average (ARMA) model on the time-series from walking data to forecast the upcoming states.…”

Section: Methodological Approach and Resultsmentioning

confidence: 99%

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

et al. 2022

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Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

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Section: Methodological Approach and Resultsmentioning

confidence: 99%

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

et al. 2022

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“…These key points were then input into various classifiers including Gradient-Boosted Trees (GDBT), Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (KNN) to detect falls. In [16], a Hidden Markov Model (HMM) was utilized, leveraging silhouette data extracted through background subtraction techniques for fall detection. Remarkably, this approach achieved an accuracy of 84.72% based on their recorded dataset.…”

Section: A Fall Detectionmentioning

confidence: 99%

Computer Vision Based Transfer Learning-Aided Transformer Model for Fall Detection and Prediction

McCall,

Kolawole,

Naz

et al. 2024

IEEE Access

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Falls bring about significant risks to individuals' well-being and independence, prompting widespread public health concerns. Swift detection and even predicting the risk of falls are crucial for implementing effective measures to alleviate the adverse consequences associated with such incidents. This study presents a new framework for identifying and forecasting fall risks. Our approach utilizes a novel transformer model trained on 2D poses extracted through an off-the-shelf pose extractor, incorporating transfer learning techniques. Initially, the transformer is trained on a large dataset containing 2D poses of general actions. Subsequently, we freeze the majority of its layers and fine-tune only the last few layers using relatively smaller datasets for fall detection and prediction tasks. Experimental results indicate that our proposed method outperforms traditional machine learning (e.g., SVM, Decision Tree, etc.) and deep learning approaches (e.g., LSTM, CNN, ST-GCN, PoseC3D, etc.) in both fall detection and prediction tasks across various datasets.

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“…Keep distances between the human and the rollator [26], [46], [50]. Fall detection [52]. The walker synchronously follows the user [28].…”

Section: Autonomoushumanmentioning

confidence: 99%

“…Disabled people [31], [35], [46]. People with diagnosis of ischaemic or hemorrhagic stroke [26] [3], [28], [29], [32], [38], [50], [52] Shared-control Personalized Creates a model to forecast human motion to keep the desired situation (separation distance and bearing in the platform) [12]. Admittance controllers and a long-term user performance [11], [24].…”

Section: Autonomoushumanmentioning

confidence: 99%

Smart Rollators Aid Devices: Current Trends and Challenges

Verdezoto

Ballesteros

Urdiales

2022

IEEE Trans. Human-Mach. Syst.

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Mobility loss has a major impact on autonomy. Smart rollators have been proposed to enhance human abilities when conventional devices are not enough. Many human-robot interaction systems have been proposed in the last decade in this area. Comparative analysis shows that mechanical issues aside, they mainly differ in i) equipped sensors and actuators; ii) input interface; iii) operation modes, and iv) adaptation capabilities. This paper presents a review and a tentative taxonomy of approaches during the last 6 years. In total, 92 papers have been reviewed. We have discarded works not focused on humanrobot interaction or focused only on mechanical adaptation. A critical analysis is provided after the review and classification, highlighting systems tested with their target population.

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Autoregressive-moving-average hidden Markov model for vision-based fall prediction—An application for walker robot

Cited by 13 publications

References 12 publications

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Computer Vision Based Transfer Learning-Aided Transformer Model for Fall Detection and Prediction

Smart Rollators Aid Devices: Current Trends and Challenges

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