Home-based risk of falling assessment test using a closed-loop balance model

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 (COP) 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

Validation of minimal number of force sensitive resistors to predict risk of falling during a timed up and go test

Purpose Several studies use force sensitive resistors (FSR) to compute gait and balance parameters related to falls without investigating the number of sensor units required to produce useful information. We propose a model with minimal sensors for an instrumented insole by investigating and optimizing the location and variety of sensors required to efficiently detect people at risk of falling. Methods Datasets previously recorded on twelve Parkinson’s disease (PD) participants (67.7 ± 10.07 years), nine healthy elderly (66.8 ± 8.0 years) and ten young healthy adults (28.27 ± 3.74 years) were used in this study. We compared the datasets obtained from the use of four FSRs with those of three, two, one and no FSR; each set was combined with an inertial measurement unit (IMU). Results During the walking activity, the risk of falling scores from four FSRs and IMU (acceleration in y-axis only) were not significantly different compared with two FSRs and IMU (p > 0.05), whereas significant difference was found for three FSRs and IMU and one FSR and IMU (p  0.05). Conclusions We concluded that it is feasible to estimate the risk index after reducing the number of sensing units from four to two FSRs during walking test and from four to three FSRs during sit-to-stand and stand-to-sit tests. The FSRs should be placed at strategic positions to avoid information loss

Wearable devices for classification of inadequate posture at work using neural networks

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture

Risk of falling assessment on different types of ground using the instrumented TUG

Degradation of postural control observed with aging is responsible for balance problems in the elderly, especially during the activity of walking. This gradual loss of performance generates abnormal gait, and therefore increases the risk of falling. This risk worsens in people with neuronal pathologies like Parkinson and Ataxia diseases. Many clinical tests are used for fall assessment such as the Timed up and go (TUG) test. Recently, many works have improved this test by using instrumentation, especially body-worn sensors. However, during the instrumented TUG (iTUG) test, the type of ground can influence risk of falling. In this paper, we present a new methodology for fall risk assessment based on quantitative gait parameters measurement in order to improve instrumented TUG test. The proposed measurement unit is used on different types of ground, which is known to affect human gait. The methodology is closer to the real walking environment and therefore enhances ability to differentiate risks level. Our system assesses the risk of falling's level by quantitative measurement of intrinsic gait parameters using fuzzy logic. He is also able to measure environmental parameters such as temperature, humidity and atmospheric pressure for a better evaluation of the risk in activities of daily living (ADL)

An Efficient Home-Based Risk of Falling Assessment Test Based on Smartphone and Instrumented Insole

The aim of this study is to improve and facilitate the methods used to assess risk of falling among older people at home. We propose an automatic version of One-Leg Standing (OLS) test for risk of falling assessment by using a Smartphone and an instrumented insole. For better clinical assessment tests, this study focuses on exploring methods to combine the most important parameters of risk of falling into a single score. Twenty-three volunteers participated in this study for evaluating the effectiveness of the proposed system which includes eleven elderly participants: seven healthy elderly (67.16 ± 4.24 years), four Parkinson disease (PD) subjects (70 ± 12.73 years); and twelve healthy young adults (28.27 ± 3.74 years). Our work suggests that there is an inverse relationship between OLS score proposed and risk of falling. Proposed instrumented insole and application running on Android could be useful at home as a diagnostic aid tool for analyzing the performance of elderly people in OLS test

Low-rank and sparse recovery of human gait data

Due to occlusion or detached markers, information can often be lost while capturing human motion with optical tracking systems. Based on three natural properties of human gait movement, this study presents two different approaches to recover corrupted motion data. These properties are used to define a reconstruction model combining low-rank matrix completion of the measured data with a group-sparsity prior on the marker trajectories mapped in the frequency domain. Unlike most existing approaches, the proposed methodology is fully unsupervised and does not need training data or kinematic information of the user. We evaluated our methods on four different gait datasets with various gap lengths and compared their performance with a state-of-the-art approach using principal component analysis (PCA). Our results showed recovering missing data more precisely, with a reduction of at least 2 mm in mean reconstruction error compared to the literature method. When a small number of marker trajectories is available, our findings showed a reduction of more than 14 mm for the mean reconstruction error compared to the literature approach

Risk of falling in a timed Up and Go test using an UWB radar and an instrumented insole

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

Comparing auditory, visual and vibrotactile cues in individuals with Parkinson’s disease for reducing risk of falling over different types of soil

Introduction: Several researches have demonstrated the positive benefits of auditory and visual cueing in the gait improvements among individuals with Parkinson’s disease (PD). However, few studies have evaluated the role of vibrotactile cueing when compared to auditory and visual cueing. In this paper, we compare how these stimuli affect the risk of falling while walking on six types of soil (concrete, sand, parquet, broken stone, and two types of carpet). Methods: An instrumented Timed Up and Go (iTUG) test served to evaluate how audio, visual and vibrotactile cueing can affect the risk of falling of elderly. This pilot study proposes twelve participants with PD (67.7 ± 10.07 years) and nine age-matched controls (66.8 ± 8.0 years). Both groups performed the iTUG test with and without cueing. The cueing frequency was set at 10% above the cadence computed at the lower risk level of falling (walking over the concrete). A computed risk of falling (ROFA) index has been compared to the TUG time (total TUG duration). Results: The index for evaluating the risk of falling appears to have a good reliability (ICC > 0.88) in this pilot study. In addition, the minimal detectable change (MDC) suggests that the proposed index could be more sensitive to the risk of falling variation compared to the TUG time. Moreover, while using the cueing, observed results suggest a significant decrease in the computed risk of falling compared to ‘without cueing’ for most of types of soil especially for deformable soils, which can lead to fall. Conclusion: When compared to other cueing, it seems that audio could be a better neurofeedback for reducing the risk of falling over different walking surfaces, which represent important risk factors for persons with gait disorder or loss functional autonomy

Enhancing Anthocyanin Extraction fromWine Lees: A Comprehensive Ultrasound-Assisted Optimization Study

Wine lees, an important by-product of the wine industry, pose a major environmental problem due to the enormous quantities of solid–liquid waste that are discarded annually without defined applications. In this study, the optimization of a method based on a Box–Behnken design with surface response has been carried out to obtain extracts with high anthocyanin content and potent antioxidant activity. Six variables have been considered: %EtOH, temperature, amplitude, cycle, pH, and ratio. The developed method exhibited important repeatability properties and intermediate precision, with less than 5% CV being achieved. Furthermore, these novel methods were successfully applied to diverse wine lees samples sourced from Cabernet Sauvignon and Syrah varieties (Vitis vinifera), resulting in extracts enriched with significant anthocyanin content and noteworthy antioxidant activity. Additionally, this study evaluated the influence of grape variety, fermentation type (alcoholic or malolactic), and sample treatment on anthocyanin content and antioxidant activity, providing valuable insights for further research and application in various sectors. The potential applications of these high-quality extracts extend beyond the winemaking industry, holding promise for fields like medicine, pharmaceuticals, and nutraceuticals, thus promoting a circular economy and mitigating environmental contamination

Freezing of gait and fall detection in Parkinson’s disease using wearable sensors:a systematic review

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets