Home-based physical therapy with an interactive computer vision system

In this paper, we present ExerciseCheck. ExerciseCheck is an interactive computer vision system that is sufficiently modular to work with different sources of human pose estimates, i.e., estimates from deep or traditional models that interpret RGB or RGB-D camera input. In a pilot study, we first compare the pose estimates produced by four deep models based on RGB input with those of the MS Kinect based on RGB-D data. The results indicate a performance gap that required us to choose the MS Kinect when we tested ExerciseCheck with Parkinson’s disease patients in their homes. ExerciseCheck is capable of customizing exercises, capturing exercise information, evaluating patient performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient throughout this period. We conclude that ExerciseCheck is a user-friendly computer vision application that can assist patients by providing motivation and guidance to ensure correct execution of the required exercises. Our results also suggest that while there has been considerable progress in the field of pose estimation using deep learning, current deep learning models are not fully ready to replace RGB-D sensors, especially when the exercises involved are complex, and the patient population being accounted for has to be carefully tracked for its “active range of motion.”Published versio

Updated Poster Presentation Abstract (n = 58) From 2020 Combined Sections Meeting Of The American Physical Therapy Association: How Well Do Clinical Walking Measures Predict Natural Walking Behavior In Parkinson Disease?

Declines in the amount and intensity of natural walking behavior in people with Parkinson disease (PD) may precede declines in motor behavior, gait, and balance. Physical interventions targeting walking behavior in PD may have the greatest impact on slowing the progression of disability. Despite a lack of supporting evidence, however, clinicians may be more likely to rely on quick performance measures of walking speed, capacity, and balance to make inferences about a patient’s walking health, rather than direct measures of natural walking behavior. Our primary purpose, therefore, was to examine the extent to which clinical walking measures might predict natural walking behavior in early to mid-stage PD. Secondarily we sought to explore differences in the predictive capability of clinical measures between relatively less active and more active participants

Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury.

<p>Abstract</p> <p>Background</p> <p>Patients with traumatic brain injury (TBI) often present with significant cognitive deficits without corresponding evidence of cortical damage on neuroradiological examinations. One explanation for this puzzling observation is that the diffuse cortical abnormalities that characterize TBI are difficult to detect with standard imaging procedures. Here we investigated a patient with severe TBI-related cognitive impairments whose scan was interpreted as normal by a board-certified radiologist in order to determine if quantitative neuroimaging could detect cortical abnormalities not evident with standard neuroimaging procedures.</p> <p>Methods</p> <p>Cortical abnormalities were quantified using multimodal surfaced-based morphometry (MSBM) that statistically combined information from high-resolution structural MRI and diffusion tensor imaging (DTI). Normal values of cortical anatomy and cortical and pericortical DTI properties were quantified in a population of 43 healthy control subjects. Corresponding measures from the patient were obtained in two independent imaging sessions. These data were quantified using both the average values for each lobe and the measurements from each point on the cortical surface. The results were statistically analyzed as z-scores from the mean with a p < 0.05 criterion, corrected for multiple comparisons. False positive rates were verified by comparing the data from each control subject with the data from the remaining control population using identical statistical procedures.</p> <p>Results</p> <p>The TBI patient showed significant regional abnormalities in cortical thickness, gray matter diffusivity and pericortical white matter integrity that replicated across imaging sessions. Consistent with the patient's impaired performance on neuropsychological tests of executive function, cortical abnormalities were most pronounced in the frontal lobes.</p> <p>Conclusions</p> <p>MSBM is a promising tool for detecting subtle cortical abnormalities with high sensitivity and selectivity. MSBM may be particularly useful in evaluating cortical structure in TBI and other neurological conditions that produce diffuse abnormalities in both cortical structure and tissue properties.</p

A Genome-wide Association Study of Autism Using the Simons Simplex Collection: Does Reducing Phenotypic Heterogeneity in Autism Increase Genetic Homogeneity?

BACKGROUND: Phenotypic heterogeneity in autism has long been conjectured to be a major hindrance to the discovery of genetic risk factors, leading to numerous attempts to stratify children based on phenotype to increase power of discovery studies. This approach, however, is based on the hypothesis that phenotypic heterogeneity closely maps to genetic variation, which has not been tested. Our study examines the impact of sub-phenotyping of a well-characterized ASD sample on genetic homogeneity and the ability to discover common genetic variants conferring liability to ASD. METHODS: Genome-wide genotypic data of 2576 families from the Simons Simplex Collection (SSC) were analyzed in the overall sample and phenotypic subgroups defined on the basis of diagnosis, IQ, and symptom profiles. We conducted a family-based association study as well as estimating heritability and evaluating allele scores for each phenotypic subgroup. RESULTS: Association analyses revealed no genome-wide significant association signal. Sub-phenotyping did not increase power substantially. Moreover, allele scores built from the most associated SNPs, based on the odds ratio in the full sample, predicted case status in subsets of the sample equally well and heritability estimates were very similar for all subgroups. CONCLUSIONS: In genome-wide association analysis of the SSC sample, reducing phenotypic heterogeneity had at most a modest impact on genetic homogeneity. Our results are based on a relatively small sample, one with greater homogeneity than the entire population; if they apply more broadly, they imply that analysis of sub-phenotypes is not a productive path forward for discovering genetic risk variants in ASD