Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm

Bargiotas, Ioannis; Audiffren, Julien; Vayatis, Nicolas; Vidal, Pierre‐Paul; Yelnik, A.; Ricard, Damien

doi:10.5201/ipol.2019.251

Cited by 2 publications

(3 citation statements)

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“…They trajectories’ time-blocks were grouped with a shallow soft unsupervised classification in two clusters (UBs/QBs) based on the Expectation–Maximization algorithm (EM) for Gaussian Mixture Models (GMM). After the reunification of the blocks, the trajectories could provide the individual’s risk of fall and interesting trajectory visualization [ 58 ].…”

Section: Methodological Approach and Resultsmentioning

confidence: 99%

“…Additionally, monitoring the individual's postural control progress using balance assessment scales is not trivial [55]. Recent studies from our lab [47][48][49][56][57][58][59] and others [60] proposed that a linear and non-linear combination (using machine learning methodologies) of many global or local posturographic parameters derived from the Centre of Pressure (CoP) trajectories can classify fallers and non-fallers. It is important to stress that these algorithms can evaluate the risk of fall in individuals who had not experienced any fall before the acquisition on the force platform (standing eyes open and eyes closed for 30 s each time).…”

Section: Fall Prediction Via Posturographymentioning

confidence: 99%

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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%

Section: Fall Prediction Via Posturographymentioning

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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“…A machine-learning based approach has been recently proposed (Bargiotas et al, 2018) then described with a multidimensional vector with established postural features from the literature (see (Quijoux et al, 2021) for relevant features); each block (its multidimensional description) is then scored, using the posterior probability to belong to the QB and UB clusters. The technical details and the performance of the algorithm were presented in an IPOL article (Bargiotas et al, 2019) accompanied by a code in open access as well as an IPOL online demo, which enables the user to provide any statokinesigram, and outputs a score and a unidimensional representation of QB and UB periods.…”

Section: Signal Processing Methods and Datasets For Biomedical Applic...mentioning

confidence: 99%

The approach to reproducible research of the Image Processing On Line (IPOL) journal

2022

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Scientific research requires that results can be accessed, tested, and replicated by other researchers. In general, performing reproducible research is not simple and it can be even impossible (for example, in the case of astrophysics replicating singular events if even out of the control of the researcher). However, in computational sciences there is no special hindrance to reproduce, repeat, and compare results. In 2009 the Image Processing On Line (IPOL) journal was founded as a modest contribution to implement reproducible research in the Image Processing field, and then expanded to more general signal-processing algorithms, such as video or physiological signal processing, among others. We re-defined the concept of publication, which is no longer just the article, but the combination of the article, its source code, and any associated data needed to reproduce the results, everything as an indivisible whole. In this article, we present the concept of IPOL, our approach to reproducible research, the challenges we had to address and solve, and some applications other than image processing in IPOL (biomedical, educational). We also present other IPOL inspired initiatives before discussing the opportunity of performing reproducible research in these fields, as well as the advantages and difficulties.

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Local Assessment of Statokinesigram Dynamics in Time: An in-Depth Look at the Scoring Algorithm

Abstract: In this work we discuss the multidimensional scoring approach proposed by Bargiotas and al.

Cited by 2 publications

References 31 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

The approach to reproducible research of the Image Processing On Line (IPOL) journal

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