Freezing of Gait Detection Considering Leaky Wave Cable

A novel study on monitoring and analysis of the debilitating condition of patients suffering from the neurological disorder is presented. Parkinson’s disease is characterized by limited motor ability of a patient. Freezing of gait is a major nonmotor condition among aging patients and its evaluation can reduce the chances of any secondary disorders. In this paper, amplitude and phase information of the radio signals observed for a fixed period of time are used to differentiate the motor and nonmotor symptoms. The amplitude information is classified using a support vector machine, while the linear transformation is applied to obtain sanitized phase information for detection. The proposed method is very handy with the minimum deployment overhead. The analysis shows that this method also offers a high-accuracy level of around 99% based on the observation of a number of patients. These features make it an attractive solution for real-time patient monitoring systems.

[1]  M. Nakamura,et al.  Development of a 300 m 2.4 GHz frequency band leaky coaxial cable for wireless network access , 2008, 2008 IEEE Radio and Wireless Symposium.

[2]  J. Jankovic.  Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[3]  Ning An,et al.  SCPL: indoor device-free multi-subject counting and localization using radio signal strength , 2013, IPSN.

[4]  Hongbo Shi,et al.  Temporal-Spatial Global Locality Projections for Multimode Process Monitoring , 2018, IEEE Access.

[5]  Takao Hashimoto,et al.  Speculation on the responsible sites and pathophysiology of freezing of gait , 2006 .

[6]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[7]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[8]  Sinziana Mazilu,et al.  Prediction of Freezing of Gait in Parkinson's From Physiological Wearables: An Exploratory Study , 2015, IEEE Journal of Biomedical and Health Informatics.

[9]  Jie Tian,et al.  Wandering Pattern Sensing at S-Band , 2018, IEEE Journal of Biomedical and Health Informatics.

[10]  Takashi Hirai,et al.  Two-frequency surveillance technique for intrusion-detection sensor with Leaky Coaxial Cables , 2014, 2014 IEEE Sensors Applications Symposium (SAS).

[11]  David Seidel,et al.  A systematic review of the worldwide prevalence and incidence of Parkinson's disease. , 2011, Journal of the Medical Association of Thailand = Chotmaihet thangphaet.

[12]  Shwetak N. Patel,et al.  Whole-home gesture recognition using wireless signals , 2013, MobiCom.

[13]  Wei Wang,et al.  Understanding and Modeling of WiFi Signal Based Human Activity Recognition , 2015, MobiCom.

[14]  Q. Abbasi,et al.  A Low Profile Antenna for Millimeter-Wave Body-Centric Applications , 2017, IEEE Transactions on Antennas and Propagation.

[15]  Sinziana Mazilu,et al.  Online detection of freezing of gait with smartphones and machine learning techniques , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[16]  Dimitrios I. Fotiadis,et al.  Automatic detection of freezing of gait events in patients with Parkinson's disease , 2013, Comput. Methods Programs Biomed..

[17]  David Wetherall,et al.  Predictable 802.11 packet delivery from wireless channel measurements , 2010, SIGCOMM '10.

[18]  W. Ondo,et al.  Ambulatory monitoring of freezing of gait in Parkinson's disease , 2008, Journal of Neuroscience Methods.

[19]  Shiwen Mao,et al.  CSI Phase Fingerprinting for Indoor Localization With a Deep Learning Approach , 2016, IEEE Internet of Things Journal.

[20]  Fay B. Horak,et al.  Quantifying freezing of gait in Parkinson's disease during the instrumented timed up and go test , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Saurabh Maheshwari,et al.  Walking parameters estimation through channel state information preliminary results , 2015, 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS).

[22]  Jeffrey M. Hausdorff,et al.  Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom , 2010, IEEE Transactions on Information Technology in Biomedicine.