Comparison of two techniques for monitoring of human movements

Abstract This paper is devoted to a comparison of two sensor technologies that may be employed in care services for elderly or disabled persons. The performance of monitoring systems, based on impulse-radar sensors and depth sensors, has been systematically compared in a series of experiments which involved the estimation of several quantities, carrying information important for medical and healthcare services, on the basis of data from those sensors. The results of the experiments have shown that, although in most cases the estimates based on data acquired by means of radar sensors are less accurate than those based on data acquired by means of depth sensors, both types of sensors provide information useful for the medical and healthcare users of the monitoring systems.

[1]  Jeffrey Kaye,et al.  The trajectory of gait speed preceding mild cognitive impairment. , 2010, Archives of neurology.

[2]  Muhammad Salman Khan,et al.  An unsupervised acoustic fall detection system using source separation for sound interference suppression , 2015, Signal Process..

[3]  Daqing Zhang,et al.  Anti-fall: A Non-intrusive and Real-Time Fall Detector Leveraging CSI from Commodity WiFi Devices , 2015, ICOST.

[4]  Bart Vanrumste,et al.  Developing a system that can automatically detect health changes using transfer times of older adults , 2016, BMC Medical Research Methodology.

[5]  Kim Delbaere,et al.  Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers , 2015, IEEE Transactions on Biomedical Engineering.

[6]  Jun Jason Zhang,et al.  Ultra-wideband radar-based accurate motion measuring: human body landmark detection and tracking with biomechanical constraints , 2015 .

[7]  S. Fritz,et al.  Determining Risk of Falls in Community Dwelling Older Adults: A Systematic Review and Meta-analysis Using Posttest Probability , 2016, Journal of geriatric physical therapy.

[8]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[9]  Patrick Royston,et al.  Augmenting the logrank test in the design of clinical trials in which non-proportional hazards of the treatment effect may be anticipated , 2016, BMC Medical Research Methodology.

[10]  Roman Z. Morawski,et al.  Selected algorithms for measurement data processing in impulse-radar-based system for monitoring of human movements , 2016 .

[11]  Bart Vanrumste,et al.  Fall prevention and detection , 2016 .

[12]  Alessio Vecchio,et al.  Improving the performance of fall detection systems through walk recognition , 2014, J. Ambient Intell. Humaniz. Comput..

[13]  Marjorie Skubic,et al.  Average in-home gait speed: investigation of a new metric for mobility and fall risk assessment of elders. , 2015, Gait & posture.

[14]  H. Vincent Poor,et al.  Position Estimation via Ultra-Wide-Band Signals , 2008, Proceedings of the IEEE.

[15]  Roman Z. Morawski,et al.  Monitoring of human movements by means of impulse-radar sensors , 2015 .

[16]  Pietro Siciliano,et al.  Heterogeneous sensor platform for circadian rhythm analysis , 2015, 2015 6th International Workshop on Advances in Sensors and Interfaces (IWASI).

[17]  Yimin Zhang,et al.  Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring , 2016, IEEE Signal Processing Magazine.

[18]  Roman Z. Morawski,et al.  Fusion of measurement data from impulse-radar sensors and depth sensors when applied for patients monitoring , 2017, 2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[19]  Roman Z. Morawski,et al.  REGULARISED DIFFERENTIATION OF MEASUREMENT DATA , 2015 .

[20]  Rossana Castaldo,et al.  Preliminary results from a proof of concept study for fall detection via ECG morphology , 2016 .

[21]  Nasser Kehtarnavaz,et al.  A survey of depth and inertial sensor fusion for human action recognition , 2015, Multimedia Tools and Applications.

[22]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[23]  Annupan Rodtook,et al.  Fall detection using directional bounding box , 2015, 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[24]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[25]  Mashiro Tanaka.  Application of depth sensor for breathing rate counting , 2015, 2015 10th Asian Control Conference (ASCC).

[26]  Roman Z. Morawski,et al.  Comparative study of three algorithms for estimation of echo parameters in UWB radar module for monitoring of human movements , 2016 .

[27]  Eduardo Casilari-Pérez,et al.  Comparison and Characterization of Android-Based Fall Detection Systems , 2014, Sensors.

[28]  Marjorie Skubic,et al.  Doppler Radar Fall Activity Detection Using the Wavelet Transform , 2015, IEEE Transactions on Biomedical Engineering.

[29]  Nasser Kehtarnavaz,et al.  Improving Human Action Recognition Using Fusion of Depth Camera and Inertial Sensors , 2015, IEEE Transactions on Human-Machine Systems.

[30]  Kristin Taraldsen,et al.  Identification of gait domains and key gait variables following hip fracture , 2015, BMC Geriatrics.

[31]  S. Studenski,et al.  Gait speed and survival in older adults. , 2011, JAMA.

[32]  Yoong Choon Chang,et al.  A simple vision-based fall detection technique for indoor video surveillance , 2015, Signal Image Video Process..

[33]  Michelle M. Lusardi,et al.  Is Walking Speed a Vital Sign? Absolutely! , 2012 .

[34]  Barbro Krevers,et al.  Symptom burden in community-dwelling older people with multimorbidity: a cross-sectional study , 2015, BMC Geriatrics.

[35]  H. Macher,et al.  FIRST EXPERIENCES WITH KINECT V2 SENSOR FOR CLOSE RANGE 3D MODELLING , 2015 .

[36]  Bogdan Kwolek,et al.  Embedded system for fall detection using body-worn accelerometer and depth sensor , 2015, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[37]  Uwe D. Hanebeck,et al.  Intelligent sensor-scheduling for multi-kinect-tracking , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[38]  Ioannis Paraskevopoulos,et al.  Fall prevention intervention technologies: A conceptual framework and survey of the state of the art , 2016, J. Biomed. Informatics.

[39]  Rui Liu,et al.  Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera , 2014, Signal Image Video Process..

[40]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[41]  Stephen M Thielke,et al.  Effects of Disease Burden and Functional Adaptation on Morbidity and Mortality on Older Adults , 2016, Journal of the American Geriatrics Society.

[42]  Jaakko Astola,et al.  Soft morphological filters , 1991, Optics & Photonics.

[43]  Yevhen Yashchyshyn,et al.  Study of detection capability of Novelda impulse transceiver with external RF circuit , 2015, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[44]  Alessio Vecchio,et al.  Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey , 2010 .

[45]  Young-Ho Suh,et al.  Continuous location tracking of people by multiple depth cameras , 2015, 2015 International Conference on Information and Communication Technology Convergence (ICTC).

[46]  Laurent Zelek,et al.  Measurement of gait speed in older adults to identify complications associated with frailty: A systematic review. , 2015, Journal of geriatric oncology.

[47]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[48]  Juan R. Terven,et al.  Kin2. A Kinect 2 toolbox for MATLAB , 2016, Sci. Comput. Program..

[49]  O. Celik,et al.  Systematic review of Kinect applications in elderly care and stroke rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[50]  R. Herrmann,et al.  M-sequence-based ultra-wideband sensor network for vitality monitoring of elders at home , 2015 .

[51]  Roman Z. Morawski,et al.  Healthcare-Oriented Characterisation of Human Movements by Means of Impulse-Radar Sensors and by Means of Accelerometric Sensors , 2017, HEALTHINF.

[52]  Roman Z. Morawski,et al.  Acquisition and preprocessing of data from infrared depth sensors to be applied for patients monitoring , 2015, 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[53]  Jorunn L Helbostad,et al.  Comparison of programs for determining temporal-spatial gait variables from instrumented walkway data: PKmas versus GAITRite , 2014, BMC Research Notes.

[54]  Marco Mercuri,et al.  Embedded DSP-Based Telehealth Radar System for Remote In-Door Fall Detection , 2015, IEEE Journal of Biomedical and Health Informatics.