Human gait is an important indicator of health, with applications ranging from diagnosis, monitoring, and rehabilitation. In practice, the use of gait analysis has been limited. Existing gait analysis systems are either expensive, intrusive, or require well-controlled environments such as a clinic or a laboratory. We present an accurate gait analysis system that is economical and non-intrusive. Our system is based on the Kinect sensor and thus can extract comprehensive gait information from all parts of the body. Beyond standard stride information, we also measure arm kinematics, demonstrating the wide range of parameters that can be extracted. We further improve over existing work by using information from the entire body to more accurately measure stride intervals. Our system requires no markers or battery-powered sensors, and instead relies on a single, inexpensive commodity 3D sensor with a large preexisting install base. We suggest that the proposed technique can be used for continuous gait tracking at home.
Storage systems are designed and optimized relying on wisdom derived from analysis studies of file-system and block-level workloads. However, while SSDs are becoming a dominant building block in many storage systems, their design continues to build on knowledge derived from analysis targeted at hard disk optimization. Though still valuable, it does not cover important aspects relevant for SSD performance. In a sense, we are “searching under the streetlight,” possibly missing important opportunities for optimizing storage system design. We present the first I/O workload analysis designed with SSDs in mind. We characterize traces from four repositories and examine their “temperature” ranges, sensitivity to page size, and “logical locality.” We then take the first step towards correlating these characteristics with three standard performance metrics: write amplification, read amplification, and flash read costs. Our results show that SSD-specific characteristics strongly affect performance, often in surprising ways.
Least squares regression is widely used to understand and predict data behavior in many fields. As data evolves, regression models must be recomputed, and indeed much work has focused on quick, efficient and accurate computation of linear regression models. In distributed streaming settings, however, periodically recomputing the global model is wasteful: communicating new observations or model updates is required even when the model is, in practice, unchanged. This is prohibitive in many settings, such as in wireless sensor networks, or when the number of nodes is very large. The alternative, monitoring prediction accuracy, is not always sufficient: in some settings, for example, we are interested in the model's coefficients, rather than its predictions.We propose the first monitoring algorithm for multivariate regression models of distributed data streams that guarantees a bounded model error. It maintains an accurate estimate using a fraction of the communication by recomputing only when the precomputed model is sufficiently far from the (hypothetical) current global model. When the global model is stable, no communication is needed.Experiments on real and synthetic datasets show that our approach reduces communication by up to two orders of magnitude while providing an accurate estimate of the current global model in all nodes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.