Robot-based assessment of sitting and standing balance: preliminary results in Parkinson’s disease

Marchesi, Giorgia; Casadio, Maura; Ballardini, Giulia; Luca, Alice De; Squeri, Valentina; Vallone, Francesco; Giorgini, Claudia; Crea, Paola; Sanfilippo, Carlo; Saglia, Jody A.; Canessa, Andrea

doi:10.1109/icorr.2019.8779387

Cited by 11 publications

(22 citation statements)

References 20 publications

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“…For Exercises 1-5, the parameter analysis considered the total duration of the task (80 seconds for Exercise 1 and 30 seconds for Exercise 2-5). For Exercise 6, we segmented the data in different epochs, from 0.25 seconds before the start of the perturbation to 1.5 seconds after the perturbation, and we focused the analysis from the start of the perturbation until 1 second after [12]. The beginning and end of each perturbation were detected by looking at the platform's angular displacement signal.…”

Section: Robotic Data Processingmentioning

confidence: 99%

“…hunova is a new robotic device that allows the evaluation of traditional stabilometric parameters and the implementation of various dynamic environments that stimulate postural responses. Owing to its accuracy, reproducibility and thoroughness in analyzing movement and postural control, which have already been shown in subjects with Parkinson's disease [12] and elderly subjects [13], this robotic device could constitute an objective fall-risk assessment tool that may find clinical application in identifying and targeting individuals at high risk and in implementing specific training to rectify balance deficits.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

et al. 2020

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Background Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults. Methods Community-dwelling subjects aged � 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameterswere identified by means of a cross-validation method.

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Section: Robotic Data Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

et al. 2020

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“…The accelerometric signal was sampled at a frequency of 50 Hz. During the experiment, for the on line computation of the vibrotactile feedback we used the raw data, while during the off line data analysis to evaluate the postural performance of the participants we took as reference for the signal pre-processing the studies of Mancini et al (2011Mancini et al ( , 2012 and Marchesi et al (2019) and filtered the data with a zero-phase fourth-order Butterworth low-pass (LP) filter with a cut-off frequency of 3.5 Hz. In fact, these studies demonstrated that in quiet standing we can extract reliable indicators of postural stability from the accelerometric signals in the horizontal plane and that these indicators are correlated with the ones extracted from the CoP, both for healthy participants and for people with Parkinson's disease.…”

Section: Discussionmentioning

confidence: 99%

“…If those are impaired or absent, postural control and balance are compromised, increasing also the risk of falling (Maki, 1989;Brown et al, 1999;Melzer et al, 2004;Horak, 2006). These impairing sensory deficits could be caused by aging (Peterka and Black, 1989;Melzer et al, 2004), diabetes (Najafi et al, 2010), vestibular disorder or neurodegenerative diseases, such as Parkinson (Mancini et al, 2011(Mancini et al, , 2012Marchesi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Vibrotactile Feedback for Improving Standing Balance

Ballardini

Florio

Canessa

et al. 2020

Front. Bioeng. Biotechnol.

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Maintaining balance standing upright is an active process that complements the stabilizing properties of muscle stiffness with feedback control driven by independent sensory channels: proprioceptive, visual, and vestibular. Considering that the contribution of these channels is additive, we investigated to what extent providing an additional channel, based on vibrotactile stimulation, may improve balance control. This study focused only on healthy young participants for evaluating the effects of different encoding methods and the importance of the informational content. We built a device that provides a vibrotactile feedback using two vibration motors placed on the anterior and posterior part of the body, at the L5 level. The vibration was synchronized with an accelerometric measurement encoding a combination of the position and acceleration of the body center of mass in the anterior-posterior direction. The goal was to investigate the efficacy of the information encoded by this feedback in modifying postural patterns, comparing, in particular, two different encoding methods: vibration always on and vibration with a dead zone, i.e., silent in a region around the natural stance posture. We also studied if after the exposure, the participants modified their normal oscillation patterns, i.e., if there were after effects. Finally, we investigated if these effects depended on the informational content of the feedback, introducing trials with vibration unrelated to the actual postural oscillations (sham feedback). Twenty-four participants were asked to stand still with their eyes closed, alternating trials with and without vibrotactile feedback: nine were tested with vibration always on and sham feedback, fifteen with dead zone feedback. The results show that synchronized vibrotactile feedback reduces significantly the sway amplitude while increasing the frequency in anterior-posterior and medial-lateral directions. The two encoding methods had no different effects of reducing the amount of postural sway during exposure to vibration, however only the dead-zone feedback led to short-term after effects. The presence of sham vibration, instead, increased the sway amplitude, highlighting the importance of the encoded information.

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“…Some of these tests have already been used to characterize postural control strategies in patients with Parkinson's disease [36].…”

Section: Robotic Evaluationmentioning

confidence: 99%

Robotic balance assessment in community-dwelling older people with different grades of impairment of physical performance

et al. 2019

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Background Impaired physical performance is common in older adults and has been identified as a major risk factor for falls. To date, there are no conclusive data on the impairment of balance parameters in older subjects with different levels of physical performance. Aims The aim of this study was to investigate the relationship between different grades of physical performance, as assessed by the Short Physical Performance Battery (SPPB), and the multidimensional balance control parameters, as measured by means of a robotic system, in community-dwelling older adults. Methods This study enrolled subjects aged ≥ 65 years. Balance parameters were assessed by the hunova robot in static and dynamic (unstable and perturbating) conditions, in both standing and seated positions and with the eyes open/closed. Results The study population consisted of 96 subjects (62 females, mean age 77.2 ± 6.5 years). According to their SPPB scores, subjects were separated into poor performers (SPPB < 8, n = 29), intermediate performers (SPPB = 8-9, n = 29) and good performers (SPPB > 9, n = 38). Poor performers displayed significantly worse balance control, showing impaired trunk control in most of the standing and sitting balance tests, especially in dynamic (both with unstable and perturbating platform/seat) conditions. Conclusions For the first time, multidimensional balance parameters, as detected by the hunova robotic system, were significantly correlated with SPPB functional performances in community-dwelling older subjects. In addition, balance parameters in dynamic conditions proved to be more sensitive in detecting balance impairments than static tests.

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Robot-based assessment of sitting and standing balance: preliminary results in Parkinson’s disease

Cited by 11 publications

References 20 publications

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

Vibrotactile Feedback for Improving Standing Balance

Robotic balance assessment in community-dwelling older people with different grades of impairment of physical performance

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