No abstract

Background: Ventriculoperitoneal shunting improves gait in patients with normal pressure hydrocephalus. Postural instability is a major concern, but mostly ignored in the evaluation and treatment of these patients. This study quantified postural instability using kinematics via a prospective cohort design. Methods: Seventeen patients with suspected normal pressure hydrocephalus and twenty age-matched, healthy controls underwent quantitative pull test and gait examinations while wearing inertial measurement units at baseline. Patients with suspected normal pressure hydrocephalus who were shunted (n=13) and not shunted (n=4) underwent further testing after a lumbar drain trial and at follow-up visits 6 and 12 months post-operatively. Results: While most gait improvement in patients who were shunted was seen immediately after the lumbar drain trial, measures of their postural response continued to improve after the lumbar drain trial through one year of follow-up. Patients who were not shunted showed no statistically significant changes in gait and postural instability measures. Conclusions: After shunting, postural instability improves continuously over one year. In contrast, a large improvement in gait is seen immediately with minimal change over the subsequent year. This difference in timing may implicate two distinct neurophysiological mechanisms of recovery and provides novel evidence that postural instability improves in response to long-term CSF diversion.

The use of wearable sensors in movement disorder patients such as Parkinson’s disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over 1 year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.

No abstract

The use of wearable sensors in movement disorder patients such as Parkinson's disease (PD) and normal pressure hydrocephalus (NPH) is becoming more widespread, but most studies are limited to characterizing general aspects of mobility using smartphones. There is a need to accurately identify specific activities at home in order to properly evaluate gait and balance at home, where most falls occur. We developed an activity recognition algorithm to classify multiple daily living activities including high fall risk activities such as sit to stand transfers, turns and near-falls using data from 5 inertial sensors placed on the chest, upper-legs and lower-legs of the subjects. The algorithm is then verified with ground truth by collecting video footage of our patients wearing the sensors at home. Our activity recognition algorithm showed >95% sensitivity in detection of activities. Extracted features from our home monitoring system showed significantly better correlation (~69%) with prospectively measured fall frequency of our subjects compared to the standard clinical tests (~30%) or other quantitative gait metrics used in past studies when attempting to predict future falls over one year of prospective follow-up. Although detecting near-falls at home is difficult, our proposed model suggests that near-fall frequency is the most predictive criterion in fall detection through correlation analysis and fitting regression models.