Age-based model for metacarpophalangeal joint proprioception in elderly

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)

Lambercy

et al. 2019

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Hand function is often impaired after neurological injuries such as stroke. In order to design patient-specific rehabilitation, it is essential to quantitatively assess those deficits. Current clinical scores cannot provide the required level of detail, and most assessment devices have been developed for the proximal joints of the upper limb. This paper presents a new robotic platform for the assessment of proprioceptive, motor, and sensorimotor hand impairments. A detailed technical evaluation demonstrated the capabilities to render different haptic environments required for a comprehensive assessment battery, and showed that the device is suitable for human interaction due to its ergonomic design. A preliminary study on proprioceptive assessment using a gauge position matching task with one healthy, one stroke, and one multiple sclerosis subject showed that the robotic system is able to rapidly and sensitively quantify proprioceptive deficits, and has the potential to be integrated into the clinical settings.

Section: Discussionmentioning

confidence: 99%

Design and Characterization of a Robotic Device for the Assessment of Hand Proprioceptive, Motor, and Sensorimotor Impairments

Zbytniewska

2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)

Lambercy

et al. 2019

Self Cite

“…The latter aspect is especially of advantage for a heterogeneous population, such as neurological patients. As PEST has already been previously used for the assessment of proprioceptive function (Metzger et al, 2014;Rinderknecht, Lambercy, Raible, Liepert, & Gassert, 2017), it was chosen as adaptive sampling procedure in this work. PEST is based on a Wald sequential likelihood-ratio test (Wald, 1947), defining at each trial whether the stimulus level should remain at the same level or be decreased, respectively increased, by a specific step size, depending on the proportion of correct responses at the specific stimulus level.…”

Section: Pest: Parameter Estimation By Sequential Testingmentioning

confidence: 99%

Algorithm for improving psychophysical threshold estimates by detecting sustained inattention in experiments using PEST

Atten Percept Psychophys

Ranzani

Popp

et al. 2018

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Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.

“…This adaptive algorithm takes the judgments (also referred to as responses) of past trials into account and changes the difference between the angles accordingly, using heuristic rules to approach the difference threshold as rapidly as possible. The same proprioceptive assessment has been previously used and described in more detail in other studies with a different robotic device for the assessment of the metacarpophalangeal joint (Rinderknecht et al, 2014(Rinderknecht et al, , 2017(Rinderknecht et al, , 2018 (Rinderknecht et al, submitted). The same movement timing characteristics and parameters for the PEST algorithm were used in the present experiment, except for the maximum number of trials (start level x 0 = 5.5 • , start step ∆x 0 = ±2 • , target performance P t = 75%, Wald sequential likelihood ratio test parameter W = 1, minimum step ∆x min = ±0.1 • , maximum trials at same level trials max@x = 20, maximum trials in total trials max = 120).…”

Section: Protocol Of the Proprioceptive Assessmentmentioning

confidence: 99%

Enhancing Simulations With Intra-Subject Variability for Improved Psychophysical Assessments

Lambercy

Gassert

2018

Preprint

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Psychometric properties of perceptual assessments, like reliability, depend on the stochastic properties of psychophysical sampling procedures resulting in method variability, as well as inter-and intra-subject variability. Method variability is commonly minimized by optimizing sampling procedures through computer simulations. Inter-subject variability is inherent in the population of interest and cannot be acted upon. In contrast, intra-subject variability introduced by confounds cannot be simply quantified from experimental data, as this data also includes method variability. Therefore, this aspect is generally neglected when developing assessments. Yet, comparing method variability and intra-subject variability could give insights on whether effort should be invested in optimizing the sampling procedure, or in addressing potential confounds instead. We propose a new approach to estimate and model intra-subject variability of psychometric functions by combining computer simulations and behavioral data, and to account for it when simulating experiments. The approach was illustrated in a real-world scenario of proprioceptive difference threshold assessments. The behavioral study revealed a test-retest reliability of 0.212. Computer simulations lacking intra-subject variability predicted a reliability of 0.768, whereas the new approach including an intra-subject variability model lead to a realistic estimate of reliability (0.207). Such a model also allows computing the theoretically maximally attainable reliability (0.552) assuming an ideal sampling procedure. Comparing the reliability estimates when exclusively accounting for method variability versus intra-subject variability reveals that intra-subject variability should be reduced by addressing confounds and that only optimizing the sampling procedure may be insufficient to achieve a high reliability. The new approach also allows accelerating the development of assessments by simulating the converging behavior of the reliability confidence interval with a large number of subjects and retests without requiring additional experiments. Having such a tool of predictive value is especially important for target populations where time is scarce, such as for assessments in clinical settings.