Sensorimotor conflict tests in an immersive virtual environment reveal subclinical impairments in mild traumatic brain injury.

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor perturbations, delivered in a highly instrumented, immersive virtual environment, would challenge sensory subsystems recruited for balance through conflicting multi-sensory evidence, and therefore reveal that not all subsystems are performing optimally.

Monitoring mobility assistive device use in post-stroke patients.

Mobility assistive devices (MAD) such as canes can improve mobility and allow independence in the performance of mobility-related tasks. The use of MAD is often prescribed for stroke survivors. Despite their acknowledged qualities, MAD in real life conditions are typically underutilized, misused and abandoned.

Identification of tasks performed by stroke patients using a mobility assistive device.

Many stroke patients are prescribed canes or other mobility assistive devices. Once taken home, these mobility assistive devices are often abandoned or misused. A means for assessing the use of the cane in the home and community settings is required to assist clinicians in the prescription of these devices.

Data mining techniques to detect motor fluctuations in Parkinson's disease.

The purpose of this paper is to present preliminary evidence that data mining and artificial intelligence systems may allow one to recognize the presence and severity of motor fluctuations in patients with Parkinson's disease (PD). We hypothesize that movement disorders in late-stage PD present with identifiable and predictable features that can be derived from accelerometer (ACC) and surface electromyographic (EMG) signals recorded during the execution of a standardized set of motor assessment tasks. Although this paper focuses on a specific clinical application requiring advanced analysis techniques, the approach can be generalized to numerous applications in which data mining and other techniques can be used to analyze large data sets derived from wearable sensors.

A clinical comparison of variable-damping and mechanically passive prosthetic knee devices.

Objective: Although variable-damping knee prostheses offer some improvements over mechanically passive prostheses to transfemoral amputees, there is insufficient evidence that such prostheses provide advantages at self-selected walking speeds. In this investigation, we address this question by comparing two variable-damping knees, the hydraulic-based Otto Bock C-leg and the magnetorheological-based Ossur Rheo, with the mechanically passive, hydraulic-based Mauch SNS.

Design: For each prosthesis, metabolic data were collected on eight unilateral amputees walking at self-selected speeds across an indoor track.

Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data.

Background: Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture.

Methods: A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation.