Accuracy of gait and posture classification using movement sensors in individuals with mobility impairment after stroke

Background: Stroke leads to motor impairment which reduces physical activity, negatively affects social participation, and increases the risk of secondary cardiovascular events. Continuous monitoring of physical activity with motion sensors is promising to allow the prescription of tailored treatments in a timely manner. Accurate classification of gait activities and body posture is necessary to extract actionable information for outcome measures from unstructured motion data. We here develop and validate a solution for various sensor configurations specifically for a stroke population. Methods: Video and movement sensor data (locations: wrists, ankles, and chest) were collected from fourteen stroke survivors with motor impairment who performed real-life activities in their home environment. Video data were labeled for five classes of gait and body postures and three classes of transitions that served as ground truth. We trained support vector machine (SVM), logistic regression (LR), and k-nearest neighbor (kNN) models to identify gait bouts only or gait and posture. Model performance was assessed by the nested leave-one-subject-out protocol and compared across five different sensor placement configurations. Results: Our method achieved very good performance when predicting real-life gait versus non-gait (Gait classification) with an accuracy between 85% and 93% across sensor configurations, using SVM and LR modeling. On the much more challenging task of discriminating between the body postures lying, sitting, and standing as well as walking, and stair ascent/descent (Gait and postures classification), our method achieves accuracies between 80% and 86% with at least one ankle and wrist sensor attached unilaterally. The Gait and postures classification performance between SVM and LR was equivalent but superior to kNN. Conclusion: This work presents a comparison of performance when classifying Gait and body postures in post-stroke individuals with different sensor configurations, which provide options for subsequent outcome evaluation. We achieved accurate classification of gait and postures performed in a real-life setting by individuals with a wide range of motor impairments due to stroke. This validated classifier will hopefully prove a useful resource to researchers and clinicians in the increasingly important field of digital health in the form of remote movement monitoring using motion sensors

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75-82%) to conventional thresholds (58-66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors' real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

De schijnwerpers op een onderbelicht fundament : de rol en invulling van het verenigingsbelang

status: publishe

Innovation as a vehicle for improving socially vulnerable groups' access to basic provisions : a research note on the development of a questionnaire module

Reducing inequality and social exclusion is a challenging task that will require a joint effort by all societal stakeholders, including not-for-profit and for-profit organizations. In order to develop and evaluate policy actions in this area, monitoring the contribution of these for-profit and not-for-profit organizations to a more inclusive society becomes crucial. This research note describes the development, cognitive pretesting, and large-scale empirical testing of a module that can be included in (inter)national innovation surveys. The module measures whether not-for-profits' and for-profits' innovation activities improve vulnerable groups' access to basic provisions. It also provides insights in the main drivers for improving beneficiaries' access to basic provisions through innovation and in the types and numbers of beneficiaries reached. The module was tested in the context of the Community Innovation Survey in Flanders, Belgium

Corrigendum to “Evidence for solar influence in a Holocene speleothem record (Père Nöel cave, SE Belgium)” (Quaternary Science Reviews (2018) 192 (249–262), (S0277379118302038) (10.1016/j.quascirev.2018.05.039))

peer reviewedWhen this paper was originally published, there was an error in Fig. 7g. Indeed the curve of percentage of hematite-stained grains measured on North Atlantic MC52-VM29-191 core, published by Bond et al. (2001), was inverted. Please find the correct Fig. 7 below: [Figure presented] Fig. 7 corrected: (a), (b) and (c) PN trace elements contents (Sr = red line; Ba = green line; and Mg = black line) in comparison to (d) PN Stalagmite δ 18 O (blue line), (e) δ 18 O record from Bunker Cave-W Germany (Fohlmeister et al., 2012), (f) δ 18 O record from lake Ammersee- Germany (von Grafenstein et al., 1999), (g) hematite-stained grains from the North Atlantic (Bond et al., 2001), (h) frequency of wet/dry events in the Northern Hemisphere as defined by Wanner et al. (2014). The blue horizontal bars mark cold events. In the paper, Fig. 7 allows us to compare the trace elements Ba, Sr, Mg and δ 18 O isotopes results of Père Noël (PN) stalagmite with other continental and marine records. In the figure, the blue lines underline the 6 geochemical events measured in our speleothem PN record that we attribute to cold events. As a first point, we note that most of the PN events are consistent with some of the dry events (Fig. 7h) defined by Wanner et al. (2014) for the Northern Hemisphere. The correspondence is clear for 5 intervals corresponding to the 2.6–2.8, 4.6–4.8, 6.2–6.4, 8.1–8.3 and 9.3–9.5 Wanner events (no data for the oldest event at 10.3–10.7). Second, we compare the PN record with the abundance of hematite-stained grains in core VM29-191 (Bond et al., 2001). This curve, that was used as one of the drift ice indices, represents the North Atlantic cold events. (Note the comparison with the stacked curve would lead to the same conclusion). At least the 3 PN events at 4.6–4.8, 6.2–6.4 and 9.3–9.5 ka BP occur in a time interval consistent with the Bond events 3, 4 and 6, respectively. In the manuscript, the text must be corrected (see lines 416–418):“During these three intervals (i.e., around 10.5, between 9.5 and 9.2 and between 8.4 and 8.2 ka BP), the wavelet spectrum of trace elements shows that the solar imprint is well present in the PN stalagmite (Fig. 6). Those three intervals may correspond to Bond cycles (Bond et al., 2001)”. Indeed only the 8.4-8.2 ka BP and 9.3–9.5 event may be related to Bond cycle since the 10.5 event is not covered by the Bond curve. There is no peak-to-peak correspondence between the two curves than are characterized by different age uncertainties. PN record presents an age uncertainty estimated at ±11–51 years at 2σ level for the interval 2–9.5 ka BP (see Table 1 in our manuscript). The chronology of the Bond et al. records presents an age error estimated at 50 years (1σ). Whatever the 8 Bond events display, as stated in the Fig. 2 caption of Bond et al. (2001), “a millennial-scale cycles regarded as part of the North Atlantic's 1500-year cycle”. Such millenial cyclicity was revealed in our PN trace element record by a 912–1029 yr peak in the wavelet spectral power (Fig. 6). Such periodicity may suggest some solar forcing in our PN record. The authors apologise for any inconvenience caused. © 2019 Elsevier Lt

Union libre: Commentaire pratique

info:eu-repo/semantics/publishe

Dynamique des populations de tiques et liaison avec les facteurs environnementaux

International audienc

Dynamique des populations de tiques et liaison avec les facteurs environnementaux

International audienc

Mechanism of Fluorescence and Conformational Changes of the Sarcoplasmic Calcium Binding Protein of the Sand Worm Nereis diversicolor upon Ca(2+) or Mg(2+) Binding

The calcium-binding protein isolated from the sarcoplasm of the muscles of the sand worm Nereis diversicolor has four EF-hands and three active binding sites for Ca(2+) or Mg(2+). Nereis diversicolor sarcoplasmic calcium-binding protein contains three tryptophan residues at positions 4, 57, and 170, respectively. The Wt protein shows a very limited fluorescence increase upon binding of Ca(2+) or Mg(2+). Single-tryptophan-containing mutants were produced and purified. The fluorescence titrations of these mutants show a limited decrease of the affinity for calcium, but no alterations of the cooperativity. Upon adding calcium, Trp170 shows a strong fluorescence increase, Trp57 an extensive fluorescence decrease, and Trp4 shows no fluorescence change. Therefore mutant W4F/W170F is ideally suited to analyze the fluorescence titrations and to study the binding mechanism. Mutations of the calcium ligands at the z-position in the three binding sites show no effect at site I and a total loss of cooperativity at sites III and IV. The quenching of Trp57 upon calcium binding is dependent on the presence of arginine R25, but this residue is not just a simple dynamic quencher. The role of the salt bridge R25-D58 is also investigated