Stepping Beyond Assessment: Fall Risk Prediction Models Among Older Adults from Cumulative Change in Gait Parameter Estimates.

Falls significantly affect the health of older adults. Injuries sustained through falls have long-term consequences on the ability to live independently and age in place, and are the leading cause of injury death in the United States for seniors. Early fall risk detection provides an important opportunity for prospective intervention by healthcare providers and home caregivers. In-home depth sensor technologies have been developed for real-time fall detection and gait parameter estimation including walking speed, the sixth vital sign, which has been shown to correlate with the risk of falling. This study evaluates the use of supervised classification for estimating fall risk from cumulative changes in gait parameter estimates as captured by 3D depth sensors placed within the homes of older adult participants. Using recall as the primary metric for model success rate due to the severity of fall injuries sustained by false negatives, we demonstrate an enhancement of assessing fall risk with univariate logistic regression using multivariate logistic regression, support vector, and hierarchical tree-based modeling techniques by an improvement of 18.80%, 31.78%, and 33.94%, respectively, in the 14 days preceding a fall event. Random forest and XGBoost models resulted in recall and precision scores of 0.805 compared to the best univariate regression model of Y-Entropy with a recall of 0.639 and precision of 0.527 for the 14-day window leading to a predicted fall event.

HTIDB: Hierarchical Time-Indexed Database for Efficient Storage and Access to Irregular Time-series Health Sensor Data.

With the enormous amount of data collected by unobtrusive sensors, the potential of utilizing these data and applying various multi-modal advanced analytics on them is numerous and promising. However, taking advantage of the ever-growing data requires high-performance data-handling systems to enable high data scalability and easy data accessibility. This paper demonstrates robust design, developments, and techniques of a hierarchical time-indexed database for decision support systems leveraging irregular and sporadic time series data from sensor systems, e.g., wearables or environmental. We propose a technique that leverages the flexibility of general purpose, high-scalability database systems, while integrating data analytics focused column stores that leverage hierarchical time indexing, compression, and dense raw numeric data storage. We have evaluated the performance characteristics and tradeoffs of each to understand the data access latencies and storage requirements, which are key elements for capacity planning for scalable systems.

Interhemispheric transfer of post-amputation cortical plasticity within the human somatosensory cortex.

Animal models reveal that deafferenting forelimb injuries precipitate reorganization in both contralateral and ipsilateral somatosensory cortices. The functional significance and duration of these effects are unknown, and it is unclear whether they also occur in injured humans. We delivered cutaneous stimulation during functional magnetic resonance imaging (fMRI) to map the sensory cortical representation of the intact hand and lower face in a group of chronic, unilateral, upper extremity amputees (N = 19) and healthy matched controls (N = 29). Amputees exhibited greater activity than controls within the deafferented former sensory hand territory (S1f) during stimulation of the intact hand, but not of the lower face. Despite this cortical reorganization, amputees did not differ from controls in tactile acuity on their intact hands. S1f responses during hand stimulation were unrelated to tactile acuity, pain, prosthesis usage, or time since amputation. These effects appeared specific to the deafferented somatosensory modality, as fMRI visual mapping paradigm failed to detect any differences between groups. We conclude that S1f becomes responsive to cutaneous stimulation of the intact hand of amputees, and that this modality-specific reorganizational change persists for many years, if not indefinitely. The functional relevance of these changes, if any, remains unknown.

Grasping with the Press of a Button: Grasp-selective Responses in the Human Anterior Intraparietal Sulcus Depend on Nonarbitrary Causal Relationships between Hand Movements and End-effector Actions.

Evidence implicates ventral parieto-premotor cortices in representing the goal of grasping independent of the movements or effectors involved [Umilta, M. A., Escola, L., Intskirveli, I., Grammont, F., Rochat, M., Caruana, F., et al. When pliers become fingers in the monkey motor system. Proceedings of the National Academy of Sciences, U.S.A., 105, 2209-2213, 2008; Tunik, E., Frey, S. H., & Grafton, S. T. Virtual lesions of the anterior intraparietal area disrupt goal-dependent on-line adjustments of grasp. Nature Neuroscience, 8, 505-511, 2005]. Modern technologies that enable arbitrary causal relationships between hand movements and tool actions provide a strong test of this hypothesis. We capitalized on this unique opportunity by recording activity with fMRI during tasks in which healthy adults performed goal-directed reach and grasp actions manually or by depressing buttons to initiate these same behaviors in a remotely located robotic arm (arbitrary causal relationship). As shown previously [Binkofski, F., Dohle, C., Posse, S., Stephan, K. M., Hefter, H., Seitz, R. J., et al. Human anterior intraparietal area subserves prehension: A combined lesion and functional MRI activation study. Neurology, 50, 1253-1259, 1998], we detected greater activity in the vicinity of the anterior intraparietal sulcus (aIPS) during manual grasp versus reach. In contrast to prior studies involving tools controlled by nonarbitrarily related hand movements [Gallivan, J. P., McLean, D. A., Valyear, K. F., & Culham, J. C. Decoding the neural mechanisms of human tool use. Elife, 2, e00425, 2013; Jacobs, S., Danielmeier, C., & Frey, S. H. Human anterior intraparietal and ventral premotor cortices support representations of grasping with the hand or a novel tool. Journal of Cognitive Neuroscience, 22, 2594-2608, 2010], however, responses within the aIPS and premotor cortex exhibited no evidence of selectivity for grasp when participants employed the robot. Instead, these regions showed comparable increases in activity during both the reach and grasp conditions. Despite equivalent sensorimotor demands, the right cerebellar hemisphere displayed greater activity when participants initiated the robot's actions versus when they pressed a button known to be nonfunctional and watched the very same actions undertaken autonomously. This supports the hypothesis that the cerebellum predicts the forthcoming sensory consequences of volitional actions [Blakemore, S. J., Frith, C. D., & Wolpert, D. M. The cerebellum is involved in predicting the sensory consequences of action. NeuroReport, 12, 1879-1884, 2001]. We conclude that grasp-selective responses in the human aIPS and premotor cortex depend on the existence of nonarbitrary causal relationships between hand movements and end-effector actions.