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.

Arterial pulse wave velocity (PWV) is associated with increased mortality in aging and disease. Several studies have shown the accuracy of applanation tonometry carotid-femoral PWV (Cf-PWV) and the relevance of evaluating central aorta stiffness using 2D cardiovascular magnetic resonance (CMR) to estimate PWV, and aortic distensibility-derived PWV through the theoretical Bramwell-Hill model (BH-PWV). Our aim was to compare various methods of aortic PWV (aoPWV) estimation from 4D flow CMR, in terms of associations with age, Cf-PWV, BH-PWV and left ventricular (LV) mass-to-volume ratio while evaluating inter-observer reproducibility and robustness to temporal resolution. We studied 47 healthy subjects (49.5 ± 18 years) who underwent Cf-PWV and CMR including aortic 4D flow CMR as well as 2D cine SSFP for BH-PWV and LV mass-to-volume ratio estimation. The aorta was semi-automatically segmented from 4D flow data, and mean velocity waveforms were estimated in 25 planes perpendicular to the aortic centerline. 4D flow CMR aoPWV was calculated: using velocity curves at two locations, namely ascending aorta (AAo) and distal descending aorta (DAo) aorta (S1, 2D-like strategy), or using all velocity curves along the entire aortic centreline (3D-like strategies) with iterative transit time (TT) estimates (S2) or a plane fitting of velocity curves systolic upslope (S3). For S1 and S2, TT was calculated using three approaches: cross-correlation (TTc), wavelets (TTw) and Fourier transforms (TTf). Intra-class correlation coefficients (ICC) and Bland-Altman biases (BA) were used to evaluate inter-observer reproducibility and effect of lower temporal resolution. 4D flow CMR aoPWV estimates were significantly (p < 0.05) correlated to the CMR-independent Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with the strongest correlations for the 3D-like strategy using wavelets TT (S2-TTw) (R = 0.62, 0.65, 0.77 and 0.52, respectively, all p < 0.001). S2-TTw was also highly reproducible (ICC = 0.99, BA = 0.09 m/s) and robust to lower temporal resolution (ICC = 0.97, BA = 0.15 m/s). Reproducible 4D flow CMR aoPWV estimates can be obtained using full 3D aortic coverage. Such 4D flow CMR stiffness measures were significantly associated with Cf-PWV, BH-PWV, age and LV mass-to-volume ratio, with a slight superiority of the 3D strategy using wavelets transit time (S2-TTw).

Almost one third of population 65 years-old and older faces at least one fall per year. An accurate evaluation of the risk of fall through simple and easy-to-use measurements is an important issue in current clinic. A common way to evaluate balance in posturography is through the recording of the centre-of-pressure (CoP) displacement (statokinesigram) with force platforms. A variety of indices have been proposed to differentiate fallers from non fallers. However, no agreement has been reached whether these analyses alone can explain sufficiently the complex synergies of postural control. In this work, we study the statokinesigrams of 84 elderly subjects (80.3+− 6.4 years old), which had no impairment related to balance control. Each subject was recorded 25 seconds with eyes open and 25 seconds with eyes closed and information pertaining to the presence of problems of balance, such as fall, in the last six months, was collected. Five descriptors of the statokinesigrams were computed for each record, and a Ranking Forest algorithm was used to combine those features in order to evaluate each subject’s balance with a score. A classical train-test split approach was used to evaluate the performance of the method through ROC analysis. ROC analysis showed that the performance of each descriptor separately was close to a random classifier (AUC between 0.49 and 0.54). On the other hand, the score obtained by our method reached an AUC of 0.75 on the test set, consistent over multiple train-test split. This non linear multi-dimensional approach seems appropriate in evaluating complex postural control.

Aortic pulse wave velocity (PWV), which substantially increases with arterial stiffness and aging, is a major predictor of cardiovascular mortality. It is commonly estimated using applanation tonometry at carotid and femoral arterial sites (cfPWV). More recently, several cardiovascular magnetic resonance (CMR) studies have focused on the measurement of aortic arch PWV (archPWV). Although the excellent anatomical coverage of CMR offers reliable segmental measurement of arterial length, accurate transit time (TT) determination remains a challenge. Recently, it has been demonstrated that Fourier-based methods were more robust to low temporal resolution than time-based approaches. We developed a wavelet-based method, which enables temporal localization of signal frequencies, to estimate TT from ascending and descending aortic CMR flow curves. This method (archPWVWU) combines the robustness of Fourier-based methods to low temporal resolution with the possibility to restrict the analysis to the reflectionless systolic upslope. We compared this method with Fourier-based (archPWVF) and time domain upslope (archPWVTU) methods in relation to linear correlations with age, cfPWV and effects of decreasing temporal resolution by factors of 2, 3 and 4. We studied 71 healthy subjects (45 ± 15 years, 29 females) who underwent CMR velocity acquisitions and cfPWV measurements. Comparison with age resulted in the highest correlation for the wavelet-based method (archPWVWU:r = 0.84,p < 0.001; archPWVTU:r = 0.74,p < 0.001; archPWVF:r = 0.63,p < 0.001). Associations with cfPWV resulted in the highest correlations for upslope techniques whether based on wavelet (archPWVWU:r = 0.58,p < 0.001) or time (archPWVTU:r = 0.58,p < 0.001) approach. Furthermore, while decreasing temporal resolution by 4-fold induced only a minor decrease in correlation of both archPWVWU (r decreased from 0.84 to 0.80) and archPWVF (r decreased from 0.63 to 0.51) with age, it induced a major decrease for the archPWVTU age relationship (r decreased from 0.74 to 0.38). By CMR, measurement of aortic arch flow TT using systolic upslopes resulted in a better correlation with age and cfPWV, as compared to the Fourier-based approach applied on the entire cardiac cycle. Furthermore, methods based on harmonic decomposition were less affected by low temporal resolution. Since the proposed wavelet approach combines these two advantages, it might help to overcome current technical limitations related to CMR temporal resolution and evaluation of patients with highly stiff arteries.

The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or blocks, while highlighting those which are more indicative to the general individual’s class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson’s disease.

Compare seven previous methods for the estimation of aortic characteristic impedance, which contributes to left ventricle pulsatile load, from phase-contrast cardiovascular magnetic resonance (CMR) and applanation tonometry data. We studied 77 healthy (43 ± 16 years) individuals and 16 hypertensive (61 ± 9 years) patients, who consecutively underwent ascending aorta CMR and carotid tonometry, resulting in flow and pressure waveforms, respectively. Characteristic impedance was semi-automatically estimated in time domain from these latter waveforms, using seven methods. The methods were based on the following: methods 1-4, magnitudes at specific times; method 5, early-systolic up-slope; method 6, time-derivatives peak; and method 7, pressure-flow loop early-systolic slope. Aortic characteristic impedance was significantly increased in hypertensive patients when compared to elderly controls (n = 32) with a similar mean age of (59 ± 8 years) when using methods based on 95% of peak flow, up-slopes, and derivatives peaks (P < 0.05). When considering healthy individuals, impedance indices were significantly correlated to central pulse pressure for all methods (P < 0.005). Finally, characteristic impedance was correlated to the frequency-domain reference values (r > 0.65, P < 0.0001), with a slight superiority for the same three methods as above (r > 0.82, P < 0.0001). This is the first study demonstrating phase-contrast CMR and tonometry usefulness in aortic characteristic impedance temporal estimation. Methods based on 95% of peak flow, as well as those based on derivative peaks and up-slopes, which are fast and independent of curve preprocessing, were slightly superior. They can be easily integrated in a clinical workflow and may help to understand the complementarity of this pulsatile index with other CMR aortic geometry and stiffness measures in the setting of left ventricle-aortic coupling.