This paper presents a novel generalized computational framework for quantitative kinematic evaluation of movement in a rehabilitation clinic setting. The framework integrates clinical knowledge and computational data-driven analysis together in a systematic manner. The framework provides three key benefits to rehabilitation: (a) the resulting continuous normalized measure allows the clinician to monitor movement quality on a fine scale and easily compare impairments across participants, (b) the framework reveals the effect of individual movement components on the composite movement performance helping the clinician decide the training foci, and (c) the evaluation runs in real-time, which allows the clinician to constantly track a patient"s progress and make appropriate adaptations to the therapy protocol. The creation of such an evaluation is difficult because of the sparse amount of recorded clinical observations, the high dimensionality of movement and high variations in subject"s performance. We address these issues by modeling the evaluation function as linear combination of multiple normalized kinematic attributes y=Σw i φ i (x i) and estimating the attribute normalization function φ i (•) by integrating distributions of idealized movement and deviated movement. The weights w i are derived from a therapist"s pair-wise comparison using a modified RankSVM algorithm. We have applied this framework to evaluate upper limb movement for stroke survivors with excellent results-the evaluation results are highly correlated to the therapist"s observations.
Laminar optical tomography (LOT) is a new three-dimensional in vivo functional optical imaging technique. Adopting a microscopy-based setup and diffuse optical tomography (DOT) imaging principles, LOT can perform both absorption- and fluorescence-contrast imaging with higher resolution (100–200 μm) than DOT and deeper penetration (2–3 mm) than laser scanning microscopy. These features, as well as a large field of view and acquisition speeds up to 100 frames per second, make LOT suitable for depth-resolved imaging of stratified tissues such as retina, skin, endothelial tissues and the cortex of the brain. In this paper, we provide a detailed description of a new LOT system design capable of imaging both absorption and fluorescence contrast, and present characterization of its performance using phantom studies.
Introduction: Vestibular migraine (VM) is the most common cause of episodic vertigo in children. We summarize the clinical findings and laboratory test results in a cohort of children and adolescents with VM. We discuss the limitations of current classification criteria for dizzy children.Methods: A retrospective chart analysis was performed on 118 children with migraine related vertigo at a tertiary care center. Patients were grouped in the following categories: (1) definite vestibular migraine (dVM); (2) probable vestibular migraine (pVM); (3) suspected vestibular migraine (sVM); (4) benign paroxysmal vertigo (BPV); and (5) migraine with/without aura (oM) plus vertigo/dizziness according to the International Classification of Headache Disorders, 3rd edition (beta version).Results: The mean age of all patients was 12 ± 3 years (range 3–18 years, 70 females). 36 patients (30%) fulfilled criteria for dVM, 33 (28%) for pVM, 34 (29%) for sVM, 7 (6%) for BPV, and 8 (7%) for oM. Somatoform vertigo (SV) co-occurred in 27% of patients. Episodic syndromes were reported in 8%; the family history of migraine was positive in 65%. Mild central ocular motor signs were found in 24% (most frequently horizontal saccadic pursuit). Laboratory tests showed that about 20% had pathological function of the horizontal vestibulo-ocular reflex, and almost 50% had abnormal postural sway patterns.Conclusion: Patients with definite, probable, and suspected VM do not differ in the frequency of ocular motor, vestibular, or postural abnormalities. VM is the best explanation for their symptoms. It is essential to establish diagnostic criteria in clinical studies. In clinical practice, however, the most reasonable diagnosis should be made in order to begin treatment. Such a procedure also minimizes the fear of the parents and children, reduces the need to interrupt leisure time and school activities, and prevents the development of SV.
BackgroundAlthough principles based in motor learning, rehabilitation, and human-computer interfaces can guide the design of effective interactive systems for rehabilitation, a unified approach that connects these key principles into an integrated design, and can form a methodology that can be generalized to interactive stroke rehabilitation, is presently unavailable.ResultsThis paper integrates phenomenological approaches to interaction and embodied knowledge with rehabilitation practices and theories to achieve the basis for a methodology that can support effective adaptive, interactive rehabilitation. Our resulting methodology provides guidelines for the development of an action representation, quantification of action, and the design of interactive feedback. As Part I of a two-part series, this paper presents key principles of the unified approach. Part II then describes the application of this approach within the implementation of the Adaptive Mixed Reality Rehabilitation (AMRR) system for stroke rehabilitation.ConclusionsThe accompanying principles for composing novel mixed reality environments for stroke rehabilitation can advance the design and implementation of effective mixed reality systems for the clinical setting, and ultimately be adapted for home-based application. They furthermore can be applied to other rehabilitation needs beyond stroke.
BackgroundFew existing interactive rehabilitation systems can effectively communicate multiple aspects of movement performance simultaneously, in a manner that appropriately adapts across various training scenarios. In order to address the need for such systems within stroke rehabilitation training, a unified approach for designing interactive systems for upper limb rehabilitation of stroke survivors has been developed and applied for the implementation of an Adaptive Mixed Reality Rehabilitation (AMRR) System.ResultsThe AMRR system provides computational evaluation and multimedia feedback for the upper limb rehabilitation of stroke survivors. A participant's movements are tracked by motion capture technology and evaluated by computational means. The resulting data are used to generate interactive media-based feedback that communicates to the participant detailed, intuitive evaluations of his performance. This article describes how the AMRR system's interactive feedback is designed to address specific movement challenges faced by stroke survivors. Multimedia examples are provided to illustrate each feedback component. Supportive data are provided for three participants of varying impairment levels to demonstrate the system's ability to train both targeted and integrated aspects of movement.ConclusionsThe AMRR system supports training of multiple movement aspects together or in isolation, within adaptable sequences, through cohesive feedback that is based on formalized compositional design principles. From preliminary analysis of the data, we infer that the system's ability to train multiple foci together or in isolation in adaptable sequences, utilizing appropriately designed feedback, can lead to functional improvement. The evaluation and feedback frameworks established within the AMRR system will be applied to the development of a novel home-based system to provide an engaging yet low-cost extension of training for longer periods of time.
Interactive neurorehabilitation (INR) systems provide therapy that can evaluate and deliver feedback on a patient's movement computationally. There are currently many approaches to INR design and implementation, without a clear indication of which methods to utilize best. This article presents key interactive computing, motor learning, and media arts concepts utilized by an interdisciplinary group to develop adaptive, mixed reality INR systems for upper extremity therapy of patients with stroke. Two INR systems are used as examples to show how the concepts can be applied within: (1) a small-scale INR clinical study that achieved integrated improvement of movement quality and functionality through continuously supervised therapy and (2) a pilot study that achieved improvement of clinical scores with minimal supervision. The notion is proposed that some of the successful approaches developed and tested within these systems can form the basis of a scalable design methodology for other INR systems. A coherent approach to INR design is needed to facilitate the use of the systems by physical therapists, increase the number of successful INR studies, and generate rich clinical data that can inform the development of best practices for use of INR in physical therapy.
This paper proposes a computational framework for movement quality assessment using a decision tree model that can potentially assist a physical therapist in a telerehabilitation context. Using a dataset of key kinematic attributes collected from eight stroke survivors, we demonstrate that the framework can be reliably used for movement quality assessment of a reach-to-grasp cone task, an activity commonly used in upper extremity stroke rehabilitation therapy. The proposed framework is capable of providing movement quality scores that are highly correlated to the ratings provided by therapists, who used a custom rating rubric created by rehabilitation experts. Our hypothesis is that a decision tree model could be easily utilized by therapists as a potential assistive tool, especially in evaluating movement quality on a large-scale dataset collected during unsupervised rehabilitation (e.g., training at the home), thereby reducing the time and cost of rehabilitation treatment.
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