NDLI: Robust Hierarchical System for Classification of Complex Human Mobility Characteristics in the Presence of Neurological Disorders

Content Provider	IEEE Xplore Digital Library
Author	Xu, Xiaoyu Batalin, Maxim A. Kaiser, William J. Dobkin, Bruce
Copyright Year	2011
Abstract	Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes. Continued rapid progress in cost reduction, energy efficiency, and new data transport architectures for body worn sensors enables remote monitoring of patient activity with critical focus and impact on successful outcomes in healthcare. Monitoring systems, composed of both sensor and signal processing systems, seek to provide the capability to classify subject motion state and characteristics. Monitoring system progress has currently enabled classification of normal gait or abnormal gait within constrained laboratory operating conditions. However, monitoring of subjects in the community (specifically in residential environments remote from the laboratory or urban outdoor environments) has introduced fundamental challenges that have not been solved in the past. These challenges become profoundly more severe when monitoring subjects suffering from impaired gait due to conditions including stroke and other neurological disorders. One of the most important measures required in neurological rehabilitation is the accurate classification of walking speed in the community. Changes in absolute speed directly indicate rehabilitation progress and also directly determine whether an individual may remain safe and functional. Healthcare delivery practice requires that characterization of walking parameters and speed must be provided with reliance only on limited system training data acquisition and time. This paper reports on a primary advance in this capability through development of a novel architecture delivering required high rate, continuous sampling at low cost, with compact sensors and with rapidly deployable systems. Most importantly, this paper introduces a new hierarchical classification system applicable to subjects afflicted with hemi paresis due to stroke and disorders including multiple sclerosis. This system provides accurate classification and characterization of walking mobility invariant to other activities performed at the same time and in the presence of interfering signals induced by gait changes.
Starting Page	65
Ending Page	70
File Size	2005421
Page Count	6
File Format	PDF
ISBN	9781457704697
DOI	10.1109/BSN.2011.23
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion Training Humans Training data Medical services Sensors Niobium
Content Type	Text
Resource Type	Article

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