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

Ayena, Johannes C.; Zaibi, Helmi; Otis, Martin J.‐D.; Ménélas, Bob-Antoine J.

doi:10.1109/tnsre.2015.2508960

Cited by 21 publications

(18 citation statements)

References 58 publications

(90 reference statements)

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“…All studies used tri-axial accelerometers. One study combined this sensor with force and bending sensors [58]; another with gyroscopes [60]. Sensor body locations included chest, insole (i.e., under the arch of the foot), and lower back.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Resultsmentioning

confidence: 99%

“…Fall risk estimation has a clear relevance for clinical practice [58]. Falls are common and disabling, even in early PD [61].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…Two previous studies developed a wearable sensor-based version of the OLS test for assessing balance deficits and risk of falling. In one study, the test was instrumented by using sensorized insoles for the estimation of CoP parameters (37), while in a second study a trunk-worn smartphone was used to estimate trunk displacements during task execution without considering the APA phase and balance phase separately (38). In addition, the present work is the first effort to adopt an instrumented version of the OLS test to discriminate subjects with idiopathic PD, with and without FOG, and FGD.…”

Section: Discussionmentioning

confidence: 99%

Investigation of Anticipatory Postural Adjustments during One-Leg Stance Using Inertial Sensors: Evidence from Subjects with Parkinsonism

et al. 2017

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The One-Leg Stance (OLS) test is a widely adopted tool for the clinical assessment of balance in the elderly and in subjects with neurological disorders. It was previously showed that the ability to control anticipatory postural adjustments (APAs) prior to lifting one leg is significantly impaired by idiopathic Parkinson’s disease (iPD). However, it is not known how APAs are affected by other types of parkinsonism, such as frontal gait disorders (FGD). In this study, an instrumented OLS test based on wearable inertial sensors is proposed to investigate both the initial anticipatory phase and the subsequent unipedal balance. The sensitivity and the validity of the test have been evaluated. Twenty-five subjects with iPD presenting freezing of gait (FOG), 33 with iPD without FOG, 13 with FGD, and 32 healthy elderly controls were recruited. All subjects wore three inertial sensors positioned on the posterior trunk (L4–L5), and on the left and right frontal face of the tibias. Participants were asked to lift a foot and stand on a single leg as long as possible with eyes open, as proposed by the mini-BESTest. Temporal parameters and trunk acceleration were extracted from sensors and compared among groups. The results showed that, regarding the anticipatory phase, the peak of mediolateral trunk acceleration was significantly reduced compared to healthy controls (p < 0.05) in subjects with iPD with and without FOG, but not in FGD group (p = 0.151). Regarding the balance phase duration, a significant shortening was found in the three parkinsonian groups compared to controls (p < 0.001). Moreover, balance was significantly longer (p < 0.001) in iPD subjects without FOG compared to subjects with FGD and iPD subjects presenting FOG. Strong correlations between balance duration extracted by sensors and clinical mini-BESTest scores were found (ρ > 0.74), demonstrating the method’s validity. Our findings support the validity of the proposed method for assessing the OLS test and its sensitivity in distinguishing among the tested groups. The instrumented test discriminated between healthy controls and people with parkinsonism and among the three groups with parkinsonism. The objective characterization of the initial anticipatory phase represents an interesting improvement compared to most clinical OLS tests.

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“…Traditionally, most accurate health monitoring has occurred in laboratory or hospital conditions. Several assessments, such as the 400-Meter Walk Test and the One-Leg Standing Test, exist as standards for identifying deterioration of mobility and morbidity (Vestergaard et al 2009;Ayena et al 2016). However, laboratory testing is costly and not undertaken routinely.…”

Section: Introductionmentioning

confidence: 99%

Fall prediction using behavioural modelling from sensor data in smart homes

2019

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The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however these are costly and cause inconvenience for the subject and health services. Replacing these intrusive testing methods with a passive in-home monitoring solution would provide a less time-consuming and cheaper alternative. As sensors become more readily available, machine learning models can be applied to the large amount of data they produce. This can support activity recognition, falls detection, prediction and risk determination. In this review, the growing complexity of sensor data, the required analysis, and the machine learning techniques used to determine risk of falling are explored. The current research on using passive monitoring in the home is discussed, while the viability of active monitoring using vision-based and wearable sensors is considered. Methods of fall detection, prediction and risk determination are then compared.

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

Cited by 21 publications

References 58 publications

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

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

Investigation of Anticipatory Postural Adjustments during One-Leg Stance Using Inertial Sensors: Evidence from Subjects with Parkinsonism

Fall prediction using behavioural modelling from sensor data in smart homes

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