7 research outputs found

    Detection of human movement by near field imaging : development of a novel method and applications

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    The proportion of senior citizens is increasing, which requires more resources in the care services. The effectiveness of these services is proposed to be increased by remote monitoring of senior citizens living at home or in nursing homes. The monitoring can be performed with various types of sensors, but the solution presented here incorporates most of the functionalities found in related work in one comprehensive system. The system that was developed uses electric field sensing to detect human presence and movement. Falls and the vital functions of a fallen person can also be extracted from the signals. The sensor arrangement consists of a matrix of thin planar electrodes under the floor surface, which makes the system completely undetectable and discreet. It is not disturbed by shading or darkness and does not require a lot of computing power. Computer vision does not enjoy these advantages. Furthermore, no devices need to be worn and no batteries need to be charged, as with systems based on transponders worn by the subject. If identification is required, the system developed in this work does not rule out the use of transponders. The impedances of the electrodes are measured using a tuned transformer and a phase-sensitive detector. A signal-to-noise ratio of 37 dB has been achieved with this structure. The mean positioning error when observing people who are walking is 21 cm. Multiple people can be discriminated with a 90% certainty if the distance between them is 78 cm. The sensitivity and specificity in fall detection have been found to be 91% and 91%, respectively. The cardiac activity and respiration are clearly visible when a person lies prone or supine on the floor. A capacitive radio frequency identification (RFID) tag in a shoe was developed for person identification. The system developed here has been installed in a large nursing home. The nurses have indicated their satisfaction in a comprehensive questionnaire, which was conducted by a representative of the nurses. Positive feedback has also been obtained from a senior person living alone and from his family members

    A shoe-integrated sensor system for wireless gait analysis and real-time therapeutic feedback

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    Thesis (Sc. D.)--Harvard-MIT Division of Health Sciences and Technology, 2004.Includes bibliographical references (p. 307-314).Clinical gait analysis currently involves either an expensive analysis in a motion laboratory, using highly accurate, if cumbersome, kinematic systems, or a qualitative analysis with a physician or physical therapist making visual observations. There is a need for a low cost device that falls in between these two methods, and can provide quantitative and repeatable results. In addition, continuous monitoring of gait would be useful for real-time physical rehabilitation. To free patients from the confines of a motion laboratory, this thesis has resulted in a wireless wearable system capable of measuring many parameters relevant to gait analysis. The extensive sensor suite includes three orthogonal accelerometers, and three orthogonal gyroscopes, four force sensors, two bi-directional bend sensors, two dynamic pressure sensors, as well as electric field height sensors. The "GaitShoe" was built to be worn on any shoes, without interfering with gait, and was designed to collect data unobtrusively, in any environment, and over long periods of time. Subject testing of the GaitShoe was carried out on ten healthy subjects with normal gait and five subjects with Parkinson's disease. The calibrated sensor outputs were analyzed, and compared to results obtained simultaneously from The Massachusetts General Hospital Biomotion Lab; the GaitShoe proved highly capable of detecting heel strike and toe off, as well as estimating orientation and position of the subject. A wide variety of features were developed from the calibrated sensor outputs, for use with standard pattern recognition techniques to classify the gait of the subject. The results of the classification demonstrated the ability of the GaitShoe to identify the subjects with(cont.) Parkinson's disease, as well as individual subjects. Real-time feedback methods were developed to investigate the feasibility of using the continuous monitoring of gait for physical therapy and rehabilitation.by Stacy J. Morris.Sc.D

    Cognitive Foundations for Visual Analytics

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