Qualitative Risk of Falling Assessment Based on Gait Abnormalities

Gagnon, David J.; Ménélas, Bob-Antoine J.; Otis, Martin J.‐D.

doi:10.1109/smc.2013.677

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…The main concept of the risk of falling adopted in this manuscript is based on the different methods (including the use of gait deviation index) proposed in the literature [ 44 , 46 , 66 , 67 , 68 , 69 , 70 , 71 ]. Most of them cannot be easily interpreted by nonprofessionals since they have high index above 100 without specific interpretation.…”

Section: Methodsmentioning

confidence: 99%

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Ayena

Chioukh

Otis

et al. 2021

Sensors

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Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision.

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Section: Methodsmentioning

confidence: 99%

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Ayena

Chioukh

Otis

et al. 2021

Sensors

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“…All these sensors are exploited to compute a risk level associated to a risk of falling. A 16-bits architecture microcontroller is included for computing, in real time, the risk level using different algorithms such as neural network [48], fuzzy logic [49] and our closed-loop balance model presented in this paper.…”

Section: A Instrumented Insolementioning

confidence: 99%

Home-Based Risk of Falling Assessment Test Using a Closed-Loop Balance Model

Ayena

Zaibi

Otis

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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The aim of this study is to improve and facilitate the methods used to assess risk of falling at home among older people through the computation of a risk of falling in real time in daily activities. In order to increase a real time computation of the risk of falling, a closed-loop balance model is proposed and compared with One-Leg Standing Test (OLST). This balance model allows studying the postural response of a person having an unpredictable perturbation. Twenty-nine volunteers participated in this study for evaluating the effectiveness of the proposed system which includes seventeen elder participants: ten healthy elderly ( 68.4 ±5.5 years), seven Parkinson's disease (PD) subjects ( 66.28 ±8.9 years), and twelve healthy young adults ( 28.27 ±3.74 years). Our work suggests that there is a relationship between OLST score and the risk of falling based on center of pressure measurement with four low cost force sensors located inside an instrumented insole, which could be predicted using our suggested closed-loop balance model. For long term monitoring at home, this system could be included in a medical electronic record and could be useful as a diagnostic aid tool.

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“…Neural Networks. Artifical neural networks are common approaches to perform classification and regression in the literature cited [28,29,33,36,48,84,91,92,97,113,140,142,147,149,161,162]. Artificial neural networks are computational models inspired by the behavior of biological neurons.…”

Section: Classification and Regressionmentioning

confidence: 99%

“…Additionally, by calculating Pearson's correlation coefficient for each feature (comparing normal and Parkinson's cases), the authors extracted four significant features and improved neural network performance to a classification accuracy of 95.63% [91]. Neural networks were also used in [36] to assess the risk of a fall, using data collected from pressure sensors embedded in the sole of a shoe. The investigation used features extracted during the stance phase (i.e., foot in contact with the floor), allowing risk to be assessed at each step, and could accurately assess four levels of risk with an accuracy of 76.6%.…”

Section: Gait Anomalymentioning

confidence: 99%

Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication

Hughes¹,

Correll²

2016

Preprint

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This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements. The purpose of this survey is to identify desired tasks to be performed in each type of material or structure (e.g., damage detection in composites), identify and compare common approaches to learning such tasks, and investigate models and training paradigms used. Machine learning approaches and common temporal features used in the domains of structural health monitoring, morphable aircraft, wearable computing and robotic skins are explored. As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered. Distributed versions of support vector machines, graphical models and mixture models developed in the field of wireless sensor networks are reviewed. Potential areas of investigation, including possible architectures for incorporating machine learning into robotic nodes, training approaches, and the possibility of using deep learning approaches for automatic feature extraction, are discussed.

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Qualitative Risk of Falling Assessment Based on Gait Abnormalities

Cited by 6 publications

References 22 publications

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Home-Based Risk of Falling Assessment Test Using a Closed-Loop Balance Model

Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication

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