3-D trajectory of body sway angles: A technique for quantifying postural stability

Hejda, Jan; Čakrt, Ondřej; Socha, Vladimír; Schlenker, Jakub; Kutílek, Patrik

doi:10.1016/j.bbe.2015.02.001

Cited by 11 publications

(11 citation statements)

References 39 publications

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“…In the case of AV, the method's high sensitivity to a motion corresponds with arm tremors. It is because the AV calculation relates not only to a range of measured data but also to the intensity of data changes occurring in the segment measured during the specific time interval [35]. Looking at the differences between the forearm and upper arm results, the AV-based method identified significant differences between the D and N arm.…”

Section: Discussionmentioning

confidence: 99%

“…To calculate the average velocity, the total trajectory length of the plot of variables is used [33,34]. The formulas, used to calculate the trajectory length, are based on the sum of Euclidean distances between consecutive data points in Euclidean 3-D space, as described in [35]. AV is measured in m•s -2 .…”

Section: Fig 2: 3-d Plot Of Linear Accelerations During 60 S With Sampling Frequency 100 Hzmentioning

confidence: 99%

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Methods Evaluating Upper Arm and Forearm Movement During a Quiet Stance

et al. 2019

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The article focuses on designing methods for quantitative assessment of the postural stability in a quiet stance by measuring segments of the appendicular skeleton, namely upper and forearms by inertial measurement units (IMU). Although an array of quantitative analysis methods assessing data of postural stability in the quiet stance exist by measuring the head and trunk movement, these methods have not been used to date to assess the behaviour of appendicular skeleton segments, namely the upper limbs. The applicability of methods assessing arm movement during the quiet stance has been verified by comparing the values of healthy subjects performing various stance tasks. The tests determined the quantitative evaluation of acceleration measured on individual anatomical axes. The quantities included: the volume of a convex polyhedron (PV), the volume of confidence ellipsoid (EV) and average velocity (AV) obtained by plotting three accelerations against each other. The most important findings in this study concern significant differences of PV and AV between dominant and non-dominant upper extremities and significant differences of EV, PV and AV between the data measured with a subject's eyes closed and open. Higher values of indicators were in the non-dominant extremities when subjects were measured with closed eyes. Interestingly, statistically significant differences between dominant and non-dominant arm movements were documented in PV and AV cases. This is due to the PV calculation being more sensitive to random deviations, i.e. the range of measured data, since the polyhedron bounds all the measured data, as opposed to the method, where the ellipse bounds only 95% of the measured data. In the case of the AV method, it is due to higher sensitivity to movements corresponding with arm tremors; the AV calculation relates not only to the range of measured data but, above all, to the intensity of data changes in the segment measured in a particular space and time interval. These conclusions demonstrate that it is possible to apply the proposed methods in the assessment of arm movement during a quiet stance since the differences between individual stance tasks and the dominant and non-dominant arms in specific cases of quiet stance have been identified. These conclusions also indicate a potentially more extensive medical application of the proposed quantitative data evaluation obtained from IMU, for example, within the rehabilitation process of injured appendicular skeleton segments. The use of cheaper IMU methods in mobile phones or watches can be of significant benefit in measuring the segmental movement of the appendicular skeleton in quiet stance. The methods outlined in this paper have remarkable potential in the field of telemedicine.

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Section: Discussionmentioning

confidence: 99%

Section: Fig 2: 3-d Plot Of Linear Accelerations During 60 S With Sampling Frequency 100 Hzmentioning

confidence: 99%

Methods Evaluating Upper Arm and Forearm Movement During a Quiet Stance

et al. 2019

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“…The three-dimensional sway angle of the torso was calculated from a Kalman filter synthesis of the accelerometer and gyroscope data from the IMU mounted on the sternum. The total path length of the postural sway angle trajectory may be considered a metric of pathological balance control, [35] and it was speculated that a longer path length corresponded to worse balance (i.e., more trunk movement). The total length of the postural sway angles was derived from the middle 30 seconds of each 60-second balance trial.…”

Section: Discussionmentioning

confidence: 99%

Investigating the Effects of the SurroGait Rx™ Device on Postural Stability, Gait, and MSIS-29 Outcomes in People with Multiple Sclerosis

Vienneau¹,

Bauman²,

Nigg³

et al. 2017

BJSTR

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The objective of this study was to investigate eight week training effects, followed by eight weeks of washout effects with a newly developed medical device (the SurroGait Rx™ [Orpyx Medical Technologies Inc., Calgary, Canada]) on physical and psychological impacts of multiple sclerosis (MS), gait, and balance parameters in individuals with MS. Seven individuals with MS completed the 16-week protocol, which included baseline, post-training (8 weeks) and post-washout (16 weeks) sessions. During each session, impact of MS on the patient (MSIS-29), gait (T25FW, stride variables), and balance (postural sway trajectory) parameters were assessed. Results showed significant improvements in the MSIS-29 scores after training, no effect on gait variables, and longer postural sway trajectories with the device on versus off. There were no significant training effects in the balance tasks. Future studies are warranted using individuals with more advanced cases of MS to more fully understand the potential benefits of the SurroGait Rx.

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“…Previous studies using wearable sensors have investigated balance impairment in Parkinson's disease [114,122,124,125,[156][157][158][159][160][161][162][163][164][165][166][167][168], multiple sclerosis [118,146,[169][170][171][172][173][174][175][176][177], stroke [52,[178][179][180][181][182][183][184], traumatic brain injuries [123,126,[185][186][187][188][189], cerebellar ataxia [130,[190][191][192][193][194]…”

Section: Wearable Technologies In Neurological Disordersmentioning

confidence: 99%

“…Regarding the number and body location of sensors, authors have used 1 to 8 inertial devices and multiple body segments, including the upper (10 studies) and lower limbs (21 studies), head (1 study), trunk (18 studies), and waist (48 studies), depending on the static or dynamic postural task chosen for balance assessment. Indeed, some authors who investigated postural evaluation during gait (e.g., [122,146,161,169,172,175]) and instrumented versions of clinical tests, such as the push and release test [171] and the Fukuda Stepping Test [182], have usually applied more sensors than those evaluating static balance during upright stance (e.g., [52,114,125,157,158,163,177,[183][184][185]188,[191][192][193][194]199,203,204]. However, despite one study [204], all authors have included the lumbo-sacral region as the main location of inertial sensors for the analysis of postural sway, according to the COM position.…”

Section: Wearable Technologies In Neurological Disordersmentioning

confidence: 99%

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

Zampogna

Mileti

Palermo

et al. 2020

Sensors

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Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

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3-D trajectory of body sway angles: A technique for quantifying postural stability

Cited by 11 publications

References 39 publications

Methods Evaluating Upper Arm and Forearm Movement During a Quiet Stance

Methods Evaluating Upper Arm and Forearm Movement During a Quiet Stance

Investigating the Effects of the SurroGait Rx™ Device on Postural Stability, Gait, and MSIS-29 Outcomes in People with Multiple Sclerosis

Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders

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